SYLLABUS & MODULES
Module 1: Getting Started with Python
- Introduction to Python programming language
- Setting up Python environment (Anaconda, Jupyter Notebook)
- Basic data types and data structures in Python
- Control flow statements (if, else, loops)
- Functions and modules in Python
Module 2: Data Wrangling with Pandas
- Introduction to Pandas library
- Reading and writing data in different formats (CSV, Excel, SQL)
- Data cleaning and preprocessing techniques
- Handling missing data
- Data manipulation and transformation using Pandas
Module 3: Data Visualization with Matplotlib and Seaborn
- Introduction to data visualization
- Basic plotting techniques with Matplotlib
- Advanced visualization with Seaborn
- Creating interactive visualizations with Plotly
- Best practices for effective data visualization
Module 4: Exploratory Data Analysis (EDA)
- Understanding the importance of EDA
- Descriptive statistics and summary metrics
- Distribution analysis and hypothesis testing
- Correlation and causation analysis
- Practical exercises on real-world datasets
Module 5: Machine Learning Fundamentals
- Introduction to machine learning concepts
- Supervised vs. unsupervised learning
- Model evaluation techniques
- Regression and classification algorithms
- Hands-on implementation of machine learning models using scikit-learn
Module 6: Advanced Topics in Data Analysis
- Time series analysis and forecasting
- Dimensionality reduction techniques (PCA, t-SNE)
- Text mining and natural language processing (NLP)
- Web scraping for data collection
- Case studies and real-life applications of advanced data analysis techniques
Module 7: Project Work
- Capstone project to apply the skills learned throughout the course
- Participants will work on a real-life dataset to perform end-to-end data analysis
- Guidance and support from instructors for project completion
- Presentation of project findings and insights