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Course Description

Data is the new oil, and Artificial Intelligence is the engine that drives it. This Python for Data Science & AI course is carefully designed for beginners and professionals who want to enter the world of data analysis, machine learning, and AI applications.

You’ll start with Python basics and progress step by step into data handling, visualization, machine learning, and AI-powered projects. By the end of the course, you’ll be able to work on real-world datasets, build predictive models, and showcase projects in your portfolio.

This course blends theory + hands-on practice + projects, making it the perfect choice for students, job seekers, IT professionals, and entrepreneurs.


What you'll learn in this course

  • Learn the basics of Python programming step by step

  • Work with data using Pandas & NumPy

  • Clean, filter, and organize large datasets

  • Create charts & graphs with Matplotlib and Seaborn

  • Understand simple statistics & probability for data science

  • Build machine learning models (regression, classification, clustering)

  • Get an introduction to AI concepts like NLP (text) and CV (images)

  • Work on real projects like price prediction, sentiment analysis, and image recognition

  • Build a portfolio to showcase your skills to employers or clients

Requirements

  • Basic Computer Knowledge (no coding experience required).

  • Laptop/PC with Internet Connection (8GB RAM preferred for AI projects).

  • Curiosity to Learn & Practice (1–2 hrs/day recommended).

  • No prior math/statistics knowledge required, but helpful.

Curriculum

  • 10 Lessons
  • 60 mins.
Introduction to Python
  • Overview of Python for Data Science & AI
  • Setting up Python, IDEs, and Jupyter Notebook
  • Understanding Python’s role in Data Science & AI
  • Variables and naming conventions
  • Python data types: int, float, string, boolean
  • Type conversion and dynamic typing
  • Operators: arithmetic, comparison, logical, assignment, bitwise
  • Conditional statements: if, elif, else
  • Loops: for, while, nested loops
  • Loop control: break, continue, pass
  • Practical examples with datasets
  • String creation, indexing, slicing
  • String methods and formatting
  • String manipulation for data cleaning
  • Working with multiline and raw strings
  • Lists: creation, indexing, slicing, methods
  • Tuples: immutability and use cases
  • Dictionaries: key-value pairs, methods, nested dictionaries
  • Choosing the right data structure for tasks
  • Defining and calling functions
  • Function arguments: positional, keyword, default, *args, **kwargs
  • Lambda functions and higher-order functions
  • Scope, recursion, and practical examples
  • Classes and objects
  • Attributes, methods, and constructors
  • Inheritance, polymorphism, and encapsulation
  • Real-world examples for data modeling
  • Errors vs exceptions in Python
  • try, except, else, finally blocks
  • Raising exceptions and custom exceptions
  • Best practices in error handling
  • Reading and writing files (text, CSV, JSON)
  • Working with file paths and directories
  • Handling exceptions during file operations
  • Practical examples in data processing
  • Overview of Machine Learning concepts
  • Supervised vs unsupervised learning
  • Regression: linear and logistic regression
  • Classification: decision trees, k-NN
  • Model evaluation: accuracy, precision, recall, F1-score

Your Instructor

Team

Skill Pathshala Team

Advanced Educator

Skill Pathshala is an online learning community with thousands of classes for creative and curious people, on topics including illustration, design, programming, photography, video, freelancing, and more. On Skill Pathshala, members come together to find inspiration and take the next step in their creative journey. 


₹199/Session
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  • Duration span 60 mins.
  • Lectures0
  • LanguageEnglish
  • Skill levelBeginner

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