Our comprehensive Python Programming program is designed to build strong Python skills from the ground up and guide you into real career paths in Data Science, Cyber Security, Automation Engineering, and Python Development. Whether you are an absolute beginner or looking to specialize, this 6-month track covers fundamentals through advanced topics with 3 hours live class and 2 hours exercise/lab per session, every Saturday and Sunday.

You will master Python fundamentals (variables, loops, functions, OOP, files, exceptions), intermediate skills (APIs, JSON, CSV, SQLite, Git/GitHub, debugging, automation), then specialize in Data Science (NumPy, Pandas, Matplotlib, ML concepts, Scikit-learn) and Cyber Security (log analysis, networking basics, security scripting, regex, hashing). Each month includes hands-on labs and step-by-step guidance for non-technical students.

By the end of the program you will complete multiple real-world projects, build a GitHub portfolio, and be prepared for junior roles as Python Developer, Data Analyst, Automation Engineer, Security Analyst, or QA Automation Engineer with Python skills.

Duration: 6 Months (24 Weeks) · 48 Sessions · 240 Hours (5 hours per session: 3h live + 2h lab). Recommended tools: Python 3, VS Code, Jupyter Notebook, Google Colab, Git and GitHub.

Python is one of the most in-demand languages for data science, cybersecurity, and automation. Skilled Python developers and data analysts are essential across fintech, healthcare, and technology industries, with strong earning potential and remote work opportunities.

Our 6-month program prepares you for immediate entry into junior roles with hands-on projects and a GitHub portfolio. Graduates can target positions as Junior Python Developer, Data Analyst, Security Analyst, and QA Automation Engineer, with strong long-term career growth into senior and lead roles.

Whether you are switching careers, advancing in your current role, or building a foundation for data science or cybersecurity, this program provides the structure and support needed for success.

Learning Path Overview

Month 1: Python Foundations

• •Introduction to Python: What is Python, why Python for career growth, installation, VS Code / Jupyter setup, first program, syntax, comments, print
• •Variables & Data Types: Variables, int, float, string, boolean, type casting, user input, naming conventions
• •Operators & Conditionals: Arithmetic, comparison, logical operators; if, elif, else; nested conditions
• •Loops & Strings: for, while, range(), break, continue; string basics, indexing, slicing, methods, f-strings
• •Lists, Tuples, Dictionaries & Sets: Creation, methods, use cases; key-value structures; removing duplicates

Month 2: Structured Python Programming

• •Functions: Defining functions, parameters, return values, scope; default/keyword arguments, *args, **kwargs, lambda
• •Modules & File Handling: Importing built-in and custom modules; read, write, append files; file paths
• •Exception Handling: try, except, else, finally; raise exceptions; safe coding
• •Object-Oriented Programming: Classes, objects, __init__, inheritance, encapsulation, polymorphism; Mini Project 1 (e.g. Student/Inventory/Library system)

Month 3: Intermediate Python + Career Tools

• •JSON & CSV: Reading/writing CSV and JSON; data conversion; parsing structured data
• •APIs with Python: requests library, GET requests, parsing JSON responses
• •SQLite & Git/GitHub: Database basics; create tables, CRUD; Git repo, commit, push, pull; portfolio
• •Debugging, Testing & Automation: Debugging strategies, unittest, assertions; os, shutil; file/folder automation; Mini Project 2 (e.g. Expense tracker, To-do app, Contact manager)

Month 4: Data Science Track Foundation

• •Intro to Data Science & NumPy: Workflow, types of data; arrays, array operations, mathematical computation
• •Pandas: Series and DataFrames; load CSV; inspect, select, clean data; sort, filter, groupby; summary statistics
• •Data Visualization & Analysis: Matplotlib (line, bar, histogram); aggregation, correlation, trend analysis
• •ML Concepts & Scikit-learn: Supervised vs unsupervised; features, target; train/test split; fitting a basic model, prediction

Month 5: Cyber Security Track Foundation

• •Cyber Security with Python: Legal/ethical boundaries; security automation; log analysis, pattern search, suspicious events
• •Networking & Security Scripting: IP, ports, protocols, DNS; sockets; file/process checks; security report scripting
• •Regex & Hashing: Pattern matching; extracting IPs, emails, dates; hashing, SHA, file integrity, password security basics
• •API Security & Cyber Mini Project: API data collection, headers/tokens; Log analyzer, File integrity monitor, Password policy checker, or Port checker

Month 6: Advanced Projects + Career Preparation

• •Advanced Python & Clean Code: List/dict comprehensions, generators, iterators, decorators; folder structure, naming, documentation
• •Capstone Planning: Data Science (dataset, analysis plan) and Cyber Security (problem, script design, reporting)
• •Capstone Development: Guided build, testing, polishing, GitHub upload, presentation prep
• •Final Presentation & Career Readiness: Project presentations; resume, LinkedIn, GitHub portfolio; mock interviews; career roadmap

Live Class — 3 Hours

• Hour 1: Theory and concept discussion
• Hour 2: Instructor live coding
• Hour 3: Guided walkthrough and Q&A

Exercise / Lab — 2 Hours

• Hands-on tasks · Practice problems · Pair or individual coding · Project/lab work

Python 3 · VS Code · Jupyter Notebook · Google Colab · Git and GitHub · SQLite · NumPy · Pandas · Matplotlib · requests

After this program you can prepare for roles such as:

• Junior Python Developer · Data Analyst · Python Automation Engineer
• Junior Data Science Intern · Security Analyst · Cyber Security Engineer
• SOC Support Analyst · QA Automation Engineer with Python skills