Upcoming Class Details

• Start Date: 9th Feb 2025
• End Date: 28th June 2025
• Timings: 9:00 AM to 11:00 AM
• Days: Sat/Sun

Course Content

1. Introduction to Data Science

• Overview of Data Science
• Importance and Applications
• Data Science vs. Data Analytics vs. Machine Learning
• Data Science Lifecycle: Data Collection, Cleaning, Exploration, Modeling, Deployment, and Maintenance
• Tools and Environment Setup: Python Ecosystem, Jupyter Notebooks, Git & GitHub

2. Data Manipulation with Numpy and Pandas

• NumPy Fundamentals: Arrays, Operations, Indexing, Slicing
• pandas for Data Manipulation: Series, DataFrames, Importing/Exporting Data, Data Cleaning

3. Exploratory Data Analysis (EDA) and Visualization

• Descriptive Statistics
• Data Visualization with matplotlib: Line, Scatter, Bar, Histogram
• Advanced Visualization with seaborn: Heatmaps, Pair Plots
• Interactive Visualizations: Plotly, Bokeh

4. Basic Statistics or Data Analysis

• Probability Theory and Inferential Statistics
• Regression Analysis: Linear and Logistic Regression

5. Introduction to Machine Learning

• Supervised vs. Unsupervised Learning
• Supervised Learning: Regression and Classification

6. Advanced Machine Learning

• Decision Trees, Random Forests, SVM
• Clustering: k-Means, Hierarchical
• Dimensionality Reduction: PCA, t-SNE

7. Model Evaluation and Optimization

• Cross-Validation, Hyperparameter Tuning
• Model Deployment and Monitoring: joblib, pickle

8. Data Engineering and Tooling

• Big Data Tools: Hadoop, Spark, Cloud Data Engineering (AWS, GCP, Azure)

9. Database and Data Storage

• SQL and NoSQL Databases: SQLite, PostgreSQL, MongoDB

10. Web Development for Data Science

• Flask for Web Development: Building APIs, Serving JSON Data

11. Project

• Exam System