## Data Science Training In chennai

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Learn Data Science Training in Chennai from basic to advanced with Placement Point Solutions. We are rated as the No.1 Data science training institute in Chennai with good placement record in the year 2019-2020. We offer high quality Data Science course in Chennai with highly experienced and certified professionals. Data science course covers, Python with Machine Learning, Deep Learning, Natural Learning, Artificial Intelligence, AWS Cloud and Tableau (Data Visualization). We understand our students mind and we take pride in training our students step-by-step with real-time project based scenarios and updated project skills. Most of our candidates are placed and working in high reputed MNC Organization like Amazon, e bay, Honeywell, Verizon, Cognizant, Infosys, Wipro, Flipkart etc..

## Why we are the Best Data Science Training institute in Chennai?

We have always looked for people who have relevant skills and experience. According to the Statistical Reports of Current IT industry Data Science Jobs will create approximately 10 Million opening by 2025 which is 5 years from now.

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All our ethical hacking subjects are well designed to ensure learners will have what is necessary to study a system. One will get all the essential tools to be capable of penetrating a program, just like a black hacker. All our courses are practical oriented to ensure a learner has what it takes to fit well in the competitive industry. Placement Point Solutions always keeps its syllabus updated to meet all the changes occurring in the dynamic hacking field. The changes also aim at giving learners the best skills and meet the quality education standards in Chennai.

## FAQ (FREQUENTLY ASKED QUESTIONS)

## Data Science Training Key Features

- 60+ Hours Course Duration
- Industry Expert Faculties
- Completed 500+ Batches
- Placed More than 1000+ Students

- 100% Job Oriented Training
- Free Demo Class Available
- Certification Guidance
- Affordable Pricing

# | Data Science Training in Chennai

As the world entered the technology of big data, the need for its storage additionally grew. It used to be the principal project and difficulty for the corporation industries till 2010. The fundamental focus was once on building framework and solutions to save data. Now when Hadoop and different frameworks have effectively solved the trouble of storage, the focus has shifted to the processing of this data. Data Science is the secret sauce here. All the ideas which you see in Hollywood sci-fi films can simply turn into fact by Data Science. Data Science is the future of Artificial Intelligence. Therefore, it is very essential to recognize what is Data Science and how can it add price to your business.

**Data Science with Python **

**Course Details:**

**1. Introduction To Data Science With Python**

- What are analytics & Data Science?
- Common Terms in Analytics
- Analytics vs. Data warehousing, OLAP, MIS Reporting
- Relevance in industry and need of the hour
- Types of problems and business objectives in various industries
- How leading companies are harnessing the power of analytics?
- Critical success drivers
- Overview of analytics tools & their popularity
- Analytics Methodology & problem solving framework
- List of steps in Analytics projects
- Identify the most appropriate solution design for the given problem statement
- Project plan for Analytics project & key milestones based on effort estimates
- Build Resource plan for analytics project
- Why Python for data science?

**2. Python: Essentials (Core)**

- Overview of Python- Starting with Python
- Introduction to installation of Python
- Introduction to Python Editors & IDE’s(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
- Understand Jupyter notebook & Customize Settings
- Concept of Packages/Libraries – Important packages (NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
- Installing & loading Packages & Name Spaces
- Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
- List and Dictionary Comprehensions
- Variable & Value Labels – Date & Time Values
- Basic Operations – Mathematical – string – date
- Reading and writing data
- Simple plotting
- Control flow & conditional statements
- Debugging & Code profiling
- How to create class and modules and how to call them?

**3. Scientific Distributions Used In Python For Data Science**

- Numpy, scify, pandas, scikitlearn, statmodels, nltk etc

**4. Accessing/Importing And Exporting Data Using Python Modules**

- Importing Data from various sources (Csv, txt, excel, access etc)
- Database Input (Connecting to database)
- Viewing Data objects – subsetting, methods
- Exporting Data to various formats
- Important python modules: Pandas, beautifulsoup

**5. Data Manipulation – Cleansing – Munging Using Python Modules**

- Cleansing Data with Python
- Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
- Data manipulation tools (Operators, Functions, Packages, control structures, Loops, arrays etc)
- Python Built-in Functions (Text, numeric, date, utility functions)
- Python User Defined Functions
- Stripping out extraneous information
- Normalizing data
- Formatting data
- Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)

**6. Data Analysis – Visualization Using Python**

- Introduction exploratory data analysis
- Descriptive statistics, Frequency Tables and summarization
- Univariate Analysis (Distribution of data & Graphical Analysis)
- Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
- Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
- Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc)

**7. Introduction To Statistics**

- Basic Statistics – Measures of Central Tendencies and Variance
- Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
- Inferential Statistics -Sampling – Concept of Hypothesis Testing
- Statistical Methods – Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square
- Important modules for statistical methods: Numpy, Scipy, Pandas

**8. Introduction To Predictive Modeling**

- Concept of model in analytics and how it is used?
- Common terminology used in analytics & modeling process
- Popular modeling algorithms
- Types of Business problems – Mapping of Techniques
- Different Phases of Predictive Modeling

**9. Data Exploration For Modeling**

- Need for structured exploratory data
- EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
- Identify missing data
- Identify outliers data
- Visualize the data trends and patterns

**10. Data Preparation**

- Need of Data preparation
- Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction
- Variable Reduction Techniques – Factor & PCA Analysis

**11. Segmentation: Solving Segmentation Problems**

- Introduction to Segmentation
- Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
- Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
- Behavioral Segmentation Techniques (K-Means Cluster Analysis)
- Cluster evaluation and profiling – Identify cluster characteristics
- Interpretation of results – Implementation on new data

**12. Linear Regression: Solving Regression Problems**

- Introduction – Applications
- Assumptions of Linear Regression
- Building Linear Regression Model
- Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
- Assess the overall effectiveness of the model
- Validation of Models (Re running Vs. Scoring)
- Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
- Interpretation of Results – Business Validation – Implementation on new data

**13. Logistic Regression: Solving Classification Problems**

- Introduction – Applications
- Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
- Building Logistic Regression Model (Binary Logistic Model)
- Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
- Validation of Logistic Regression Models (Re running Vs. Scoring)
- Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
- Interpretation of Results – Business Validation – Implementation on new data

**14. Time Series Forecasting: Solving Forecasting Problems**

- Introduction – Applications
- Time Series Components (Trend, Seasonality, Cyclicity and Level) and Decomposition
- Classification of Techniques (Pattern based – Pattern less)
- Basic Techniques – Averages, Smoothening, etc
- Advanced Techniques – AR Models, ARIMA, etc
- Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc

**15**.**Machine Learning -Predictive Modeling – Basics**

- Introduction to Machine Learning & Predictive Modeling
- Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting
- Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
- Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
- Overfitting (Bias-Variance Trade off) & Performance Metrics
- Feature engineering & dimension reduction
- Concept of optimization & cost function
- Overview of gradient descent algorithm

- Overview of Cross validation(Bootstrapping, K-Fold validation etc)
- Model performance metrics (R-square, Adjusted R-squre, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics

**16. Unsupervised Learning: Segmentation**

- What is segmentation & Role of ML in Segmentation?
- Concept of Distance and related math background
- K-Means Clustering
- Expectation Maximization
- Hierarchical Clustering
- Spectral Clustering (DBSCAN)
- Principle component Analysis (PCA)

**17. Supervised Learning:** **Decision Trees**

- Decision Trees – Introduction – Applications
- Types of Decision Tree Algorithms
- Construction of Decision Trees through Simplified Examples; Choosing the “Best” attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
- Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
- Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules
- Decision Trees – Validation
- Overfitting – Best Practices to avoid

**18**.**supervised ****Learning: Ensemble Learning**

- Concept of Ensembling
- Manual Ensembling Vs. Automated
- Ensembling
- Methods of Ensembling (Stacking, Mixture of Experts)
- Bagging (Logic, Practical Applications)
- Random forest (Logic, Practical Applications)
- Boosting (Logic, Practical Applications)
- Ada Boost
- Gradient Boosting Machines (GBM)
- XGBoost

**19. Supervised Learning: Artificial Neural Networks (ANN)**

- Motivation for Neural Networks and Its Applications
- Perceptron and Single Layer Neural Network, and Hand Calculations
- Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques
- Neural Networks for Regression
- Neural Networks for Classification
- Interpretation of Outputs and Fine tune the models with hyper parameters
- Validating ANN models

**20. Supervised Learning: Support Vector Machines**

- Motivation for Support Vector Machine & Applications
- Support Vector Regression
- Support vector classifier (Linear & Non-Linear)
- Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
- Interpretation of Outputs and Fine tune the models with hyper parameters
- Validating SVM models

**21. Supervised Learning: KNN**

- What is KNN & Applications?
- KNN for missing treatment
- KNN For solving regression problems
- KNN for solving classification problems
- Validating KNN model
- Model fine tuning with hyper parameters

**22. Supervised Learning: Naïve Bayes**

- Concept of Conditional Probability
- Bayes Theorem and Its Applications
- Naïve Bayes for classification
- Applications of Naïve Bayes in Classification

**23. Text Mining & Analytics**

- Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)
- Finding patterns in text: text mining, text as a graph
- Natural Language processing (NLP)
- Text Analytics – Sentiment Analysis using Python
- Text Analytics – Word cloud analysis using Python
- Text Analytics – Segmentation using K-Means/Hierarchical Clustering
- Text Analytics – Classification (Spam/Not spam)
- Applications of Social Media Analytics
- Metrics (Measures Actions) in social media analytics
- Examples & Actionable Insights using Social Media Analytics
- Important python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk etc)
- Fine tuning the models using Hyper parameters, grid search, piping etc.

