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Data science can be defined as a combination of mathematics, business acumen, tools, algorithms and machine learning techniques that help us discover hidden ideas or raw data patterns that can be very useful for forming large business decisions.

 

In data science, they are structured and unstructured data. The algorithms also involve predictive analysis. So data science is about the present and the future. That is, finding trends based on historical data that can be useful for current decisions and finding patterns that can be modeled and used to forecast to see how things could look in the future.

 

Data Science is an amalgam of statistics, tools and business knowledge. Therefore, it is essential for a data scientist to have a good knowledge and understanding of them.

The term “data scientist” was coined in 2008, when companies realized the need for data professionals specialized in organizing and analyzing large amounts of data. 1 In a 2009 article by McKinsey & Company, Hal Varian, chief economist at Google and professor of information science, business and economics at UC Berkeley, predicted the importance of adapting to the influence of technology and reconfiguration of different industries . 2

“The Hal Varian, chief economist at Google and professor of information science, business and economics at UC Berkeley

Data scientists can identify ambiguous problems, collect data from different sources across heterogeneous platforms, organize information, translate reports into solutions and communicate results in a way that positively affects business decisions.

 

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Key Features

About Data Science Course

The emergence of data science is mainly due to the growing growth of data in business, the Internet, the increase in computer power, etc. For example, today 2.5 billion bytes of estimated data are estimated. Over time, companies have not only understood the importance of data by storing them, but also analyzing them, which affects key business decisions to gain a competitive advantage and improve the user experience.

 

In the modern history, Data Science has evolved step by step become a significant engine in several industries: risk management, fraud detection, selling improvement, selling analysis, and plenty of others.Now, Data Science has far-reaching implications for a variety of flows, both in the field of academic and applied research, such as voice recognition, digital economics, machine translation, on the one hand, while areas such as computer science. Medical, health and social sciences, on the other. side.

 

Little by little it is becoming clear that the value lies only in cleaning, processing and, finally, the analysis of large data, which is why the role of a data scientist becomes so important. We have all heard that data science is an attractive industry, but many people are not clear about the value that a data scientist adds to an organization.

 

 

 

 

 

The main focus of Data Science is to help humans make better, more effective and faster decisions. This benefit is not related to any particular sector, but to all sectors of the world, such as health, electronic commerce, retail trade, etc.

 

 

Placement Point Solutions  Certification is Accredited by all major Global Companies around the world. We provide after completion of the theoretical and practical sessions to fresher’s as well as corporate trainees.

Our certification at Placement Point Solutions is accredited worldwide. It increases the value of your resume and you can attain leading job posts with the help of this certification in leading MNC’s of the world. The certification is only provided after successful completion of our training and practical based projects.

 

One of the most common topics of discussion in industry literature since August 2017 has been Gartner’s 10 main strategic technology trends for 2019. A recurring theme in the 2019 technology trends is the combination of “physical and digital worlds.” within Data Science, AI container and machine learning. According to Gartner, intelligent self-learning systems (with ML technology) will continue to reign in the technological marathon until 2020.

 

Many industry observers echoed the prediction n. Gartner ° 1 that most IT applications will adopt artificial intelligence in one way or another in the coming years. The Journal responds with a strong acceptance of this truth in the article entitled AI, fusion of digital and physical worlds among the 10 main technological trends for 2018.

 

In fact, the four main technological trends in Gartner’s top 10 list are related to intelligent machines or systems. The first three trends, AI, smart applications and smart things are closely followed by digital twins, which refer to the world of physical responses through digital sensors or the Internet of Things (IoT). The news titled The Top 10 Technology Trends for 2018 indicates that global companies will benefit from this wave of artificial intelligence in 2018 and will use it to create differentiation in the market. The disruptive characteristics of AI, ML and IoT were highlighted in this review of the technological trends of the year of nesting.

Data Science Training Key Features

  • Overview
  • Course Content
  • Program Details
  • Reviews

Learning Outcomes of our Best Data Science Training

There are several ways to use data science in business. If you are trying to find out exactly what benefits of commercial data science have for your business, consider the following ways to use data science:

building better products

make better decisions

Automate repetitive and time-consuming processes.

Let's take a look at these three areas.

Using data science to create better products

By using data science in business, you can bring a better product to your target market in two main ways: you can customize a product or service to make it more personal, or you can provide a new product or service experience. .

Today, machine learning seems more attractive to companies in terms of generating real value and allowing innovative innovations. There are three main types of machine learning algorithms: unsupervised, supervised and reinforcement learning. We will focus on the first two and share with you real examples of how these algorithms can benefit your product.

