Program Overview

This 20-hour program introduces Python programming with a strong focus on data analytics: core language fundamentals, NumPy and Pandas for data manipulation, Matplotlib/Seaborn for visualization, and an introduction to automation and basic machine learning concepts using scikit-learn.

Target Audience

Analysts, professionals, and career-switchers with little to no programming background who want practical, job-ready Python data skills.

Prerequisites

Comfort with basic computer operations and spreadsheets. No prior coding experience required.

Detailed Syllabus

Unit 1: Python Fundamentals (5 hrs)

Setting up the Python environment (Anaconda/Jupyter/VS Code)
Variables, data types, and operators
Control flow: loops and conditionals
Functions and modules
Data structures: lists, tuples, dictionaries, sets

Unit 2: File Handling & Error Management (1 hr)

Reading/writing files and exception handling

Unit 3: Data Analysis with NumPy & Pandas (5 hrs)

NumPy arrays and vectorized operations
Pandas Series and DataFrames
Data cleaning, filtering, and transformation
Grouping, merging, and pivoting data

Unit 4: Data Visualization (2 hrs)

Matplotlib fundamentals
Statistical visualization with Seaborn

Unit 5: Working with External Data (2 hrs)

Introduction to APIs and web scraping basics
Reading and writing Excel/CSV files programmatically

Unit 6: Introduction to AI/ML & Automation (3 hrs)

Overview of AI/ML concepts and the scikit-learn workflow
Building a simple predictive model
Automation scripting for repetitive analytical tasks

Unit 7: Capstone Project (2 hrs)

End-to-end data analysis project build and presentation

Hour-Wise Breakdown (20 Hours)

Hour	Topic	Content Covered	Hands-On Activity
1	Python Fundamentals	Environment setup: Anaconda, Jupyter, VS Code	Install environment and run first Python script
2	Python Fundamentals	Variables, data types, operators	Write scripts using variables and arithmetic/logical operators
3	Python Fundamentals	Control flow: loops & conditionals	Build a grade-calculator using loops and conditionals
4	Python Fundamentals	Functions & modules	Refactor prior scripts into reusable functions
5	Python Fundamentals	Data structures: lists, tuples, dicts, sets	Build an inventory tracker using dictionaries and lists
6	File Handling	File I/O & exception handling	Read/write a CSV file with error handling
7	NumPy	Introduction to NumPy arrays	Perform vectorized calculations on a numeric dataset
8	NumPy	NumPy operations: indexing, reshaping, statistics	Compute summary statistics on an array dataset
9	Pandas	Introduction to Pandas Series & DataFrames	Load and explore a dataset with Pandas
10	Pandas	Data cleaning & manipulation	Clean a messy dataset (missing values, duplicates, types)
11	Pandas	Grouping, merging & pivoting	Build grouped summaries and merge two datasets
12	Pandas	Advanced Pandas: apply, lambda, time series basics	Analyze a time-series sales dataset
13	Visualization	Matplotlib fundamentals	Create line, bar, and scatter plots from a dataset
14	Visualization	Statistical visualization with Seaborn	Build a correlation heatmap and distribution plots
15	External Data	Introduction to APIs & basic web scraping	Pull data from a public API into a DataFrame
16	External Data	Reading/writing Excel & CSV files programmatically	Automate an Excel report generation script
17	AI/ML Introduction	AI/ML concepts & scikit-learn workflow	Explore a scikit-learn dataset and workflow
18	AI/ML Introduction	Building a simple predictive model	Train and evaluate a basic regression/classification model
19	Capstone Project	Applied project work session	Build an end-to-end data analysis pipeline on a business dataset
20	Capstone Project	Project presentation & wrap-up	Present the analysis/model and receive feedback

Learning Objectives

By the end of this module, participants will be able to:

Write clean, structured Python code using core language constructs
Manipulate and clean real-world datasets using NumPy and Pandas
Create clear, informative visualizations with Matplotlib and Seaborn
Pull data from external sources (APIs, files) into Python workflows
Understand foundational AI/ML concepts and build a simple predictive model
Automate repetitive data tasks with Python scripts

Practical Exercises

Building small programs using loops, functions, and data structures
Cleaning a messy real-world dataset with Pandas
Creating grouped summaries and merged datasets from multiple sources
Building visualizations to communicate trends and distributions
Training and evaluating a basic scikit-learn predictive model

Real-World Projects

Capstone: End-to-end data analysis pipeline — ingest, clean, analyze, visualize, and summarize a real-world business dataset, with an optional predictive model.
Mini-project: Automated Excel/CSV reporting script that ingests raw data and outputs a formatted summary report.

Assessment Plan

Component	Weight	Description
Formative quizzes (end of each unit)	20%	Short quizzes on syntax, Pandas, and ML concepts
Practical exercises	30%	Hands-on coding tasks completed during each session
Capstone project	40%	End-to-end analysis pipeline build and presentation
Participation & engagement	10%	In-class contribution and peer feedback

Tools Required

Python 3.x (Anaconda distribution recommended)
Jupyter Notebook or VS Code
Key libraries: NumPy, Pandas, Matplotlib, Seaborn, scikit-learn, requests
Sample datasets (provided): sales, HR, and public API data

Expected Outcomes

Write functional Python code to solve analytical problems
Confidently clean, transform, and analyze datasets with Pandas
Produce clear visualizations to support data-driven decisions
Build and evaluate a simple predictive model
Automate recurring data-processing tasks, saving manual effort

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