Training Program

Data Science using Python

Build end-to-end data science capability with Python through a practical 5-day program covering data collection, pandas, analysis, visualization, pipelines, and business-ready reporting outputs.

Duration

5 Days

Overview

This 5-day applied program builds practical Python data science capability across data preparation, pandas, analysis, visualization, data pipelines, and publishing analytical outputs as usable business reports and dashboards.

Introduction

This 5-day Data Science using Python program is designed for learners who want a fuller end-to-end analytics workflow rather than isolated analysis techniques. In addition to Python, pandas, data analysis, and visualization, participants will learn how to collect data from structured sources such as SQL databases, NoSQL platforms, and APIs, automate data movement into a central location, and then turn analytical output into publishable web reports and dashboard-style views. The program combines practical hands-on work with a business-focused mindset so participants can understand how Python supports real reporting, productivity, and decision-making environments.

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
Data Collection Fundamentals
Working with SQL Data Sources
Working with NoSQL Data Sources
Collecting Data from APIs
Building a Simple Data Pipeline
NumPy Foundations for Numerical Data
Introduction to pandas and Why It Matters
pandas Series Fundamentals
DataFrame Creation and Structure
Data Cleaning with pandas
Selecting, Filtering, and Transforming DataFrames
Aggregation, Grouping, and Reshaping
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
Reporting Output Design
Publishing Analytical Results to Web Reports
Dashboard Fundamentals
Building a Simple Python-Driven Reporting View
Final Capstone Project

Audience Profile

Excel users, business analysts, aspiring data analysts, reporting professionals, developers moving into analytics, and teams building practical data workflows with 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

Lab 1: Python Practice for Data Work

Variables, loops, and functions
Lists and dictionaries
Simple reusable scripts
Preparation for data collection work

Day 2 - Data Collection, Integration, and Pipeline Design

6. Data Collection Fundamentals

Structured vs semi-structured data sources
Files, databases, and APIs as data sources
Batch vs scheduled data collection
Centralized data collection concepts
Designing a practical collection workflow

7. Working with SQL Data Sources

SQL databases in analytics workflows
Connecting Python to SQL data sources
Reading query results into Python
Basic filtering and extraction strategies
Moving database records into analysis pipelines

8. Working with NoSQL Data Sources

NoSQL concepts for analysts
Document-style data and nested structures
Reading NoSQL-style records into Python
Flattening records for analysis
Choosing SQL vs NoSQL sources

9. Collecting Data from APIs

API basics for data professionals
Requests, endpoints, and JSON responses
Authentication basics and API keys
Loading API responses into Python
Handling pagination and repeated calls

10. Building a Simple Data Pipeline

Extract, transform, and load concepts
Bringing multiple sources into one location
Automating collection with Python scripts
Preparing pipeline output for pandas
Monitoring and maintaining simple pipelines

Lab 2: Create a Practical Data Collection Pipeline

Read data from files and an API
Load data into a common structure
Standardize collected records
Produce a pipeline-ready dataset

Day 3 - Data Science Foundations with Python

11. NumPy Foundations for Numerical Data

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

12. 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

13. pandas Series Fundamentals

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

14. DataFrame Creation and Structure

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

15. Data Cleaning with pandas

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

Lab 3: Data Science Preparation with pandas

Load collected data into pandas
Clean and standardize the dataset
Prepare analysis-ready structures
Create reusable preparation steps

Day 4 - Data Analytics, Exploration, and Insight Generation

16. Selecting, Filtering, and Transforming DataFrames

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

17. Aggregation, Grouping, and Reshaping

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

18. Exploratory Data Analysis for Business Decisions

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

19. Numerical Data Analysis

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

20. 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

Lab 4: Applied Data Analytics

Analyze numeric and text data
Compare categories and segments
Extract sentiment and keyword patterns
Present analytical findings

Day 5 - Publishing Insights, Web Reports, and Dashboards

21. 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

22. 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

23. Reporting Output Design

From analysis to publishable output
Choosing metrics for stakeholders
Structuring business-friendly reports
Combining text, tables, and charts
Designing repeatable reporting outputs

24. Publishing Analytical Results to Web Reports

Static report concepts
Exporting analysis outputs for the web
HTML-based reporting ideas
Organizing sections and visuals clearly
Sharing report output with business teams

25. Dashboard Fundamentals

What makes a useful dashboard
KPI and metric presentation
Charts, filters, and drill-down thinking
Operational vs management dashboards
Dashboard usability for decision makers

26. Building a Simple Python-Driven Reporting View

Preparing pandas outputs for presentation
Generating chart-ready datasets
Creating simple report pages or dashboard views
Refreshing outputs from updated data
Turning analysis into shareable artifacts

27. Final Capstone Project

Collect data from multiple sources
Prepare the data with Python and pandas
Perform business-focused analysis
Create charts and insights
Publish the results as a report or dashboard-style output