The discussion about supervised and unsupervised learning can sometimes become complex and technical. Essentially, supervised learning is about predicting an outcome, while unsupervised learning is about identifying a pattern. Both can help you offer better products to your customers by understanding them better.

Unsupervised learning allows you to capture your customers' preferences and use data to anticipate future needs and behaviors. The most common examples of unsupervised learning are Amazon's recommendations based on what other customers have also bought, as well as Spotify's suggestions for their playlist based on songs they have already liked or added. To create this type of recommendation, data scientists solve a grouping problem by grouping similar users to form homogeneous groups.

Supervised learning is used to predict customer behavior. By solving a classification problem, machine learning engineers can help you identify satisfied and dissatisfied customers and predict rotation. By solving a recommendation problem, data scientists try to guess the things that their customers might be interested in. By solving a classification problem, data scientists help users find the right thing faster when searching.

Supervised learning is also used to enable features such as facial recognition, image classification and voice recognition. These features revolutionize the customer experience and make technology products more intuitive, such as telling virtual assistants to schedule a meeting instead of accessing the programming software to find a time, create an event and enter details.

Importance of data science to make better decisions

With data science and predictive analysis in particular, you can predict useful metrics and trends for your business. This approach can help you improve your ability to serve your customers or compete in the market. The importance of data science and predictive analysis, that is, in the financial sector, is that organizations can harness the power of technology to detect what can negatively affect their business before these problems occur or spread.

Predictive analysis is not a new field, but where and how it can be applied has grown thanks to recent advances in technology. Today, predictive analysis is about connecting disparate systems and datasets to analyze and obtain valuable information from seemingly chaotic data.

Solutions based on advanced analysis have great potential to reduce costs arising from failures, bottlenecks, customer rotation, etc. A good example of predictive analysis in action is the detection of anomalies by Anodot for IoT. Using machine learning algorithms, the Anadot analysis platform keeps the machines running smoothly, signaling data anomalies. If a machine begins to show signs that it needs maintenance or repair, the algorithms can understand this with minor changes in the sensor data. This proactive maintenance can keep support costs and customer satisfaction low.

Advanced analysis introduces the ability to harness the power of multiple data sets and discover connections where they have not been found before. A good example of this is when the New York City government in 2016 was trying to reduce costs related to lawsuits against the city. By collecting data from all departments and applying advanced analyzes, the city found correlations that were not obvious to human eyes. One was that the number of tree-related accidents increased after a large budget cut was introduced in the Parks and Recreations department.

As cumulative data increases (IBM predicted a 42 percent increase by 2020), advanced analyzes will become the norm rather than a way to gain a competitive advantage.

Using data science to automate processes

Automation is one of the most popular trends in modern technology. So let's analyze the applications of data science in business to create automated innovations.

Introduction to R

  • What is R?
  • Why R?
  • Installing R
  • R environment
  • How to get help in R
  • R Studio Overview

The understanding R data structure

  • Variables in R
  • Scalars
  • Vectors
  • Matrices
  • List
  • Data frames
  • Cbind, Rbind, attach and detach functions in R
  • Factors
  • Getting a subset of Data
  • Missing values
  • Converting between vector types

Importing data

  • Reading Tabular Data files
  • Reading CSV files
  • Importing data from excel
  • Loading and storing data with a clipboard
  • Accessing database
  • Saving in R data
  • Loading R data objects
  • Writing data to file
  • Writing text and output from analyses to file

Manipulating Data

  • Selecting rows/observations
  • Rounding Number
  • Creating a string from a variable
  • Search and Replace a string or Number
  • Selecting columns/fields
  • Merging data
  • Relabeling the column names
  • Data sorting
  • Data aggregation
  • Finding and removing duplicate records

Using functions in R

  • Apply Function Family
  • Commonly used Mathematical Functions
  • Commonly used Summary Functions
  • Commonly used String Functions
  • User defined functions
  • local and global variable
  • Working with dates

R Programming

  • While loop
  • If loop
  • For loop
  • Arithmetic operations

Charts and Plots

  • Box plot
  • Histogram
  • Pie graph
  • Line chart
  • Scatterplot
  • Developing graphs
  • Cover all the current trending packages for Graphs

Machine Learning Algorithm:

  • Sentiment analysis with Machine learning
  • C 5.0
  • Support vector Machines
  • K Means
  • Random Forest
  • Naïve Bayes algorithm