**24. Project – Consolidate Learnings :**

Applying different algorithms to solve the business problems and bench mark the results

**Data Science with R**

**Course Outline**

**1. Introduction To Data Science With R**

- What is analytics & Data Science?
- Common Terms in Analytics
- Analytics vs. Data warehousing, OLAP, MIS Reporting
- Relevance in industry and need of the hour
- Types of problems and business objectives in various industries
- How leading companies are harnessing the power of analytics?
- Critical success drivers
- Overview of analytics tools & their popularity
- Analytics Methodology & problem solving framework
- List of steps in Analytics projects
- Identify the most appropriate solution design for the given problem statement
- Project plan for Analytics project & key milestones based on effort estimates
- Build Resource plan for analytics project
- Why R for data science?

**2. Introduction – Data Importing/Exporting**

- Introduction R/R-Studio – GUI
- Concept of Packages – Useful Packages (Base & Other packages)
- Data Structure & Data Types (Vectors, Matrices, factors, Data frames, and Lists)
- Importing Data from various sources (txt, dlm, excel, sas7bdata, db, etc.)
- Database Input (Connecting to database)
- Exporting Data to various formats)
- Viewing Data (Viewing partial data and full data)
- Variable & Value Labels – Date Values

**3. Data Manipulation**

- Data Manipulation steps
- Creating New Variables (calculations & Binning)
- Dummy variable creation
- Applying transformations
- Handling duplicates
- Handling missings
- Sorting and Filtering
- Subsetting (Rows/Columns)
- Appending (Row appending/column appending)
- Merging/Joining (Left, right, inner, full, outer etc)
- Data type conversions
- Renaming
- Formatting
- Reshaping data
- Sampling
- Data manipulation tools
- Operators
- Functions
- Packages
- Control Structures (if, if else)
- Loops (Conditional, iterative loops, apply functions)
- Arrays
- R Built-in Functions (Text, Numeric, Date, utility)
- Numerical Functions
- Text Functions
- Date Functions
- Utilities Functions
- R User Defined Functions
- R Packages for data manipulation (base, dplyr, plyr, data.table, reshape, car, sqldf, etc)

**4. Data Analysis – Visualization**

- Introduction exploratory data analysis
- Descriptive statistics, Frequency Tables and summarization
- Univariate Analysis (Distribution of data & Graphical Analysis)
- Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
- Creating Graphs- Bar/pie/line chart/histogram/boxplot/scatter/density etc)
- R Packages for Exploratory Data Analysis (dplyr, plyr, gmodes, car, vcd, Hmisc, psych, doby etc)
- R Packages for Graphical Analysis (base, ggplot, lattice,etc)

**5. Introduction To Statistics**

- Basic Statistics – Measures of Central Tendencies and Variance
- Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
- Inferential Statistics -Sampling – Concept of Hypothesis Testing
- Statistical Methods – Z/t-tests (One sample, independent, paired), Anova, Correlations and Chi-square

**6. Introduction To Predictive Modeling**

- Concept of model in analytics and how it is used?
- Common terminology used in analytics & modeling process
- Popular modeling algorithms
- Types of Business problems – Mapping of Techniques
- Different Phases of Predictive Modeling.

**8. Data Preparation**

- Need of Data preparation
- Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction
- Variable Reduction Techniques – Factor & PCA Analysis

**9.Segmentation: Solving Segmentation Problems**

- Introduction to Segmentation
- Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
- Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
- Behavioral Segmentation Techniques (K-Means Cluster Analysis)
- Cluster evaluation and profiling – Identify cluster characteristics
- Interpretation of results – Implementation on new data.

**10. Linear Regression: Solving Regression Problems**

- Introduction – Applications
- Assumptions of Linear Regression
- Building Linear Regression Model
- Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis, etc)
- Assess the overall effectiveness of the model
- Validation of Models (Re running Vs. Scoring)
- Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
- Interpretation of Results – Business Validation – Implementation on new data

**11. Logistic Regression: Solving Classification Problems**

- Introduction – Applications
- Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
- Building Logistic Regression Model (Binary Logistic Model)
- Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
- Validation of Logistic Regression Models (Re running Vs. Scoring)
- Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
- Interpretation of Results – Business Validation – Implementation on new data

**12. Time Series Forecasting: Solving Forecasting Problems**

- Introduction – Applications
- Time Series Components (Trend, Seasonality, Cyclicity and Level) and Decomposition
- Classification of Techniques (Pattern based – Pattern less)
- Basic Techniques – Averages, Smoothening, etc
- Advanced Techniques – AR Models, ARIMA, etc
- Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc

**13. Machine Learning -Predictive Modeling – Basics**

- Introduction to Machine Learning & Predictive Modeling
- Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting
- Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
- Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
- Overfitting (Bias-Variance Trade off) & Performance Metrics
- Feature engineering & dimension reduction
- Concept of optimization & cost function
- Overview of gradient descent algorithm
- Overview of Cross validation (Bootstrapping, K-Fold validation etc)
- Model performance metrics (R-square, Adjusted R-squre, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics)

**14. Unsupervised Learning: Segmentation**

- What is segmentation & Role of ML in Segmentation?
- Concept of Distance and related math background
- K-Means Clustering
- Expectation Maximization
- Hierarchical Clustering
- Spectral Clustering (DBSCAN)
- Principle component Analysis (PCA)

**15. Supervised Learning: Decision Trees**

- Decision Trees – Introduction – Applications
- Types of Decision Tree Algorithms
- Construction of Decision Trees through Simplified Examples; Choosing the “Best” attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
- Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
- Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules
- Decision Trees – Validation
- Overfitting – Best Practices to avoid

**16. Supervised Learning: Ensemble Learning**

- Concept of Ensembling
- Manual Ensembling Vs. Automated Ensembling
- Methods of Ensembling (Stacking, Mixture of Experts)
- Bagging (Logic, Practical Applications)
- Random forest (Logic, Practical Applications)
- Boosting (Logic, Practical Applications)
- Ada Boost
- Gradient Boosting Machines (GBM)
- XGBoost

**17. Supervised Learning: Artificial Neural Networks (ANN)**

- Motivation for Neural Networks and Its Applications
- Perceptron and Single Layer Neural Network, and Hand Calculations
- Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques
- Neural Networks for Regression
- Neural Networks for Classification
- Interpretation of Outputs and Fine tune the models with hyper parameters
- Validating ANN models

**18. Supervised Learning: Support Vector Machines**

Motivation for Support Vector Machine & Applications

- Support Vector Regression
- Support vector classifier (Linear & Non-Linear)
- Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
- Interpretation of Outputs and Fine tune the models with hyper parameters
- Validating SVM models

**19.** **Supervised Learning: KNN**

- What is KNN & Applications?
- KNN for missing treatment
- KNN For solving regression problems
- KNN for solving classification problems
- Validating KNN model
- Model fine tuning with hyper parameters

**20. Supervised Learning: Naïve Bayes**

- Concept of Conditional Probability
- Bayes Theorem and Its Applications
- Naïve Bayes for classification
- Applications of Naïve Bayes in Classifications

**21. Text Mining & Analytics**

- Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)
- Finding patterns in text: text mining, text as a graph
- Natural Language processing (NLP)
- Text Analytics – Sentiment Analysis using R
- Text Analytics – Word cloud analysis using R
- Text Analytics – Segmentation using K-Means/Hierarchical Clustering
- Text Analytics – Classification (Spam/Not spam)
- Applications of Social Media Analytics
- Metrics(Measures Actions) in social media analytics
- Examples & Actionable Insights using Social Media Analytics
- Important R packages for Machine Learning (caret, H2O, Randomforest, nnet, tm etc)

**22. Project – Consolidate Learnings:**

Applying different algorithms to solve the business problems and bench mark the results

**Data Science with SAS**

**Course Outline**

**1. Introduction To The Analytics World And ETL**

- Analytics World
- Introduction to Analytics
- Concept of ETL
- S-A-S in advanced analytics

- Global Certification: Induction and walk through
- Getting Started
- Software installation
- Introduction to GUI
- Different components of the language
- All programming windows
- Concept of Libraries and Creating Libraries
- Variable Attributes – (Name, Type, Length, Format, In format, Label)
- Importing Data and Entering data manually

- Understanding Datasets
- Descriptor Portion of a Dataset (Proc Contents)
- Data Portion of a Dataset
- Variable Names and Values
- Data Libraries

**2.Base S-A-S – Accessing The Data**

- Understanding Data Step Processing
- Data Step and Proc Step
- Data step execution
- Compilation and execution phase
- Input buffer and concept of PDV

- Importing Raw Data Files
- Column Input and List Input and Formatted methods
- Delimiters, Reading missing and non standard values
- Reading one to many and many to one records
- Reading Hierarchical files
- Creating raw data files and put statement
- Formats / Informat

- Importing and Exporting Data (Fixed Format / Delimited)
- Proc Import / Delimited text files
- Proc Export / Exporting Data
- Datalines / Cards;
- Atypical importing cases (mixing different style of inputs)
- Reading Multiple Records per Observation
- Reading “Mixed Record Types”
- Sub-setting from a Raw Data File
- Multiple Observations per Record
- Reading Hierarchical Files

- Importing Tips

**3. Data Understanding, Managing And Manipulation**

- Understanding and Exploration Data
- Introduction to basic Procedures – Proc Contents, Proc Print

- Understanding and Exploration Data

- Operators and Operands
- Conditional Statements (Where, If, If then Else, If then Do and select when)
- Difference between WHERE and IF statements and limitation of WHERE statements
- Labels, Commenting
- System Options (OBS, FSTOBS, NOOBS etc…)

- Data Manipulation
- Proc Sort – with options / De-Duping
- Accumulator variable and By-Group processing
- Explicit Output Statements
- Nesting Do loops
- Do While and Do Until Statement
- Array elements and Range

- Combining Datasets (Appending and Merging)
- Concatenation
- Interleaving
- Proc Append
- One To One Merging
- Match Merging
- IN = Controlling merge and Indicator

**4. Data Mining With Proc SQL**

- Introduction to Databases
- Introduction to Proc SQL
- Basics of General SQL language
- Creating table and Inserting Values
- Retrieve & Summarize data
- Group, Sort & Filter
- Using Joins (Full, Inner, Left, Right and Outer)
- Reporting and summary analysis
- Concept of Indexes and creating Indexes (simple and composite)
- Connecting S-A-S to external Databases
- Implicit and Explicit pass through methods

