Python + Data Science
The Python + Data Science program is a comprehensive, practical training track engineered to take participants from foundational programming logic into the world of data-driven decision-making.
This program bridges the gap between core software coding and practical data manipulation. Participants will learn how to write clean Python scripts, clean messy data sets, perform exploratory data analysis (EDA), and build visual data dashboards to uncover actionable corporate insights.
This is a structural training program intended to develop operational data manipulation and programming skills. It is an independent program that issues a formal Course Completion Certificate; it is not an official software engineering or implementation consulting track.
Programming Language Course Content
Instructor-Led Conceptual Syntax Breakdowns
Live Coding Exercises and Step-by-Step Script Construction
Real-World File Sorting and Report Automation Assignments
Interactive Bug-Hunting Workshops and Practical Labs
Introduction to Programming & Python Architecture (Anaconda/VS Code/Jupyter)
Core Python Syntax, Data Types, and Dynamic Variable Assignments
Arithmetic, Logical, and Assignment Operators
Controlling Code Flow: If-Else Conditional Blocks and Nested Statements
Outcome: Construct basic logical scripts and execute code in a local IDE.
Definite and Indefinite Iterations: While Loops and For Loops
Mastery of Python Sequences: Lists (Slicing, Methods) and Tuples
Mapping Complex Key-Value Pairs: Dictionaries and Structured Sets
Designing Custom Functions, Keyword Arguments, and Scope (Local vs. Global)
Outcome: Write clean, modular, reusable code profiles that manage relational variables.
Introduction to the NumPy Library: Arrays, Vectors, and Matrix Computations
Loading Messy External Data: Reading and Importing Excel and CSV Data via Pandas
Advanced Data Cleaning: Handling Missing Values, Removing Duplicates, and Dropping Columns
Data Wrangling Operations: Row Filtering, Multi-Criteria Sorting, and Indexing DataFrames
Data Grouping & Aggregation: Computing Averages, Sums, and Key Business Metrics
Outcome: Import, transform, and structurally analy
Principles of Exploratory Data Analysis (EDA) and Statistical Summaries
Introduction to Data Visualization Engineering via Matplotlib and Seaborn Libraries
Creating Business Visuals: Line Graphs, Bar Charts, Histograms, and Scatter Plots
Customizing Visual Outputs: Formatting Labels, Legends, Color Schemes, and Subplots
Capstone Project: Live data analysis of a real multi-channel retail sales dataset to isolate high-performing inventory trends and summarize leadership outcomes.
Outcome: Transform heavy datasets into clean visual narratives for executive review.
Assessment & Completion
Participants will complete:
Core Syntax and Logic Quizzes
Functional Script Development Practical Exercises
Final Python Data Script Evaluation
A Course Completion Certificate will be issued upon successful completion to verify the formal training hours.
