Data Science Foundations to Advanced Applications Program

Module 1: Descriptive & Diagnostic Analysis of Data using Python

Duration: [X sessions/weeks — fill in]

This module builds a strong foundation in data analysis using Python, taking learners from core programming concepts to statistical modeling techniques used in real-world data science.

1. Python Refresher
Quick recap of Python essentials — data types, control flow, functions, and object-oriented basics — to ensure every learner is comfortable before diving into data-specific tools.

2. Data Handling Libraries — NumPy & Pandas

• NumPy: arrays, vectorized operations, broadcasting, and numerical computing fundamentals
• Pandas: DataFrames, data cleaning, transformation, merging, grouping, and exploratory data analysis (EDA)

3. Statistics for Data Science
Core statistical concepts essential for data-driven decision making — descriptive statistics, probability distributions, hypothesis testing, and correlation analysis.

4. SciPy for Statistical Computing
Applying SciPy for statistical tests, distributions, and optimization functions that support deeper diagnostic analysis.

5. Diagnostic Analytics Models

• ARIMA — time series analysis and forecasting fundamentals
• ANOVA — comparing group means and testing variance across datasets
• Additional diagnostic techniques to identify why patterns occur in data, not just what the data shows

Outcome: By the end of this module, learners will be able to independently clean, explore, analyze, and statistically test real-world datasets using Python — setting the stage for predictive modeling (Part 2) and advanced applications (Part 3).