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Data Science With Python
Upgrade your coding skills with Placement Point Solutions’ Best-Selling Data science Course with Python!
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Why Study Data Science With Python??
Studying data science with Python is beneficial because Python is
easy to learn, has powerful libraries (like Pandas, NumPy, and Scikitlearn), and
is
widely used
in
the
industry
for
data
analysis,
machine
learning, and
automation.
Benefits of Learning Data Science With Python?
✨ Easy to Learn – Simple syntax and beginner-friendly.
✨ Powerful Libraries – Pandas, NumPy, Scikit-learn, etc.
✨ Versatile – Used in data analysis, ML, and automation.
✨ High Demand – Strong career opportunities.
✨ Great Community Support – Help available worldwide.
✨ Scalable & Efficient – Handles big data effectively.
✨ Seamless Integration – Works with various technologies.
Future Scope of Data Science With Python?
✨ Growing Demand – High demand across industries like healthcare,finance, and tech.
✨ AI & Machine Learning – Python is key for advancements in AI, ML,and deep learning.
✨ Big Data Analytics – Essential for handling and analyzing massive datasets.
✨ Automation & AI Integration – Expanding in process automation and smart systems.
✨ Cloud & IoT Applications – Used in cloud computing, IoT, and realtime analytics.
✨ Business & Decision Science – Crucial for data-driven decisionmaking.
✨ Continuous Innovation – Python’s evolving ecosystem ensureslong-term relevance.
Kickstart Your Career with Data Science With Python
With its endless applications and growing demand, Data Science
with Python is the key to a successful tech career. Enroll in a course
today and step into a world of innovation and opportunity!
key features
Course Duration : 40 hours
Intensive, focused training designed to quickly upskill you in a short timeframe.
100% Job-Oriented Training
Curriculum tailored to industry requirements, ensuring you gain the skills employers are looking for.
Industry Expert Faculties
Learn from seasoned professionals with real-world experience.
Flexible Demo Class Available
Opportunity to attend a demo session and experience the training style before enrolling.
Completed 400+ Batches
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Certification Guidance
Support throughout your journey to help you earn relevant certifications that boost your career.
Live Practical Sessions (if needed)
Hands-on, interactive sessions to reinforce learning and apply concepts in real-time.
Peaceful Environment
A conducive learning atmosphere that helps you focus and maximize your learning potential.
Syllabus of Data Science With Python
- What is Data Science?
- Applications of Data Science
- Overview of Python for Data Science
- Installing Python and Setting up Jupyter Notebook
- Python Basics: Data Types, Variables, Operators
- Conditional Statements & Loops
- Functions and Lambda Expressions
- Exception Handling
- Introduction to NumPy: Arrays, Operations, Indexing & Slicing
- Introduction to Pandas: DataFrames, Series, Indexing
- Importing & Exporting Data (CSV, Excel, JSON, SQL)
- Data Cleaning & Preprocessing (Handling Missing Values, Duplicates, Outliers)
- Feature Engineering Basics
- Introduction to Matplotlib: Line, Bar, Scatter, Pie Charts
- Introduction to Seaborn: Histograms, Boxplots, Heatmaps, Pairplots
- Customizing Plots (Titles, Labels, Legends)
- Interactive Visualizations with Plotly
- Understanding Data Distributions
- Measures of Central Tendency & Dispersion (Mean, Median, Mode, Variance, Standard Deviation)
- Correlation & Covariance
- Detecting and Handling Outliers
- Hypothesis Testing
- Overview of Machine Learning (Supervised vs. Unsupervised Learning)
- Introduction to Scikit-Learn
- Train-Test Split & Cross-Validation
- Performance Metrics (Accuracy, Precision, Recall, F1-Score)
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forests
- Support Vector
- Machines (SVM)
- Hyperparameter Tuning
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- Association Rule Mining
- Natural Language Processing (NLP) Basics
- Introduction to Deep Learning with TensorFlow & Keras
- Time Series Analysis (ARIMA, LSTMs)
- Recommender Systems
- Introduction to Flask/Django for Model Deployment
- Saving & Loading Models with Pickle & Joblib
- Introduction to Big Data (Hadoop, Spark with PySpark)
- Cloud Deployment (AWS, GCP, Azure)
- End-to-End Data Science Project
- Data Cleaning, EDA, Feature Engineering
- Model Building & Evaluation
- Deployment & Presentation
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