schedule60 Hours Duration

Python Programming

Build a rock-solid Python foundation purpose-built for data science. From core syntax and data structures to powerful analytics libraries like Pandas, NumPy, and Scikit-learn — gain the practical, hands-on coding skills the industry demands.

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Who Should Apply

Target Audience & Eligibility

This program is ideal for anyone looking to enter the world of data science or strengthen their programming foundation. No prior coding experience is required — just the curiosity to learn and the drive to apply it to real-world problems.

schoolStudents in Stat, Math, Economics, Commerce, BE/BTech, BBA, MBA, or MCA
workFreshers and IT, DW, MIS, or Reporting professionals looking to upskill
trending_upBusiness professionals in Marketing, Finance, HR, or Supply Chain seeking data fluency
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Why this program?

Go beyond just learning syntax. This program is designed around real data science applications — hands-on data wrangling, manipulation, visualization, and model implementation — so you can write efficient Python code and immediately contribute to analytics projects.

  • ✓ Core Python, OOP & Error Handling
  • ✓ Data Science Libraries (NumPy, Pandas, SciPy)
  • ✓ Google Colab & PyCharm IDE Integration
Detailed Syllabus

Course Curriculum

A structured 60-hour path from Python fundamentals to real-world data analytics — designed for aspiring data professionals with no prior coding background.

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Python Fundamentals & OOP

  • • Python Syntax, Variables, Data Types & Operators
  • • Flow Control — If-Else, For Loop & While Loop
  • • Functions, Modules & Exception/Error Handling
  • • Object-Oriented Programming (Classes & Inheritance)
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Data Structures & Libraries

  • • Lists, Tuples, Strings, Sets & Dictionaries
  • • NumPy Arrays & SciPy for Scientific Computing
  • • Pandas — DataFrames, Data Wrangling & Merging
  • • Google Colab & PyCharm IDE Workflow
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Analytics & Visualization

  • • Matplotlib & Seaborn — Histogram, Boxplot, Scatter & Pareto
  • • Plotly for Interactive & Web-ready Visualizations
  • • Scikit-learn Fundamentals — ML Model Implementation
  • • Statistical Analysis & Data-Driven Insights
Practical Application

Real-World Industry Projects

Beginner – Intermediate

Data Wrangling & EDA Pipeline

Use Pandas and NumPy to clean, wrangle, and explore a messy real-world dataset — handling missing values, outliers, type casting, and generating summary statistics.

Intermediate

Sales & Marketing Analytics Report

Analyze a multi-domain sales dataset using Pandas and Seaborn — uncover trends, segment customers, and produce a polished visual analytics report using Matplotlib and Plotly.

Advanced Capstone

End-to-End ML Pipeline in Python

Build a complete machine learning pipeline using Scikit-learn — covering data preprocessing, feature engineering, model training, evaluation, and results visualization using Plotly.