Training Program

Data Science using Python

Move from spreadsheet-heavy work to practical Python analytics through a focused 3-day program covering Python foundations, pandas mastery, and business-ready data analysis and visualization.

Duration

3 Days

Overview

This 3-day practical program helps participants use Python for real-world data analysis, reporting, visualization, and insight generation. It is designed for Excel users who want a more powerful, repeatable, and scalable way to work with data.

Introduction

Python has become one of the most practical languages for data analysis because it allows professionals to move beyond repetitive spreadsheet work into faster, cleaner, and more scalable analytics workflows. This program is designed for learners who want to use Python as a serious data analysis tool, especially those coming from an Excel background. Participants will build confidence with Python syntax, work deeply with pandas Series and DataFrames, and apply analytical techniques that help transform raw data into business insight, productivity improvements, and clearer decision support.

Topics Covered

Introduction to Python for Data Work
Python Syntax, Variables, and Data Types
Control Structures and Program Flow
Python Data Structures for Analysis
Functions, Modules, and Reusable Analysis Logic
NumPy Foundations for Numerical Data
Introduction to pandas and Why It Matters
pandas Series Fundamentals
DataFrame Creation and Structure
Selecting, Filtering, and Transforming DataFrames
Data Cleaning with pandas
Aggregation, Grouping, and Reshaping
Working Beyond Excel with pandas
Exploratory Data Analysis for Business Decisions
Numerical Data Analysis
Text Data Analysis and Sentiment Analysis
Data Visualization with Matplotlib and Seaborn
Turning Analysis into Productivity and Business Improvement

Audience Profile

Excel users, business analysts, aspiring data analysts, operations staff, reporting professionals, and anyone who wants to perform stronger data analysis using Python.

Prerequisites

There are no formal prerequisites required to attend this course, although basic computer literacy and familiarity with spreadsheet-based work will be helpful.

Day 1 - Python Language Foundations for Data Analysis

1. Introduction to Python for Data Work

Python in data analysis and reporting
Python vs spreadsheet workflows
Notebooks, scripts, and environments
Python workflow for data professionals
Setting up the analysis environment

2. Python Syntax, Variables, and Data Types

Python syntax basics
Variables and naming rules
Numbers, strings, and booleans
Type conversion
Comments and readable code style

3. Control Structures and Program Flow

if, elif, and else
for loops and while loops
Looping through collections
break and continue
Simple control flow patterns

4. Python Data Structures for Analysis

Lists and tuples
Dictionaries
Sets and uniqueness checks
Indexing and slicing
Choosing the right structure

5. Functions, Modules, and Reusable Analysis Logic

Defining and calling functions
Parameters and return values
Scope and variable visibility
Importing modules and libraries
Reusable analysis functions

6. NumPy Foundations for Numerical Data

Why NumPy matters
Creating arrays
Vectorized operations
Array indexing and slicing
Basic numerical calculations

Lab 1: Python and NumPy Practice for Data Work

Variables, loops, and functions
Lists and dictionaries
NumPy calculations
Preparation for pandas work

Day 2 - Mastering pandas Series and DataFrames

8. Introduction to pandas and Why It Matters

What pandas is
Why pandas beats manual spreadsheet work
Series and DataFrame concepts
Business use cases for pandas
pandas for reporting productivity

9. pandas Series Fundamentals

Creating a Series
Series indexing and selection
Filtering Series data
Applying functions to a Series
Missing values in a Series

10. DataFrame Creation and Structure

Creating DataFrames
Rows, columns, index, and data types
Shape, columns, and metadata
Reading CSV and Excel-style data
Saving DataFrames

11. Selecting, Filtering, and Transforming DataFrames

Column and subset selection
loc and iloc
Conditional filtering
Sorting and resetting indexes
Calculated columns and transformations

12. Data Cleaning with pandas

Nulls, blanks, and inconsistent values
Handling missing data
Duplicates and formatting issues
Renaming and standardizing columns
Data type conversion

13. Aggregation, Grouping, and Reshaping

groupby summaries
sum, mean, count, min, and max
Value counts and frequencies
Pivot-style analysis
Merging and joining datasets

14. Working Beyond Excel with pandas

Spreadsheet formulas vs pandas operations
Automating repetitive report tasks
Handling larger datasets
Repeatable workflows
Reducing human error

Lab 2: Business Reporting with pandas

Load CSV business data
Clean and standardize the dataset
Filter, sort, group, and summarize
Create a reusable reporting workflow

Day 3 - Data Analysis, Visualization, and Business Insight

16. Exploratory Data Analysis for Business Decisions

Patterns, distributions, and anomalies
Trends, gaps, and outliers
Descriptive statistics
Summaries for deeper analysis
Turning observations into questions

17. Numerical Data Analysis

Totals, averages, and ratios
Category and segment comparisons
Top and low performers
Practical correlation ideas
Interpreting numeric findings

18. Text Data Analysis and Sentiment Analysis

Text analysis in business
Cleaning text data
Word frequency and keyword patterns
Basic sentiment analysis workflow
Customer feedback insights

19. Data Visualization with Matplotlib and Seaborn

Choosing the right chart
Bar, line, scatter, and histogram charts
Seaborn basics
Trends, comparisons, and distributions
Business presentation visuals

20. Turning Analysis into Productivity and Business Improvement

Improving reporting speed and quality
Finding inefficiencies and bottlenecks
Supporting business decisions
Repeatable analysis for productivity
Data-driven confidence

Lab 3: Applied Data Analysis Project

Prepare and clean a business dataset
Analyze numeric and text data
Create charts for findings
Present business improvement insights