DATA SCIENCE WITH PYTHON AND R

1. Introduction to Data Science

• Overview of Data Science
• Roles and Responsibilities of a Data Scientist
• Tools and Technologies Used in Data Science

2. Data Handling and Manipulation

• Python
• Introduction to Python for Data Science
• Data Structures in Python (Lists, Dictionaries, Tuples, Sets)
• Data Manipulation with Pandas
• Data Cleaning and Preparation
• Data Visualization with Matplotlib and Seaborn
• R
• Introduction to R for Data Science
• Data Structures in R (Vectors, Matrices, Data Frames)
• Data Manipulation with dplyr and tidyr
• Data Cleaning and Preparation in R
• Data Visualization with ggplot2

3. Statistical Analysis and Modeling

• Descriptive Statistics
• Measures of Central Tendency
• Measures of Dispersion
• Correlation and Covariance
• Inferential Statistics
• Hypothesis Testing
• Confidence Intervals
• ANOVA and Chi-Square Tests
• Regression Analysis
• Simple Linear Regression
• Multiple Linear Regression
• Logistic Regression
• Time Series Analysis
• Time Series Decomposition
• ARIMA Modeling
• Forecasting Techniques

4. Machine Learning

• Python
• Supervised Learning Algorithms (Linear Regression, Decision Trees, Random Forests)
• Unsupervised Learning Algorithms (K-Means, PCA)
• Model Evaluation and Tuning with Scikit-Learn
• R
• Supervised Learning Algorithms (Linear Regression, Decision Trees, Random Forests)
• Unsupervised Learning Algorithms (K-Means, PCA)
• Model Evaluation and Tuning in R

5. Data Science Project Lifecycle

• Problem Definition and Data Collection
• Data Exploration and Visualization
• Feature Engineering
• Model Building and Evaluation
• Model Deployment

6. Big Data and Cloud Computing

• Introduction to Big Data and Hadoop
• Data Processing with Spark
• Cloud Computing Basics (AWS, Google Cloud, Azure)
• Data Science on Cloud Platforms

7. Project Work

• End-to-End Data Science Project with Python
• End-to-End Data Science Project with R
• Model Deployment and Monitoring

8. Soft Skills and Interview Preparation

• Problem-Solving Techniques
• System Design Concepts
• Coding Practice with Data Structures and Algorithms
• Mock Interviews and Resume Building

9. Optional Topics

• Deep Learning with TensorFlow and Keras
• Natural Language Processing (NLP)
• Reinforcement Learning
• Data Ethics and Privacy