**5. Macros For Automation**

- Macro Parameters and Variables
- Different types of Macro Creation
- Defining and calling a macro
- Using call Symput and Symget
- Macros options (mprint symbolgen mlogic merror serror)

**6. Fundamental Of Statistics**

- Basic Statistics – Measures of Central Tendencies and Variance
- Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
- Inferential Statistics -Sampling – Concept of Hypothesis Testing
- Statistical Methods – Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square

**7. Introduction To Predictive Modelling**

- Introduction to Predictive Modeling
- Types of Business problems – Mapping of Techniques
- Different Phases of Predictive Modeling

**8. Data Preparation**

- Need of Data preparation
- Data Audit Report and Its importance
- Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction
- Variable Reduction Techniques – Factor & PCA Analysis

**9. Segmentation**

- Introduction to Segmentation
- Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
- Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
- Behavioural Segmentation Techniques (K-Means Cluster Analysis)
- Cluster evaluation and profiling
- Interpretation of results – Implementation on new data

**10. Linear Regression**

- Introduction – Applications
- Assumptions of Linear Regression
- Building Linear Regression Model
- Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
- Validation of Models (Re running Vs. Scoring)
- Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
- Interpretation of Results – Business Validation – Implementation on new data

**11. Logistic Regression**

- Introduction – Applications
- Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
- Building Logistic Regression Model
- Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, etc)
- Validation of Logistic Regression Models (Re running Vs. Scoring)
- Standard Business Outputs (Decile Analysis, ROC Curve,

Probability Cut-offs, Lift charts, Model equation, Drivers, etc) - Interpretation of Results – Business Validation -Implementation on new data

**12. Time Series Forecasting**

- Introduction – Applications
- Time Series Components (Trend, Seasonality, Cyclicity and Level) and Decomposition
- Classification of Techniques (Pattern based – Pattern less)
- Basic Techniques – Averages, Smoothening, etc
- Advanced Techniques – AR Models, ARIMA, etc
- Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc

**13. Introduction To Machine Learning**

- Statistical learning vs. Machine learning
- Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
- Concept of Overfitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
- Types of Cross validation(Train & Test, Bootstrapping, K-Fold validation etc)

**14. Regression & Classification Model Building**

- Recursive Partitioning(Decision Trees)
- Ensemble Models(Random Forest, Bagging & Boosting)

K-Nearest neighbours

**What is Data Science?**

Data science can be defined as a combination of mathematics, enterprise acumen, tools, algorithms and machine learning techniques, all of which assist us in discovering out the hidden insights or patterns from raw information which can be of major use in the formation of large business decisions.

In data science, one offers with each structured and unstructured data. The algorithms additionally contain predictive analytics in them. Thus, data science is all about the current and future. That is, discovering out the trends primarily based on historical data which can be beneficial for existing decisions and finding patterns which can be modeled and can be used for predictions to see what things might also look like in the future.

Data Science is an amalgamation of Statistics, Tools and Business knowledge. So, it becomes essential for a Data Scientist to have good know-how and perception of these.

*HISTORICAL BACKGROUND OF DATA SCIENCE*

*HISTORICAL BACKGROUND OF DATA SCIENCE*

History of data goes returned to 1500s when the Latin originated phrase “datum” was once used. But the work commenced on it at some point of the duration from 1940 to 1950. Claude Elwood Shannon, an American Mathematical Engineer published a paper “A Mathematical Theory of Communication” in 1948. Although he was no longer a facts scientist however his statistics idea formed the groundwork of machine learning algorithms.

John Wilder Tukey wrote a book Exploratory Data Analysis in 1977. The idea of Exploratory Data Analysis used to be promoted by means of him to explore the data. The exploratory facts analysis (EDA) method is used to analyze datasets typically with the visual methods.

Peter Naur wrote the Concise Survey of Computer Methods in 1974 where he utilized the expression “Data Science” first time. He used this time period many times in his book.

In 1999, Jacob Zahavi brought up the requirement for new gadgets to deal with the widespread measures of data handy to organizations, in “Mining Data for Nuggets of Knowledge”.

In 2001, William Cleveland published a paper, “Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics”. You can discover the paper here.

The International Council for Science: Committee on Data for Science and Technology started distributing the Data Science Journal in 2001, targeted on issues like the portrayal of facts systems, their production on the web, purposes and respectable issues.

In 2008, the title, “Data Scientist” turned into a ultra-modern expression and in the lengthy run a piece of the language. Jeff Hammerbacher and DJ Patil of Facebook and LinkedIn are given acknowledgment for starting its utilization as a modern day expression. Johan Oskarsson reintroduced the term NoSQL in 2009 when he sorted out a dialog on “open-source, non-relational databases”.

**Let’s Understand Why We Need Data Science**

- Traditionally, the information that we had used to be mostly structured and small in size, which could be analyzed by using the simple BI tools. Unlike data in the typical structures which was on the whole structured, today most of the data is unstructured or semi-structured. Let’s have a seem to be at the facts trends in the photograph given below which indicates that via 2020, more than 80 percent of the data will be unstructured.

**Flow of unstructured records**

This data is generated from one of a kind sources like economic logs, text files, multimedia forms, sensors, and instruments. Simple BI tools are no longer capable of processing this large volume and range of data. This is why we want greater complex and advanced analytical equipment and algorithms for processing, inspecting and drawing meaningful insights out of it.

Here, are significant advantages of using Data Analytics Technology:

- Data is the oil for today’s world. With the right tools, technologies, algorithms, we can use statistics and convert it into a different business advantage
- Data Science can help you to notice fraud the use of advanced machine learning algorithms
- It helps you to prevent any widespread economic losses
- Allows to construct talent ability in machines
- You can function sentiment analysis to gauge purchaser company loyalty
- It enables you to take better and faster decisions
- Helps you to advise the proper product to the proper patron to enhance your business

**Data Science Applications**

The role of Data Science Applications hasn’t developed overnight. Thanks to faster computing and cheaper storage, we can now predict results in minutes, what ought to take quite a few human hours to process.

A Data Scientist gets domestic a whopping $124,000 12 months and they owe it to the deficiency of knowledgeable gurus in this field. This is the purpose why Data Science Certifications are at an all-time high!

Through this blog, we bring to you, 10 purposes that build upon the concepts of Data Science, exploring a number of domains such as the following:

- Fraud and Risk Detection
- Healthcare
- Internet Search
- Targeted Advertising
- Website Recommendations
- Advanced Image Recognition
- Speech Recognition
- Airline Route Planning
- Gaming
- Augmented Reality

**Fraud and Risk Detection**

The earliest applications of facts science had been in Finance. Companies had been fed up of bad debts and losses every year. However, they had a lot of facts which use to get collected during the initial paperwork whilst sanctioning loans. They decided to deliver in records scientists in order to rescue them out of losses.

Over the years, banking companies realized to divide and overcome statistics by means of consumer profiling, past expenditures, and other fundamental variables to analyze the chances of risk and default. Moreover, it additionally helped them to push their banking products based totally on customer’s purchasing power.

**Healthcare**

The healthcare sector, especially, receives exceptional benefits from statistics science applications.

**1.Medical Image Analysis**

Procedures such as detecting tumors, artery stenosis, organ delineation employ quite a number distinctive techniques and frameworks like MapReduce to discover optimum parameters for duties like lung texture classification. It applies computer getting to know methods, help vector machines (SVM), content-based clinical picture indexing, and wavelet analysis for stable texture classification.

**2.Genetics & Genomics**

Data Science purposes also allow a superior degree of treatment personalization thru research in genetics and genomics. The aim is to apprehend the impact of the DNA on our health and locate person organic connections between genetics, diseases, and drug response. Data science methods allow integration of specific types of statistics with genomic information in the disorder research, which offers a deeper perception of genetic issues in reactions to specific capsules and diseases. As soon as we collect reliable private genome data, we will reap a deeper grasp of the human DNA. The advanced genetic danger prediction will be a important step in the direction of greater person care.

This is now not the solely purpose why Data Science has come to be so popular. Let’s dig deeper and see how Data Science is being used in a number of domains.

- How about if you ought to understand the specific requirements of your customers from the current data like the customer’s past shopping history, purchase history, age and income. No doubt you had all this records formerly too, however now with the extensive amount and variety of data, you can educate fashions more effectively and advise the product to your customers with greater precision. Wouldn’t it be terrific as it will deliver extra business to your organization?

Let’s take a different scenario to apprehend the role of Data Science in selection making. How about if your car had the intelligence to drive you home? The self-driving automobiles acquire live data from sensors, inclusive of radars, cameras and lasers to create a map of its surroundings. Based on this data, it takes choices like when to speed up, when to speed down, when to overtake, where to take a turn – making use of superior machine getting to know algorithms.

- Let’s see how Data Science can be used in predictive analytics. Let’s take weather forecasting as an example. Data from ships, aircrafts, radars, satellites can be amassed and analyzed to construct models. These models will now not solely forecast the weather but also help in predicting the occurrence of any herbal calamities. It will assist you to take suitable measures in the past and shop many treasured lives.

* **BASIC COMPONENTS OF DATA SCIENCE*

*BASIC COMPONENTS OF DATA SCIENCE*

**DATA**

Data is a very fundamental issue of facts science. There are unique sorts of data. This photograph suggests you what are exceptional kinds.

Data is divided into categorical or qualitative data and numerical or quantitative data.

Categorical or qualitative data is based on descriptive facts e.g. He is a cleaver boy. It has further three types:-

- Binomial Data (Variable data with only two selections e.g. good or bad, true or false )
- Nominal or Unordered Data (Variable data which is in unordered form e.g. red, green, man )
- Ordinal Data (Variable data with applicable order e.g. short, medium, long)

Numerical or quantitative data is primarily based on numerical facts e.g. He has 2 legs. It is in addition divided into:

- Discrete data (This statistics is countable e.g. no. of children, complete numbers) and
- Continuous data (This data is measurable e.g. height, width, size ). Continuous data has similarly two types.
- Interval (No true zero e.g. absence of temperature)
- Ratio (Absolute zero e.g. height can be zero)

**BIG DATA**

Big data consists of massive data sets. These data sets are analyzed and visualized to unveil the trends, human behavior, and interactions.