Statistics:

  • Correlation
  • Linear Regression
  • Non-Linear Regression
  • Predictive time series forecasting
  • K means clustering
  • P value
  • Find outlier
  • Neural Network
  • Error Measure

Leading Topics:

  • Overture of R Shiny
  • What is Hadoop
  • Integration of Hadoop in R
  • Data Mining using R
  • Clinical research preface in R
  • API in R (Twitter and Facebook)
  • Word Cloud in R

 

You will be going through detailed 2 to 3 months of Data Science Hands-on training


  • Detailed instructor led sessions to help you become a proficient Expert in Data Science.
  • Build a Data Science professional portfolio by working on hands on assignments and projects.
  • Personalised mentorship from professionals working in leading companies.
  • Lifetime access to downloadable Data Science course materials, interview questions and project resources.
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Enroll for the Best Data Science Training with Certification and become a Cloud Expert

To know more details about the Data Science Course and its services, Real-time projects and placements, Ring us ✆ +91-7358655420

Data Science Training in Chennai

Placement Point Solutions provides an affordable Data Science Training in Chennai which provides ultimate support to the candidates. Like wise, our Data Science course content covers all the topics from basics like networking and could computing introduction to advanced topics.  Such as cloud front, elastic cache, and also cloud formation. Most importantly,  this unique hands-on training program helps the candidates to be highly qualified Data Science administrators in this industry. Also Minimum Data Science training cost in Chennai while comparing others since providing the best training in the city.

In addition, Cloud Computing training in Chennai with Data Science Certification is much needed now because of its market growth. The survey says the worldwide market growth of Cloud computing increasing at an average of 30%. In that “Data Science” is the best and most used cloud service platform.

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Data Science Certified Solutions Architect – Associate Exam Details

Also review the McKinsey Report on the ten IT-enabled business trends for the early decade, which talks about three other technologies that shape today’s business world, namely, the cloud, automated knowledge work and the platform. mobile These three trends have had the biggest impact on modern digital businesses in recent years.

We are also providing assistance to clear all the levels of Data Science certification exams on successfully completing the Data Science course in our institute. To know more about the training and certification please feel free to reach us via  +91-7358655420

Placement Assistance after completing Data Science course in Chennai

We are providing the complete placement assistance for each and every candidate according to their need. Our placement approach will be unique and professional which is handled down by a separate team of experienced professionals. The team will guide you in,

  • Resume Building
  • Mock Interview by Professionals
  • Updating the frequently asked interview questions & answers
  • Helping you to Complete Data Science Certifications.
  • Regular Assignments & Assessment to examine the candidate’s knowledge
  • Frequent update about the latest job opportunities available in and around Chennai

Data Science Training with Placement in Chennai

The superposition of the physical and digital world acquires a new meaning in the context of these three technologies. This report also indicates that social platforms make a powerful contribution to the digital business. The data collected from the combined social channels makes a significant difference in the production of the business.

Data Science Online Training India

It is not out of context to mention Gartner’s version of 5 Trends in Cyber   Security for 2017 and 2018, which is a strong case for security concerns in cloud environments. This article becomes more important as global companies look for serverless computing, a change in location to the management of hosted data.

Highlights of Data Science Training by Placement Point Solutions:

  • First of all our, Data Science certification course will be handled Data Science certified & experienced professionals.
  • Secondly, Placement Point Solutions ranked as the No.1 Data Science training institute in Chennai due to covering most Data Science Services and specially designed Data Science course content.
  • Most Importantly, real-time project scenarios & case studies will be shared during the Data Science certification course.
  • Further, Providing placement assistance & Data Science certification guidance to every individual candidate.
  • Similarly, You will learn all the significant and most used Data Science during the session.
  • We offer mock interviews, job updates and also regular trending updates on Data Science & Cloud Computing.
  • Likewise, Providing the best Data Science training and certification in affordable course fees.
  • In addition, Data Science training program available over online and classroom as weekend & weekday sessions.

