Python for Data Analytics

Module 1: Introduction to Python for Analytics Why Python for Data Analytics? Setting up Python environment (Anaconda, Jupyter, VS Code) Python basics recap (variables, data types, operators) Input/Output and basic scripts Practical: Simple data-driven Python script Module 2: Core Python Programming Control structures (if, for, while) Functions and modular programming Data structures: lists, tuples, sets, dictionaries String handling & formatting File handling (CSV, TXT, JSON) Exception handling Practical: Write a script to parse CSV data Module 3: Working with Libraries for Analytics NumPy Arrays, indexing, slicing Vectorized operations Descriptive statistics with NumPy Pandas Series and DataFrames Importing data (CSV, Excel, SQL) Data cleaning & transformation GroupBy, aggregation, merging, joining Practical: Sales dataset analysis with Pandas Module 4: Data Visualization Matplotlib Line, bar, scatter, pie charts Customizing plots (labels, legends, colors) Seaborn Distribution plots, boxplots, heatmaps Pairplots and correlation visualization Practical: Visualizing stock market data Module 5: Exploratory Data Analysis (EDA) Handling missing values Outlier detection & treatment Feature engineering basics Statistical summaries & insights Practical: EDA on a real dataset (e.g., Titanic dataset) Module 6: Working with Databases Introduction to SQL & Python integration Using sqlite3 and SQLAlchemy Querying data with Pandas (read_sql) Practical: Query and analyze customer transactions Module 7: Data Analytics with Python Descriptive analytics (mean, median, mode, variance, correlation) Inferential statistics basics (hypothesis testing, p-value, t-test) Time series basics (resampling, rolling mean, trend analysis) Practical: Monthly sales forecasting basics Module 8: Introduction to Machine Learning for Analytics Scikit-learn overview Supervised learning basics (regression, classification) Train-test split & model evaluation Practical: Predicting customer churn Module 9: Advanced Topics (Optional) Web scraping for data collection (BeautifulSoup, requests) APIs and JSON handling Automation with Python scripts Dashboarding with Plotly & Streamlit Module 10: Capstone Project Learners will apply all skills to a real-world dataset. Example projects: Retail Analytics: Customer purchase behavior analysis Finance Analytics: Stock price trend visualization & prediction Healthcare Analytics: Patient data cleaning and survival analysis Marketing Analytics: Campaign performance analysis Learning Outcomes By the end of this course, learners will be able to: Clean, transform, and analyze datasets using Python Visualize insights with professional plots Perform EDA and basic statistical analysis Integrate Python with databases for analytics Apply Python to solve real-world business problems