INDEX

• Introduction to Data Science
• Programming for Data Science
• Data Analysis and Visualization
• Statistics and Probability
• Machine Learning Basics
• Advanced Machine Learning
• Natural Language Processing (NLP)
• Big Data Technologies
• Model Deployment and Production

Project

1. Introduction to Data Science

• Overview of Data Science
• Applications of Data Science
• Data Science Process

2. Programming for Data Science

• Python Programming
• Basics of Python
• Data Structures (Lists, Tuples, Dictionaries)
• Functions and Modules
• Libraries: NumPy, Pandas

3. Data Analysis and Visualization

• Data Cleaning and Preprocessing
• Exploratory Data Analysis (EDA)
• Data Visualization Tools
• Matplotlib
• Seaborn

4. Statistics and Probability

• Descriptive Statistics
• Inferential Statistics
• Probability Theory
• Hypothesis Testing

5. Machine Learning Basics

• Supervised Learning
• Linear Regression
• Logistic Regression
• Decision Trees
• Support Vector Machines (SVM)
• Unsupervised Learning
• Clustering (K-Means, Hierarchical)
• Principal Component Analysis (PCA)

6. Advanced Machine Learning

• Ensemble Methods
• Random Forest
• Gradient Boosting
• Neural Networks and Deep Learning
• Introduction to Neural Networks
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)

7. Natural Language Processing (NLP)

• Text Preprocessing
• Sentiment Analysis
• Topic Modeling

8. Big Data Technologies

• Introduction to Big Data
• Spark data processing
• NoSQL Databases

9. Model Deployment and Production

• Model Evaluation and Validation
• Model Deployment Techniques
• Introduction to MLOps

10. Project

• End-to-End Live Data Science Project
• Presentation and Reporting

Apart from this will provide overview of real time use case scenario issues and workflow.