Ranked as Best Data Science Training in Chennai

  • Firstly, Placement Point Solutions ranked as the No.1 Data Science Training institute in Chennai. We have successfully trained 1500+ IT professionals.
  • Detailed Course Plan to help both Fresher & Experienced Professionals, detailing on 20+ services.
  • In addition, proividing the Real Time Practical oriented Data Science Training center in chennai Velachery.
  • Likewise, Skilled & expert trainers with 10+ years of IT industry experience. Most Importantly Data Science Certification Course is handled by Data Science Professional Level Certified Trainers
  • In addition, flexible Data Science training program in Weekdays, Weekends and Online as well.
  • On other hand, Complete Hands-on Data Science training in Chennai with 100% Placement Assistance.
  • Specially designed Data Science Certification course content which covers more than 20+ services.
  • Moreover, in reality providing the best Data Science training and certification in chennai with minimum Data Science course fees in Chennai.
    The most importantly, Suggested by more than 2000+ amazon certified candidates as Data Science authorized training partner in Chennai.
  • Moreover, to cater to any special needs of the candidates, we offer Online Data Science Training program as well.
  • On the other hand, Professional teams to assist with Career guidance, Interview preparation and Mock Interviews. Also Placement Counselling, Data Science Certification Assistance, Resume writing and Job updates.
  • Further, Our unique Data Science Course syllabus suits for both Freshers and Experienced Professionals.
  • In conclusion, ranked as Best Data Science Training Institute in Chennai providing best Data Science training in the city with nominal course fees. Above all, attend the 1st Data Science session for free !!

Ranked as Best Data Science Training in Chennai

In statistics and machine learning, one of the most common tasks is to adapt a model to a set of training data so that you can make reliable predictions about general untrained data.
In overadaptation, a statistical model describes the random error or noise instead of the underlying relationship. Overfitting happens if a model is extremely complex, like  having many more parameters in relation to the number of observations. A model that has been adjusted too much has poor predictive performance because it reacts to small fluctuations in training data.
The mismatch occurs when a statistical model or machine learning algorithm cannot capture the underlying trend of the data. Insufficient adjustment would occur, for example, by adjusting a linear model to non-linear data. This model would also have low predictive performance.

Machine learning can free up resources by retrieving, generating or processing content automatically. It is increasingly important in the era of large information repositories where the data they contain does not have a natural order.

 

For example, brand managers analyze large collections of images and publications on social networks every day, trying to discover how, when and where people use their product, and how their customers feel about the brand. A social network analysis company Brandwatch uses machine learning to automate the process of image detection and analysis. The Image Insight product helps you collect and analyze more images that contain your brand. This saves valuable time from the best people where it really matters.

Use of the term Data Science is increasingly commonbut what does it exactly mean? What skills do you need to become Data Scientist? What is the difference between BI and Data Science? How are decisions and predictions made in Data Science? These are some of the questions that will be answered further.

In a world that is increasingly becoming a digital space, organizations deal with zettabytes and yottabytes of structured and unstructured data every day. Evolving technologies have enabled cost savings and smarter storage spaces to store critical data.

In this section of the ‘What is Data Science?’ blog, we will look at how top industry players like Google, Amazon, and Visa are using Data Science. IT organizations need to address their complex and expanding data environments in order to identify new value sources, exploit opportunities, and grow or optimize themselves, efficiently. Here, the deciding factor for an organization is ‘what value they extract from their data repository using analytics and how well they present it’. Below, we list some of the biggest and best companies that are hiring Data Scientists at top-notch salaries.

  • For a better understanding of ‘What is Data Science?’, let’s explore its life cycle. Suppose, Mr. X is the owner of a retail store and his goal is to improve the sales of his store by identifying the drivers of sales. To accomplish the goal, he needs to answer the following questions:

    • Which are the most profitable products in the store?
    • How are the in-store promotions working?
    • Are the product placements effectively deployed?

    His primary aim is to answer these questions which would surely influence the outcome of the project. Hence, he appoints you as a Data Scientist. Let’s solve this problem using the Data Science life cycle

Linear regression helps to understand the linear relationship between dependent and independent variables.
Linear regression is a supervised learning algorithm that helps to find the linear relationship between two variables. One is the predictor or independent variable and the other is the response or dependent variable. In linear regression, we try to understand how the dependent variable changes with the independent variable.
If there is more than one independent variable, it is called simple linear regression and if there is more than one independent variable, it is known as multiple linear regression.

Assessments – Our Sensitivity is commonly used to validate the accuracy of a classifier (Logistics, SVM, Random Forest, etc.).. The true events here are the events that were true and the model also predicted them as true.
The calculation of seasonality is quite simple.
Seasonality = (True positives) / (Positive in real dependent variable)aining pattern includes conducting frequent assessments to understand your technical competence & brief your areas of improvement, during the tenure of the course.

Placement Point Solutions offers Class room training, online training and Corporate Training. The training will be provided by expert trainers having more than 10+years IT experience currently working in the Industry.Book Your Free Demo Session: +91-7358655420

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