A superb example of large data is social media website Facebook the place heaps of terabytes statistics is delivered every day in the structure of text, audio, video, images etc.

**MACHINE LEARNING**

Machine Learning is a phase of Data Science that allows the system to technique data sets without any human interference (autonomously). It makes use of specific algorithms to work on a huge volume of data generated from number sources and makes a prediction, evaluation patterns and offers recommendations. The real-life example of Machine learning is its use in fraud detection and consumer retention.

Machine gaining knowledge of has three types.

- Supervised machine learning (labeled data sets are used, here input and output variables are used to produce an outcome)
- Unsupervised machine learning (un-labeled data sets are used, here only input variables are used and no output variable is used)
- Reinforcement gaining knowledge of (It is one-of-a-kind from supervised machine learning. It is about taking appropriate action in a unique state of affairs to maximize the reward.)

**STATISTICS AND PROBABILITY**

Statistics and Probability are assumed crucial elements in data science as they make the numerical basis of data science and likelihood. It is challenging to do data science except the primary knowledge of information and probability.

**PROGRAMMING LANGUAGES**

Programming languages especially Python and R play a essential position in data organization, visualization, and information investigation. Python is a high-level programming language that offers free libraries for records analysis. It is popular amongst the facts scientists.

R is every other popular language. The high-quality feature of R is a records visualization. This language is in the main used for social media post-analysis.

There are some other languages that grant support for information science like Java eight with Lambdas and Scala. SQL is used for structured records and NoSQL for unstructured data.

* **MAIN PROCESSES OF DATA SCIENCE*

*MAIN PROCESSES OF DATA SCIENCE*

Main processes of Data Science are as follows:

**DATA EXPLORATION**

It is a necessary step as it consumes most amount of time span. About 70% of the time is spent on information investigation.

The principle element for data science is information, so, when we get information, it is only from time to time that information is in a proper organized structure.

There is a ton of commotion current in the information which means a terrific amount of undesirable data that isn’t required. So what we do in this progression? This progression includes examining and change of information in which we take a look at the perceptions (lines) and highlights (segments) and expel the commotion with the aid of utilizing measurable techniques.

This development is likewise used to take a look at the relationship amongst exclusive features(columns) in the informational index, by way of the relationship we imply whether the features(columns) are difficulty to one some other or self-sufficient of one another, regardless of whether or not there are lacking traits in the information or not. So, really the information is modified and prepared for further use.

**MODELING:**

At this point, our information is organized and prepared to go ahead. This is the second step the place we truly utilized the Machine Learning algorithms to fit the information into the model.

The determination of a model relies upon the kind of information we have and the enterprise prerequisite. For instance, the model desire for prescribing an article to a customer will be no longer quite the identical as the model required for foreseeing the quantity of articles that will be bought on a unique day.

**MODEL TESTING:**

Model deployment is the subsequent stage and necessary for the execution of the model. The model is tried with test data to check the precision and distinctive features of the model and roll out the required improvements in the model to get the best outcome.

In the match that we don’t get the perfect precision we can again go to preceding Step-II i.e. modeling, select an alternate model and in a while rehash a similar Step-III i.e. model testing and select the model which offers the best outcome according to the business necessity.

**MODEL DEPLOYMENT:**

When we achieve the perfect outcome by appropriate testing in accordance to the business prerequisites, we conclude the model, which gives us the fine outcome according to testing outcomes and send the model in the manufacturing location.

### Job Trends

**Jobs by Salary**

Nearly 46% of Data Scientists earn a salary between 6-15 LPA.

**Education Requirement**

Candidates with B.Tech / B.E. or M.Tech / M.E. degrees are sought by 37% of the recruiters.

**Experience Requirement**

17% of available job requirements are looking for fresher candidates

38% of Data Science job openings are for professionals with more than 5 years of job experience

**Requirement by Tools**

Python is one of the most sought-after tool by companies followed by R.

**Industries Hiring**

Banking & financial are the leaders with over 40% all jobs advertised

Energy and Utilities contribute 15% of total jobs

**Data Science Applications**

The role of Data Science Applications hasn’t developed overnight. Thanks to faster computing and cheaper storage, we can now predict results in minutes, what ought to take quite a few human hours to process.

A Data Scientist gets domestic a whopping $124,000 12 months and they owe it to the deficiency of knowledgeable gurus in this field. This is the purpose why Data Science Certifications are at an all-time high!

Through this blog, we bring to you, 10 purposes that build upon the concepts of Data Science, exploring a number of domains such as the following:

- Fraud and Risk Detection
- Healthcare
- Internet Search
- Targeted Advertising
- Website Recommendations
- Advanced Image Recognition
- Speech Recognition
- Airline Route Planning
- Gaming
- Augmented Reality

**Fraud and Risk Detection**

The earliest applications of facts science had been in Finance. Companies had been fed up of bad debts and losses every year. However, they had a lot of facts which use to get collected during the initial paperwork whilst sanctioning loans. They decided to deliver in records scientists in order to rescue them out of losses.

Over the years, banking companies realized to divide and overcome statistics by means of consumer profiling, past expenditures, and other fundamental variables to analyze the chances of risk and default. Moreover, it additionally helped them to push their banking products based totally on customer’s purchasing power.

**Healthcare**

The healthcare sector, especially, receives exceptional benefits from statistics science applications.

**1.Medical Image Analysis**

Procedures such as detecting tumors, artery stenosis, organ delineation employ quite a number distinctive techniques and frameworks like MapReduce to discover optimum parameters for duties like lung texture classification. It applies computer getting to know methods, help vector machines (SVM), content-based clinical picture indexing, and wavelet analysis for stable texture classification.

**2.Genetics & Genomics**

Data Science purposes also allow a superior degree of treatment personalization thru research in genetics and genomics. The aim is to apprehend the impact of the DNA on our health and locate person organic connections between genetics, diseases, and drug response. Data science methods allow integration of specific types of statistics with genomic information in the disorder research, which offers a deeper perception of genetic issues in reactions to specific capsules and diseases. As soon as we collect reliable private genome data, we will reap a deeper grasp of the human DNA. The advanced genetic danger prediction will be a important step in the direction of greater person care.

**3.Drug Development**

The drug discovery technique is rather intricate and entails many disciplines. The best ideas are frequently bounded via billions of testing, large economic and time expenditure. On average, it takes twelve years to make an official submission.

Data science functions and computer gaining knowledge of algorithms simplify and shorten this process, including a point of view to every step from the initial screening of drug compounds to the prediction of the success charge based on the organic factors. Such algorithms can forecast how the compound will act in the physique using advanced mathematical modeling and simulations alternatively of the “lab experiments”. The concept behind the computational drug discovery is to create laptop model simulations as a biologically relevant community simplifying the prediction of future results with excessive accuracy.

**4.Virtual assistance for patients and customer support**

Optimization of the clinical manner builds upon the idea that for many instances it is no longer truly integral for patients to visit physicians in person. A mobile application can give a extra high-quality solution through bringing the doctor to the patient instead.

The AI-powered mobile apps can provide primary healthcare support, normally as chatbots. You without a doubt describe your symptoms, or ask questions, and then get hold of key facts about your clinical situation derived from a vast network linking symptoms to causes. Apps can remind you to take your medication on time, and if necessary, assign an appointment with a doctor.

**Internet Search**

Now, this is possibly the first element that strikes your thought when you suppose Data Science Applications.

When we talk of search, we suppose ‘Google’. Right? But there are many different search engines like Yahoo, Bing, Ask, AOL, and so on. All these search engines (including Google) make use of data science algorithms to deliver the first-rate end result for our searched question in a fraction of seconds. Considering the reality that, Google tactics more than 20 petabytes of records every day.

Had there been no statistics science, Google wouldn’t have been the ‘Google’ we know today.

**Targeted Advertising**

If you concept Search would have been the largest of all data science applications, right here is a challenger – the complete digital advertising and marketing spectrum. Starting from the show banners on a number of websites to the digital billboards at the airports – almost all of them are determined by means of the usage of records science algorithms.

This is the purpose why digital commercials have been able to get a lot higher CTR (Call-Through Rate) than regular advertisements. They can be centered based totally on a user’s past behavior.

at the equal time.ions of products accessible with them however also adds a lot to the person experience.

A lot of agencies have fervidly used this engine to promote their products in accordance with user’s interest and relevance of information. Internet giants like Amazon, Twitter, Google Play, Netflix, Linkedin, imdb and many more use this system to improve the person experience. The tips are made based on previous search results for a user.

**Advanced Image Recognition**

You upload your picture with pals on Facebook and you start getting guidelines to tag your friends. This computerized tag suggestion feature makes use of face focus algorithm.

In their modern-day update, Facebook has outlined the extra growth they’ve made in this area, making precise observe of their advances in image recognition accuracy and capacity.

“We’ve witnessed large advances in photograph classification (what is in the image?) as well as object detection (where are the objects?), however this is just the beginning of appreciation the most applicable visual content material of any picture or video. Recently we’ve been designing methods that discover and section every and every object in an image, a key functionality that will allow completely new applications.”

**6. VITAL DATA SCIENCE SKILLS EVERY DATA SCIENTIST MUST POSSESS!**

Acquiring the required data science competencies has emerge as a very prominent necessary if you’re looking to become a data scientist. As such, data scientist is one of the top-notch job roles whereby enormously essential expert aspirants are queuing to work!

While many of you may additionally have the proper abilities and expertise, it is also important that you exhibit them well.

Data Science is a highly complex and niche phase and it requires a bag full of skills. Amongst them there are a few features that are need to have on your resumes.

