Course Title: Advanced Data Analysis with Python

📅 Duration: 4 Weeks (Twice a Week)
🖥 Format: Online Live Sessions + Hands-on Assignments

Week 1: Advanced Data Manipulation & SQL for Data Analysis

📌 Session 1:

• Optimizing DataFrames in Pandas (Efficient Memory Usage, astype())

• Advanced Data Cleaning Techniques (Handling Outliers, Duplicates)

• Working with Large Datasets (dask, polars, vaex)

• Mini Project: Optimizing a Large E-Commerce Dataset

📌 Session 2:

• SQL for Data Analysis (Joins, Subqueries, Window Functions)

• Integrating Pandas with SQL (sqlite3, SQLAlchemy)

• Writing Efficient Queries for Large Databases

• Mini Project: Analyzing Customer Transactions with SQL

Week 2: Time-Series Analysis & Advanced Visualization

📌 Session 3:

• Introduction to Time-Series Analysis

• Working with Time-Series Data in Pandas (resample(), Rolling Windows)

• Decomposing Time-Series (Trend, Seasonality, Noise)

• Mini Project: Forecasting Sales Trends with Time-Series Data

📌 Session 4:

• Advanced Data Visualization Techniques

• Creating Interactive Dashboards (Plotly, Dash, Streamlit)

• Geographic Data Visualization with geopandas & folium

• Mini Project: Building an Interactive Sales Dashboard

Week 3: Statistical Modeling & Machine Learning for Data Analysis

📌 Session 5:

• Introduction to Inferential Statistics & Hypothesis Testing

• Linear & Multiple Regression for Data Analysis

• Feature Engineering & Selection for Predictive Analysis

• Mini Project: Predicting House Prices Using Regression Models

📌 Session 6:

• Introduction to Machine Learning for Data Analysis (scikit-learn)

• Clustering Techniques (K-Means, DBSCAN)

• Anomaly Detection for Fraud Analysis

• Mini Project: Customer Segmentation Using Clustering

Week 4: Big Data, Automation & Final Project

📌 Session 7:

• Handling Big Data with PySpark & Hadoop Basics

• Automating Data Pipelines with Airflow

• Working with APIs & Web Scraping for Data Collection

• Mini Project: Scraping and Analyzing Twitter Sentiments

📌 Session 8:

• Final Project Implementation & Debugging

• Code Optimization & Performance Tuning

• Final Project Showcase & Code Review

• Next Steps: Deep Learning, AI-Driven Analytics, Data Engineering