STAGE 1: Data Manipulation & Exploratory Data Analysis (Data Preparation & Discovery)

Duration: 35 Hours

Focus: Data structures with Python, data cleaning processes, and exploratory data analysis (EDA).

• Hours 01-10: Python & Data Analytics Foundations: Optimizing Anaconda, Jupyter Notebook, and VS Code environments. Working with multi-dimensional arrays, vector operations, and matrix manipulation using NumPy—the heart of data analysis.

• Hours 11-20: Data Manipulation with Pandas: Understanding DataFrames and Series structures. Loading and filtering data from diverse sources (CSV, Excel, JSON), sorting, and applying smart strategies to handle or clean missing values (NaN).

• Hours 21-30: Exploratory Data Analysis (EDA): Extracting statistical summaries of datasets, detecting and analyzing outliers. Generating basic distribution, line, and bar charts along with correlation heatmaps using Matplotlib and Seaborn.

• Hours 31-35: Hands-on Lab: An end-to-end project dedicated to cleaning and preparing a messy, manipulated real-world e-commerce sales dataset (containing customer demographics, cart amounts, etc.) to make it fully analysis-ready.

STAGE 2: Advanced SQL, Statistical Analysis & BI Tools (Business Intelligence & Relational Data)

Duration: 35 Hours

Focus: Querying relational databases, analytical SQL functions, and corporate dashboard designs.

• Hours 36-45: Advanced SQL for Data Analysis: Executing complex data retrieval scenarios on PostgreSQL/MySQL using Joins, Group By, subqueries, Common Table Expressions (CTEs), and Window Functions (ROW_NUMBER(), LEAD/LAG, RANK).

• Hours 46-55: Database Integration with Python & Statistics: Connecting Python to SQL using SQLAlchemy and psycopg2. Foundations of descriptive and inferential statistics in data analysis, hypothesis testing (A/B Testing logic), p-value concepts, and correlation analyses.

• Hours 56-65: Business Intelligence (BI) & Power BI / Tableau: Importing cleaned data into BI tools, data modeling (Star Schema), writing DAX/Calculated Fields formulas, and designing interactive, dynamic Executive Dashboards.

• Hours 66-70: Project: Fetching data from a company’s live SQL database into Python to perform statistical analysis, and transforming the insights into a real-time updating revenue/performance dashboard on Power BI or Tableau.

STAGE 3: Advanced Analytics, Predictive Modeling & Big Data (Advanced Analytics & Forecasting)

Duration: 30 Hours

Focus: Forward-looking forecasting (Machine Learning fundamentals), Big Data tools, and cloud integration.

• Hours 71-75: Predictive Analytics: Introduction to the Scikit-Learn library. Predicting continuous values using Linear Regression (e.g., forecasting next month’s sales revenue) and building Classification models via Logistic Regression and Decision Trees.

• Hours 76-80: Time Series Analysis: Working with timestamped data, conducting trend and seasonality analysis, and utilizing core time series models for financial data or inventory forecasting.

• Hours 81-85: Introduction to Big Data (PySpark): Understanding the PySpark architecture to work with millions of rows of Big Data efficiently, alongside data processing and manipulation techniques in distributed systems.

• Hours 86-90: Cloud Data Analytics: An overview of data analytics tools on AWS (Amazon Web Services) or Google Cloud platforms. Cloud-based data storage (S3/BigQuery) and data analysis workflows.

• Hours 91-100: Final Capstone (Large-Scale Data Analytics & Forecasting): A massive portfolio project involving the processing of multi-source data (SQL + Cloud Storage) from a large finance or tech corporation using PySpark and Pandas. The project includes building a Scikit-Learn model to predict user churn or financial risk, and compiling all insights into a professional BI report and presentation deck.

📊 Curriculum and Investment Value Summary