We have listed 6 information factors which are a ought to for each and every Data Scientist. Here it goes:

**(1) Statistical skills:**

Data Science capabilities is all about decoding the underlying insights from the facts sets, which includes a sturdy command over statistics. As a budding Data Scientist, you need to be well-versed with concepts like checking out hypotheses, distributions, Bayesian Analysis, Identifying the first-class estimator, Parameter estimation etc.

A mastery over core statistical ideas like Linear Regression, Time-series Analysis and non-straight regression are additionally quintessential attributes of a Data Science.

**(2) Mathematical skills:**

Certain mathematical standards like Multivariate Calculus, Liner Algebra, and Probability theory structure the foundation for Data Science. Most of the Data Scientists have a strong expertise about Applied Mathematics which helps them to sail through their function with ease.

Even those who do no longer have a background in mathematics want to attain a sturdy foothold in these concepts. We have determined that the businesses normally decide on candidates who have an educational qualification in Maths and Stats such as Ph.D, Masters in Applied Mathematics, Masters in Statistics, etc.

**(3) Programming skills:**

It goes except saying that for a beneficial profession in Data Science, you must have a robust computing mind set and the capability to understand codes. An expert degree expertise of programming languages plays a massive role in determining your success in your career as a Data Scientist. To commence with, the knowledge of R is a must-have in the kitty.

R is a programming language that is used in most of the statistical problem-solving in Data Science and is in particular designed for the scientists. Python is also a widely-used language in Data Science and is notably less difficult to draw close and use.

Python can additionally be used throughout massive data-sets and also in growing information sets. More than half of the world’s Data Scientists vouch for Python as the foundation for executing data analysis projects.

**(4) Participation in real-world events:**

Today there are a lot of hackathons, events, coding seminars and Data science meets organised via leading corporations to groom younger Genius and also scout for the best. Participation in these activities no longer only helps you to network easily but additionally develop your know-how horizon to face real-world challenges.

Bootcamps, Workshops on AI and Machine Learning help you in taking your abilities to a whole new level and also offers you an facet over other candidates who are greater focussed on theoretical concepts. Some of the typical activities for Data Science are Data Science Congress, Practicla Machine Learning & Data Science Workshop, DataHack Summit etc.

**(5) Comfort with unstructured data:**

Data Scientists have to work with huge quantities of data most of which is also unstructured. This data is taken from obscure places, social media, videos, blogs, audios, net sites, and different open data sources.

Sanitizing such data sets and structuring them into an organised pattern requires an eye for detail and a sorted notion process. There are many software program and equipment such as Hadoop, Apache, NoSQL, Polybase to take care of unstructured records and a data scientist ought to be able to cope with them very well.

**(6) Data Storytelling:**

Knowing how to present your insights is as important as being able to cope with to derive these insights. The insights have to be in a form that is relatable to the variety of stakeholders. Data Scientists who are adept at this existing their findings through Data Visualisation techniques and weave the insights into a story that is crisp, engaging and relatable to absolutely everyone involved in the selection making process.

The quite a number of tools that resource the Data Scientists in visualisation and storytelling is Tableau, Plotly, Chart.js, DataHero etc. Presenting the facts in an easily understandable manner helps the decision makers to recognize complex insights better, as a consequence helping faster decision-making.

**Understanding The DataScience Lifecycle**

Data science is quickly evolving to be one of the most up to date fields in the technology industry. With rapid advancements in computational performance that now allow for the evaluation of large datasets, we can discover patterns and insights about user conduct and world trends to an exceptional extent.

With the influx of buzzwords in the subject of information science and applicable fields, a frequent question I’ve heard from buddies is “Data science sounds distinctly cool – how do I get started?” And so what began out as an try to provide an explanation for it to a pal who wanted to get began with Kaggle tasks has culminated in this post. I’ll provide a quick overview of the seven steps that make up a data science lifecycle – commercial enterprise understanding, facts mining, records cleaning, information exploration, characteristic engineering, predictive modeling, and data visualization. For each step, I will also furnish some sources that I’ve observed to be useful in my experience.

As a disclaimer, there are limitless interpretations to the lifecycle (and to what information science even is), and this is the appreciation that I have built up via my analyzing and journey so far. Data science is a quickly evolving field, and its terminology is rapidly evolving with it. If there’s something that you strongly disagree with, I’d love to hear about it!

**1**.**Business Understanding**

The data scientists in the room are the humans who preserve asking the why’s. They’re the people who prefer to make sure that every decision made in the enterprise is supported through concrete data, and that it is guaranteed (with a high probability) to attain results. Before you can even start on a information science project, it is vital that you recognize the hassle you are attempting to solve.

According to Microsoft Azure’s blog, we normally use information science to answer five types of questions:

- How an awful lot or how many? (regression)
- Which category? (classification)
- Which group? (clustering)
- Is this weird? (anomaly detection)
- Which option have to be taken? (recommendation)

In this stage, you must also be identifying the central goals of your project by using identifying the variables that want to be predicted. If it’s a regression, it should be something like a sales forecast. If it’s a clustering, it should be a consumer profile. Understanding the electricity of facts and how you can utilize it to derive results for your business with the aid of asking the right questions is extra of an art than a science, and doing this nicely comes with a lot of experience. One shortcut to gaining this trip is to examine what other human beings have to say about the topic, which is why I’m going to advocate a bunch of books to get started.

**2. Data Mining**

Now that you’ve described the targets of your project, it’s time to start gathering the data. Data mining is the process of gathering your facts from different sources. Some people have a tendency to group data retrieval and cleansing together, but each of these strategies is such a full-size step that I’ve decided to destroy them apart. At this stage, some of the questions really worth considering are – what data do I need for my project? Where does it live? How can I attain it? What is the most efficient way to save and access all of it?

If all the information fundamental for the project is packaged and passed to you, you’ve won the lottery. More often than not, discovering the right facts takes both time and effort. If the data lives in databases, your job is highly simple – you can query the applicable data using SQL queries, or manipulate it the use of a dataframe device like Pandas. However, if your data doesn’t truly exist in a dataset, you’ll want to scrape it. Beautiful Soup is a famous library used to scrape web pages for data. If you’re working with a cellular app and prefer to track user engagement and interactions, there are infinite equipment that can be integrated within the app so that you can start getting precious information from customers. Google Analytics, for example, lets in you to outline customized occasions inside the app which can assist you understand how your customers behave and collect the corresponding data.

**3.Data Cleaning**

Now that you’ve bought all of your data, we move on to the most time-consuming step of all – cleansing and making ready the data. This is in particular authentic in huge data projects, which often contain terabytes of information to work with. According to interviews with data scientists, this procedure (also referred to as ‘data janitor work’) can often take 50 to 80 percentage of their time. So what exactly does it entail, and why does it take so long?

The motive why this is such a time-consuming technique is absolutely because there are so many viable situations that should necessitate cleaning. For instance, the statistics could also have inconsistencies inside the identical column, that means that some rows should be labelled 0 or 1, and others should be labelled no or yes. The data sorts should additionally be inconsistent – some of the 0s may integers, whereas some of them ought to be strings. If we’re dealing with a specific records kind with more than one category, some of the classes may want to be misspelled or have different cases, such as having categories for each male and Male. This is just a subset of examples the place you can see inconsistencies, and it’s essential to trap and restore them in this stage.

One of the steps that is often forgotten in this stage, causing a lot of troubles later on, is the presence of missing data. Missing data can throw a lot of mistakes in the model advent and training. One choice is to either ignore the instances which have any missing values. Depending on your dataset, this could be unrealistic if you have a lot of missing data. Another common approach is to use something known as common imputation, which replaces missing values with the average of all the other instances. This is not usually advocated because it can decrease the variability of your data, however in some cases it makes sense.

**4.Data Exploration**

Now that you’ve bought a glowing clean set of data, you’re prepared to subsequently get started out in your analysis. The data exploration stage is like the brainstorming of information analysis. This is where you understand the patterns and bias in your data. It could contain pulling up and analyzing a random subset of the information the use of Pandas, plotting a histogram or distribution curve to see the everyday trend, or even developing an interactive visualization that lets you dive down into each data point and discover the story behind the outliers.

Using all of this information, you begin to form hypotheses about your information and the trouble you are tackling. If you had been predicting student scores for example, you should strive visualizing the relationship between scores and sleep. If you had been predicting actual property prices, you may want to possibly plot the expenses as a warmth map on a spatial plot to see if you can catch any trends.

There is a tremendous summary of equipment and procedures on the Wikipedia web page for exploratory information analysis.

**5**.**Feature Engineering**

In machine learning, a feature is a measurable property or attribute of a phenomenon being observed. If we had been predicting the scores of a student, a possible characteristic is the amount of sleep they get. In more complicated prediction tasks such as character recognition, points may want to be histograms counting the wide variety of black pixels

According to Andrew Ng, one of the top experts in the fields of machine learning and deep learning, “Coming up with aspects is difficult, time-consuming, requires expert knowledge. ‘Applied desktop learning’ is basically characteristic engineering.” Feature engineering is the system of using domain understanding to transform your raw data into informative features that characterize the commercial enterprise trouble you are attempting to solve. This stage will at once have an effect on the accuracy of the predictive model you construct in the next stage.

We commonly function two kinds of tasks in feature engineering – feature selection and construction.

Feature choice is the method of cutting down the elements that add extra noise than information. This is commonly achieved to avoid the curse of dimensionality, which refers to the improved complexity that arises from high-dimensional areas (i.e. way too many features). I won’t go too a lot into detail right here due to the fact this topic can be enormously heavy, however we commonly use filter techniques (apply statistical measure to assign scoring to every feature), wrapper methods (frame the decision of elements as a search hassle and use a heuristic to perform the search) or embedded strategies (use computing device gaining knowledge of to discern out which features make a contribution best to the accuracy).

Feature building includes developing new features from the ones that you already have (and maybe ditching the historic ones). An example of when you might favor to do this is when you have a non-stop variable, but your area knowledge informs you that you only really need an indicator variable based on a acknowledged threshold. For example, if you have a characteristic for age, however your model only cares about if a individual is an adult or minor, you should threshold it at 18, and assign one-of-a-kind categories to cases above and below that threshold. You may want to additionally merge a couple of elements to make them more informative with the aid of taking their sum, distinction or product. For example, if you have been predicting student rankings and had facets for the wide variety of hours of sleep on every night, you would possibly want to create a characteristic that denoted the common sleep that the pupil had instead.

Get started: Introduction to Feature Selection Methods, Feature Selection with sklearn, Best Practices for Feature Engineering

**6.Predictive Modeling**

Predictive modeling is where the machine learning sooner or later comes into your information science project. I use the term predictive modeling because I assume a correct venture is no longer one that just trains a model and obsesses over the accuracy, however additionally makes use of comprehensive statistical strategies and checks to make certain that the results from the model actually make feel and are significant. Based on the questions you asked in the enterprise understanding stage, this is the place you figure out which mannequin to pick for your problem. This is never an easy decision, and there is no single proper answer. The mannequin (or models, and you should always be testing several) that you end up education will be structured on the size, type and quality of your data, how tons time and computational assets you are inclined to invest, and the kind of output you intend to derive. There are a couple of exclusive cheat sheets available on-line which have a flowchart that helps you decide the right algorithm based on the type of classification or regression problem you are trying to solve. The two that I sincerely like are the Microsoft Azure Cheat Sheet and SAS Cheat Sheet.

Once you’ve educated your model, it is imperative that you evaluate its success. A technique referred to as k-fold pass validation is frequently used to measure the accuracy of a model. It entails separating the dataset into k equally sized organizations of instances, education on all the groups besides one, and repeating the manner with different corporations left out. This allows the model to be skilled on all the facts as an alternative of the use of a normal train-test split.

For classification models, we frequently test accuracy the use of PCC (percent correct classification), along with a confusion matrix which breaks down the errors into false positives and false negatives. Plots such as ROC curves, which is the proper wonderful charge plotted against the false effective rate, are also used to benchmark the success of a model. For a regression model, the frequent metrics include the coefficient of dedication (which gives statistics about the goodness of fit of a model), imply squared error (MSE), and average absolute error.

**7**.**Data Visualization**

Data visualization is a tricky field, in most cases due to the fact it appears easy however it ought to per chance be one of the hardest matters to do well. That’s because data viz combines the fields of communication, psychology, statistics, and art, with an ultimate intention of communicating the records in a simple yet wonderful and visually desirable way. Once you’ve derived the meant insights from your model, you have to symbolize them in way that the specific key stakeholders in the venture can understand.

Again, this is a subject matter that ought to be a weblog publish on its own, so as a substitute of diving deeper into the area of statistics visualization, I will supply a couple of beginning points. I for my part love working via the evaluation and visualization pipeline on an interactive Python pocket book like Jupyter, in which I can have my code and visualizations side through side, permitting for rapid generation with libraries like Seaborn and Bokeh. Tools like Tableau and Plotly make it honestly convenient to drag-and-drop your statistics into a visualization and manipulate it to get greater complicated visualizations. If you’re building an interactive visualization for the web, there is no higher beginning point than D3.js.

**8**.**Business Understanding**

Phew. Now that you’ve long gone thru the complete lifecycle, it’s time to go again to the drawing board. Remember, this is a cycle, and so it’s an iterative process. This is where you evaluate how the success of your mannequin relates to your authentic business understanding. Does it handle the issues identified? Does the analysis yield any tangible solutions? If you encountered any new insights at some stage in the first iteration of the lifecycle (and I assure you that you will), you can now infuse that expertise into the next iteration to generate even extra powerful insights, and unleash the electricity of facts to derive phenomenal outcomes for your business or project.

**The 17 Best Free Tools for Data Science**

One of the best things about working in the data science industry is that it is full of free tools. The data science neighborhood is, by way of and large, quite open and giving, and a lot of the tools that professional data analysts and data scientists use each day are completely free.

If you are simply getting started, though, the sheer wide variety of resources accessible to you can be overwhelming**. **So rather than bury you in a listing of open-source goodies, we’ve picked out some of our absolute favorites: the fine free equipment for data science using Python, R, and SQL**.**

**Languages**

It’s convenient to overlook because they’re so ubiquitous, but programming languages are certainly the pleasant free tools for data science work. Simply learning one of these languages places terrific analytical power at your fingertips. And the three we’ve listed right here — the three most commonly-used languages in data science — are all completely free to use.

For submit people, languages are the largest preference they shall make when selecting data science tools. The three best languages are:

- R
- Python
- SQL

You’ll find hundreds of articles that try to tease aside which of Python and R are better for data science. We’ve written our very own article comparing Python versus R on greater objective grounds — how each language handles frequent data science tasks.

The reality is that they’re both great options each with their respective strengths, which we are going to outline below. If you are simply starting out, it’s better to pick either and start learning, as a substitute than wasting time making an attempt to work out which is best.

SQL, on the other hand, is greater complementary to each Python and R. It might not be the first language you learn; however you will need to study it.

**1.R**

The R programming language used to be initially created in the mid-90s. R is the statistical language of choice during academia and has a reputation for being easy to learn, in particular for these who’ve in no way used a programming language before.

A key gain of the R language is that it was once designed especially for statistical computing, so many of the key elements that data scientists need is built-in.

R also has a sturdy ecosystem of applications that allow for extended capabilities. There are quite a few R packages which are considered via many to be vital if you are working with data. We’ll define those later in the “R Packages” section.

**2.Python**

Like R, Python was once also created in the 90s. But in contrast to R, Python is a general-purpose programming language. It’s often used for web development, and it is one of the most popular usual programming languages.

Using Python for data science work started to turn out to be famous in the mid-to-late ’00s after specialized libraries (analogous to R packages) emerged that provided higher functionality for working with data. Over the ultimate decade, Python’s use as a data science language has grown tremendously, and it is now the most popular language for data science with the aid of some metrics.

One of the key advantages of Python is that due to the fact it’s a general-purpose language, it is less complicated to operate prevalent tasks that intersect with your data work. Similarly, if you research Python and later figure out that software development is a higher suit for you than data science, a lot of what you have discovered is transferable.

**3.SQL**

SQL is complimentary language to Python and R — often it will be the 2d language any individual learns if they’re searching to get into facts science. SQL is a language used to have interaction with data stored in databases.

Because most of the world’s data is saved in databases, SQL is a highly treasured language to learn. It’s common for information scientists to use SQL to retrieve data that they will then smooth and analyze using Python or R.

Many agencies also use SQL as a “first-class” evaluation language, the usage of tools that enable visualizations and reviews to be built at once from the effects of SQL queries.

**R Packages**

R has a thriving ecosystem of programs that add functionality to the core R language. These packages are allotted by using CRAN and can be downloaded the use of R syntax (as antagonistic to Python that uses separate package deal managers). The applications we listing under are some of the most generally used and popular packages for information science in R.

**4.Tidyverse**

Technically, tidyverse is a collection of R packages, however we include it right here together due to the fact it is the most generally used set of packages for facts science in R. two Key packages in the collection include dplr for data manipulation, readr for importing data, ggplot2 for facts visualization, and many more.

The tidyverse packages have an opinionated design philosophy that revolves around “tidy data” — data with a consistent form that makes analysis (particularly with tidyverse packages) easier.

The popularity of the tidyverse has grown to the point that, for many, the idea of ”working in R” really means working with the tidyverse in R.

**5.ggplot2**

The ggplot2 package allows you to create data visualizations in R. Even though ggplot2 is part of the tidyverse collection, it predates the collection and is important sufficient to mention is its own.

ggplot2 is famous because it permits you to create professional-looking visualizations quick the usage of easy-to-understand syntax.

R includes plotting performance built-in, but the ggplot package deal is commonly viewed most desirable and less difficult to use and is the variety one R package deal for information visualization.

**6.R Markdown**

The R Markdown bundle helps the creation of reviews using R. R Markdown archives are textual content documents that include code snippets interleaved with markdown text.

R Markdown documents are frequently edited in a pocket book interface that permits the advent of code and textual content aspect through side. The pocket book interface lets in the code to be achieved and the output of the code to be viewed in line with the text

R Markdown files can be rendered into many versatile formats together with HTML, PDF, Microsoft Word, books, and more!

**7.Shiny**

The Shiny bundle approves you to construct interactive net apps the use of R. You can build functionality that allows humans to interact with your data, analysis, and visualizations as a internet page.

Shiny is especially powerful because it eliminates the need for web improvement skills and knowledge when creating apps and lets in you to focus on your data.

**8.mlr**

The mlr package deal presents a standard set of syntax and elements that enable you to work with computer getting to know algorithms in R. While R has built-in machine learning capabilities, they are cumbersome to work with. Mlr gives an easier interface so you can focus on coaching your models.

mlr consists of classification, regression, and clustering analysis techniques as well as limitless different related capabilities.

**8.Python Libraries**

Like R, Python also has a thriving package deal ecosystem, though Python packages are often known as libraries.

Unlike R, Python’s major purpose is not as a data science language, so use of data-focused libraries like pandas is greater or much less obligatory for working with information in Python.

Python programs can be downloaded from PyPI (the Python Package Index) using pip, a device that comes with Python but is external to the Python coding environment.

(A complementary alternative to pip is the conda package manager, which we will speak about later on.)

**9.pandas**

The pandas library is constructed for cleaning, manipulating, reworking and visualizing data in Python. Although it is a single package, its closest analog in R is the tidyverse collection.

In addition to offering a lot of convenience, pandas are also frequently quicker than pure Python for working with data. Like R, pandas take benefit of vectorization, which speeds up code execution.

**10.NumPy**

NumPy is a critical Python library that gives functionality for scientific computing. NumPy affords some of the core common sense that pandas is built upon. Usually, most data scientists will work with pandas, however knowing NumPy is necessary as it approves you to get entry to some of the core performance when you need to.

**11.Matplotlib**

The Matplotlib library is a effective plotting library for Python. Data scientists regularly use the Pyplot module from the library, which presents a widespread interface for plotting data.

The plotting functionality that is protected in pandas calls Matplotlib below the hood, so perception matplotlib helps with customizing plots you make in pandas.

**12.Scikit-Learn**

Scikit-learn is the most famous machine learning library for Python. The library presents a set of equipment built on NumPy and Matplotlib for that permit for the coaching and education of computing device studying models.

Available model types encompass classification, regression, clustering, and dimensionality reduction.

**13.Tensorflow**

Tensorflow is a Python library at the start developed by way of Google that affords an interface and framework for working with neural networks and deep learning.

Tensorflow is best for duties where deep learning excels, such as pc vision, herbal language processing, audio/video recognition, and more.

So far, we’ve regarded at the great languages for data science and the satisfactory packages for two of these languages. (As a question language, SQL is a bit exceptional and would not use “packages” in the same sense).

Next, we will look at some software tools that are useful for information science work. These don’t seem to be all open-source, however they’re free for anyone to use, and if you work with data on a regular basis they can be huge time-savers.

**14.Google Sheets**

If this had been now not a list of free tools, then definitely Microsoft Excel would be at the top of this list. The ubiquitous spreadsheet software program makes it rapid and effortless to work with statistics in a visual way, and is used with the aid of hundreds of thousands of humans round the world.

Google’s Excel clone has of the core performance of Excel, and is on hand free to all people with a Google account.

**15.RStudio Desktop**

RStudio Desktop is the most popular environment for working with R. It includes a code editor, an R console, notebooks, tools for plotting, debugging, and more.

Additionally, Rstudio (the agency who make Rstudio Desktop) are at the core of current R development, using the developers of the tidyverse, shiny, and different vital R packages.

**16.Jupyter Notebook**

Jupyter Notebook is the most famous environment for working with Python for data science. Similar to R Markdown, Jupyter notebooks permit you to combine code, text, and plots in a single document which makes data work easy.

Like RMarkdown, Jupyter notebooks can be exported in a quantity of formats which includes HTML, PDF, and more.

Dataquest’s guided Python data science projects nearly all task students with building initiatives in Jupyter Notebooks, due to the fact it’s what working information analysts and scientists usually do in real-world work.

**17.Anaconda**

Anaconda is a distribution of Python designed especially to help you get the scientific Python tools installed. Before Anaconda, the only option was once to install Python through itself, and then installation packages like NumPy, pandas, Matplotlib one with the aid of one. That which wasn’t continually a simple process, and it was once regularly challenging for new learners.

Anaconda consists of all of the major applications wanted for information science in one effortless install, which saves time and approves you to get started out quickly. It additionally has Jupyter Notebooks built-in, and makes beginning a new records science task easily accessible from a launcher window. It is the advocated way to get commenced the use of Python for information science.

Anaconda also consists of the conda bundle manager, which can be used as an alternative to pip to set up Python applications (although you can also use pip if you prefer).

**Data Science Career Opportunities: Your Guide to Unlocking Top Data Scientist Jobs**

In a world where 2.5 quintillion bytes of facts is produced every day, an expert who can arrange this humongous data to furnish enterprise options is indeed the hero! Much has been spoken about why Big Data is right here to stay and why Big Data Analytics is the nice profession move. Building on what’s already been written and said, let’s discuss Data Science profession opportunities and why ‘Data Scientist’ is the sexiest job title of the 21st century.

**Data Science Career Opportunities**

A Data Scientist, according to Harvard Business Review, “is a high-ranking professional with the education and curiosity to make discoveries in the world of Big Data”. Therefore, it comes as no shock that Data Scientists are coveted experts in the Big Data Analytics and IT industry.

With specialists predicting that 40 zettabytes of data will be in existence via 2020 (Source), Data Science profession possibilities will only shoot via the roof! Shortage of expert authorities in a world which is more and more turning to records for decision making has also led to the large demand for Data Scientists in start-ups as nicely as well-established companies. A McKinsey Global Institute find out about states that by means of 2018, the US on my own will face a scarcity of about 190,000 gurus with deep analytical skills. With the Big Data wave displaying no signs of slowing down, there’s a rush amongst global companies to employ Data Scientists to tame their business-critical Big Data.

**Data Scientist Salary Trends**

A document by way of Glassdoor indicates that Data scientists lead the pack for the exceptional jobs in America. The document goes on to say that the median earnings for a Data Scientist is an superb $91,470 in the US and ₹622,162 and there are over 2300 job openings posted on the website (Source).

On Indeed.com, the average Data Scientist salaries for job postings in the US are 80% greater than common salaries for all job postings nationwide, as of May 2019.

In India the trend is no different; as of May 2019, the median revenue for a Data Scientist function is Rs. 622,162 according to Payscale.com.

**Data Scientist Job Roles**

A Data Scientist dons many hats in his/her workplace. Not solely are Data Scientists accountable for business analytics, they are also involved in building data products and software program platforms, alongside with growing visualizations and computer gaining knowledge of algorithms.

Some of the distinguished Data Scientist job titles are:

- Data Scientist
- Data Architect
- Data Administrator
- Data Analyst
- Business Analyst
- Data/Analytics Manager
- Business Intelligence Manager

**TOP DATA SCIENCE: PROFILES**

**Hot Data Science Skills**

Coding capabilities clubbed with knowledge of information and the capacity to assume critically, make up the arsenal of a successful data scientist. Some of the in-demand Data Scientist abilities that will fetch big career opportunities in Data Science are:

- Programming Languages: R/Python/Java
- Statistics and Applied Mathematics
- Working Knowledge of Hadoop and Spark
- Databases: SQL and NoSQL
- Machine Learning and Neural Networks
- Proficiency in Deep Learning Frameworks: TensorFlow, Keras, Pytorch
- Creative Thinking & Industry Knowledge

The Payscale.com chart below shows the average Data Scientist Salary by way of skills in the USA and India.

The upward swing in Data Science profession possibilities is predicted to continue for a lengthy time to come. As data pervades our lifestyles and businesses attempt to make experience of the records generated, professional Data Scientists will be persevered to be wooed via companies huge and small. Case in point, a seem at the jobs board on Indeed.com reveals pinnacle businesses competing with every different to appoint Data Scientists. A few large names encompass Facebook, Twitter, Airbnb, Apple, LinkedIn, IBM and PayPal amongst others.

The time is ripe to up-skill in Data Science and Big Data Analytics to take advantage of the Data Science career possibilities that come your way.

**Starting Your Career in Data Science: What Are Your Options?**

Learning data science skills can revolutionize your career. But unfortunately, extremely good jobs don’t actually fall out of the sky as soon as you’ve mastered Python or R, SQL, and the different indispensable technical skills. Finding a job takes time and effort. Finding the proper job takes time, effort, and knowledge.

The purpose of this career guide is to arm you with that knowledge, so you can spend your time successfully and end up with the data science profession you want.

The first step is figuring out what the profession you want in reality appears like. Where can your new data science skills take your career? Which path is proper for you?

Answering these questions should be the first step in your records science job journey. And even though the answers would possibly seem obvious, it’s really worth taking the time to probe deeper and absolutely explore all of your possible options. That’s what we’ll be doing in this article.

Specifically, we’re going to take a appear at some of the exceptional job titles and descriptions that would possibly be selections for you if you’re looking to change careers. We’ll also take a look at picks you can also now not have idea about: going freelance and the usage of data science in your current position.

**Switching Careers: What Job Titles Are Available in Data Science?**

The first step in any job search is identifying the kinds of jobs you should be looking for. In the area of data science, this gets complex quickly, for a couple of reasons:

- There’s no typical definition of “data scientist” or “data analyst” that each corporation agrees on, so one-of-a-kind positions with the identical title may require distinct ability sets
- There is a plethora of other commonly-used job titles that involve data science work that you may not discover if you’re simply searching for “data analyst” or “data scientist” roles.

Obviously, we can’t cowl each and every achievable job title that would possibly be used by means of a company, but we can speak about some of the predominant roles in the statistics science universe, how they differ, and the development of your career in the discipline if you’re beginning out in that role.

**Note:** below, we’re the use of average revenue data from Indeed for each position, primarily based on U.S. data. Obviously, salaries will differ by way of location, company, and based on your very own ability set and ride level, so it’s probably first-class to deal with these numbers as tough guidelines. They were last up to date on February 8, 2019.

**The Big Three: Data Analyst, Data Scientist, and Data Engineer**

**Data Analyst**

**Average salary:** $68,752

**What is a data analyst?** This is normally considered an “entry-level” role in the data science field, even though not all data analysts are junior and salaries can vary widely.

A facts analyst’s principal job is to look at business enterprise or enterprise data and use it to reply commercial enterprise questions, then speak these solutions to other groups in the corporation to be acted upon. For example, a data analyst would possibly be requested to appear at sales data from a latest advertising marketing campaign to examine its effectiveness and discover strengths and weaknesses. This would contain gaining access to the data, possibly cleansing it, performing some statistical evaluation to reply the relevant commercial enterprise questions, and then visualizing and communicating the results.

Over time, data analysts regularly work with a range of extraordinary teams inside a company; you may work on advertising analytics one month, then assist the CEO use data to find motives the enterprise has grown the next. You will usually be given business questions to answer as an alternative than requested to find fascinating traits on your own, as data scientists regularly are, and you’ll commonly be tasked with mining insights from information alternatively than predicting future effects with desktop learning.

**Skills required:** Specifics differ from position to position, however in general, if you’re looking for data analyst roles, you’ll favor to be comfortable with:

- Intermediate data science programming in either Python or R, which includes the use of popular packages
- Intermediate SQL queries
- Data cleaning
- Data visualization
- Probability and statistics
- Communicating complicated records analysis in reality and understandably to human beings with no data or programming background

**Career prospects:** Data analyst is a large time period that encompasses a huge range of positions, so your profession course is pretty open-ended. One common subsequent step is to continue constructing your data science abilities — frequently with a focal point on computing device mastering — and work toward a role as a data scientist. Alternatively, if you’re more involved in software development, information infrastructure, and supporting build a whole records pipeline, you should work towards a position as a data engineer. Some records analysts also use their programming competencies to transition into more standard developer roles.

If you stick with information analysis, many agencies hire senior facts analysts. At large groups with data teams, you can additionally suppose about working towards administration roles if you’re involved in growing administration skills.

**Data Scientist**

**Average salary: **$128,173

**What is a data scientist?** Data scientists do many of the identical matters as facts analysts, but they also normally build computing device mastering fashions to make correct predictions about the future based totally on previous data. A data scientist regularly has more freedom to pursue their own thoughts and experiment to find interesting patterns and developments in the information that management might also not have thinking about.

As a data scientist, you might be requested to check how an exchange in advertising strategy may want to affect your company’s backside line. This would entail a lot of information evaluation work (acquiring, cleaning, and visualizing data), but it would also probably require building and training a machine learning model that can make dependable future predictions based totally on previous data.

**Skills required: All of the capabilities required of a records analyst, plus:**

- A solid perception of each supervised and unsupervised machine learning methods
- A robust understanding of data and the capability to consider statistical models
- More advanced data-science-related programming competencies in Python or R, and probably familiarity with other equipment like Apache Spark

**Career prospects:** If you’re working as a data scientist, your subsequent job title can also well be senior data scientist, a role that’ll earn you about $20,000 more per yr on average. You would possibly also choose to specialize in addition in computer gaining knowledge of as a computing device gaining knowledge of engineer, which would also convey a pay raise. Or, you can look greater towards management with roles like lead records scientist. If you prefer to maximize earnings, your closing goal might be a C-suite position in data — such as chief data officer — although these roles require management capabilities and might also no longer contain a lot of authentic everyday work with data.

** ****Data Engineer**

**Average salary:** $132,653

**What is a data engineer?** A data engineer manages a company’s data infrastructure. Their job requires a lot much less statistical evaluation and a lot of extra software improvement and programming skill. At a organization with a data team, the data engineer may be accountable for building data pipelines to get the trendy sales, marketing, and revenue data to data analysts and scientists shortly and in a usable format. They’re additionally likely responsible for constructing and preserving the infrastructure wanted to keep and shortly access past data.

**Skills required:** The capabilities required for data engineer positions tend to be more centered on software development. Depending on the company you’re looking at, they can also also be pretty dependent on familiarity with specific technologies that are already section of the company’s stack. But in general, a data engineer needs:

- Advanced programming capabilities (probably in Python) for working with massive datasets and constructing data pipelines
- Advanced SQL skills and probably familiarity with a device like Postgres

**Career prospects:** Data engineers can pass into extra senior engineering positions via endured experience, or use their capabilities to transition into a range of different software program improvement specialties. Outside of specialization, there is also the achievable to cross into management roles, either as the chief of an engineering or data group (or both, even though only very giant organizations are probable to have a substantial data engineering team).** **

**Becoming a Data Engineer**

**1.Programming Language**: Start with learning Programming Language, like Python, as it has clear and readable syntax, versatility, and widely available resources and a very supportive community.

**2.Operating System**: Mastery in at least one OS like Linux and UNIX OS is recommended, RHEL is a prevalent OS adopted by the industry which can also be learned.

**3.DBMS**: Enhance your DMBS skills and get your hands-on experience at least one relational database, preferably MySQL or Oracle DB. Thorough with database administrator skills as well as skills like capacity planning, installation, configuration, database design, monitoring security, troubleshooting such as backup and recovery of data.

**4.NoSQL**: This is the next skill to focus as it would help you understand how to handle semi and unstructured data.

**5.ETL**: Understand to extract data using ETL and data warehousing tools from various sources. Transform and clean data according to the user and then load your data into the data warehouse. This is an important skill which data engineers must possess. Since we are at the age of revolution where the data is the fuel of the 21st century, various data sources and numerous technologies have evolved over the last two decades major ones being NoSQL databases and big data frameworks.

**6.Big Data Frameworks**: Big data engineers are required to learn multiple big data frameworks to create and design processing systems.

**7.Real-time Processing Frameworks**: Concentrate on learning frameworks like Apache Spark, which is an open-source cluster computing framework for real-time processing, and when it comes to real-time data analytics spark stands as go-to-tool across all solutions.

**8.Cloud**: Next in the career path, one must learn cloud which will serve as a big plus. A good understanding of cloud technology will provide the option of stable significant amounts of data and allowing big data to be further available, scalable and fault-tolerant.

**Other Job Titles in Data Science**

While data analyst, data scientist, and data engineer broadly describe the distinctive roles data professionals can play at a company, there are a range of other job titles you’ll see that both relate immediately to these roles or otherwise contain the use of data science skills. Below, we’ll take a speedy appear at job titles you might prefer to consider when looking for employment.

**Machine Learning Engineer**

**Average salary:** $144,085

**What is a machine learning engineer?** There is a lot of overlap between a machine learning engineer and a data scientist. At some companies, this title just potential a data scientist who has specialized in computer learning. At different companies, “machine getting to know engineer” is more of a software program engineering role that involves taking a data scientist’s evaluation and turning it into deployable software. Although the specifics vary, truly all machine learning engineer positions will require at least facts science programming capabilities and an extraordinarily advanced expertise of machine learning techniques.

You might also see positions like this listed as “Machine Learning Specialist,” particularly if the employer is searching for a data scientist who has specialized in computer gaining knowledge of instead than a software program engineer who can construct deployable products that make use of machine learning.

**Quantitative Analyst**

**Average salary:** $142,049

**What is a quantitative analyst?** Quantitative analysts, sometimes called “quants”, use superior statistical analyses to reply questions and make predictions associated to finance and risk. Needless to say, most information science programming competencies are immensely useful for quantitative analysis, and a strong understanding of records is integral to the field. Understanding of machine learning models and how they can be applied to remedy economic problems and predict markets is additionally increasingly common.

**Data Warehouse Architect**

**Average salary:** $136,151

**What is a data warehouse architect?** Essentially, this is a specialty or sub-field within facts engineering for folks who’d like to be in charge of a company’s information storage systems. SQL skills are virtually going to be vital for a function like this, though you’ll additionally need a strong command of other tech skills that’ll range based totally on the employer’s tech stack. You won’t be employed as a data warehouse architect fully on your data science skills, however the SQL capabilities and data management information you’ll have from learning data science make it a function that have to be on your radar if you’re involved in the data engineering facet of the business.

**Business Intelligence Analyst**

**Average salary:** $90,150

**What is a business intelligence analyst?** A business analyst is if truth be told a data analyst who is targeted on inspecting market and commercial enterprise trends. This position every so often requires familiarity with software-based data analysis equipment (like Microsoft Power BI), but many data science skills are also fundamental for business brain analyst positions, and many of these positions will also require Python or R programming skills.

**Statistician**

**Average salary:** $87,021

**What is a statistician?** ‘Statistician’ is what data scientists had been known as earlier than the time period ‘data scientist’ existed. Required abilities can vary quite a bit via from job to job, however all of them will require a strong grasp of chance and statistics. Programming skills, in particular in a statistics-focused language like R, are in all likelihood to be of use as well. Unlike data scientists, a statistician will no longer generally be anticipated to understand how to construct and train machine learning models (although they may additionally want to be familiar with the mathematical standards that underlie machine learning models).

**Business Analyst**

**Average salary:** $78,172

**What is a business analyst?** ‘Business analyst’ is a quite popular job title that’s utilized to a vast variety of roles, but in the broadest terms, an enterprise analyst helps organizations reply questions and remedy problems. This doesn’t necessarily contain the use of data science skills, and some business analyst positions don’t require them. But many business analysts’ jobs do require the analyst to capture, analyze, and make guidelines based on a company’s data, and having data competencies would probably make you an extra compelling candidate for nearly any enterprise analyst role.

** ****Systems Analyst**

**Average salary:** $73,574

**What is a systems analyst?** Systems analysts are regularly tasked with identifying organizational problems, and then planning and overseeing the adjustments or new systems required to remedy these problems. This normally requires programming skill (although systems analysts are not always at once involved in growing the systems they recommend) and information evaluation and statistical abilities are additionally frequently critical for identifying problematical developments and quantifying what’s working nicely and what isn’t within a company’s tech systems.

**Marketing Analyst**

**Average salary:** $66,470

**What is a marketing analyst?** Marketing analysts seem to be at sales and marketing data to verify and improve the effectiveness of marketing campaigns. In the digital age, these analysts have get admission to more and more large quantities of data, specially at corporations that promote digital products, and whilst there are a range of software program solutions like Google Analytics that can allow for decent evaluation barring programming skills, an applicant with data science and information chops is in all likelihood to have a leg up on many different candidates if they also have ample area know-how in the location of marketing. Plus, a advertising analyst whose analyses make a giant impact can set their long-term points of interest on a Chief Marketing Officer position, which pays an common of $157,960 per year.

**Operations Analyst**

**Average salary:** $62,468

**What is an operations analyst?** Operations analysts are generally tasked with analyzing and streamlining a business’s inside operations. Specific responsibilities and salaries can differ widely, and now not all operations analyst positions will make use of data skills, but in many cases, being in a position to clean, analyze, and visualize data will be necessary in figuring out what company systems are working easily and what areas may need improvement.

**Other Data Science Positions**

If you’re looking out on job sites (which would possibly no longer be the excellent idea; we’ll get to that later), preserve in idea that businesses use all sorts of titles and that you can adjust any of the above titles to your ride degree by way of tacking phrases like “junior,” “associate,” “senior,” “lead,” etc. in front of them.

Moreover, these are simply some of the regular full-time profession options. If you’re searching for data science work, there are also some picks you can also not have considered, and we’ll take a seem to be at these now.