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Python For Data Science and AI/ML Certification Training

The Python Certification Course offers a comprehensive learning experience for individuals looking to master the Python programming language and its versatile applications.
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12
live sessions
9
modules
80
lessons
54
self-paced videos
Python For Data Science and AI/ML Certification Training
12 live sessionsinstructor-led 9-module programstructured path Certificateon completion

Python For Data Science and AI/ML Tools and Technologies Covered

Python
VS Code
Jupyter
Code Debugging
OOPs
Numpy
Pandas
Matplotlib
Fast API
Statistics
Machine Learning
AI
NLP
ChatGPT
GitHub Copilot

Python For Data Science and AI/ML Course Key Features

10 Weeks of Intensive Live Training

Interactive sessions with real-time problem solving

Learn from Microsoft MVPs

Training by globally recognized experts

Build Data Science & AI Projects

Hands-on project for your portfolio

Hands-On Labs

Practice real scenarios with guided, interactive labs

Interview Q&A

Frequently asked interview questions with clear answers

Quick Notes

Concise revision notes for fast and effective learning

Why Learn Python For Data Science and AI/ML in 2025?

  • High Demand Skills: Python is the most sought-after skill in data science and AI job markets.
  • Versatile Applications: Widely used in data analysis, machine learning, and AI-powered solutions.
  • Beginner-Friendly: Easy to learn with extensive libraries and community support.
  • Future-Ready: Powers cutting-edge technologies like deep learning and natural language processing.
  • Career Boost: Opens doors to high-paying roles in data science, AI, and analytics.
  • Industry Standard: Preferred language for top companies like Google, Facebook, and Netflix.

    • Python For Data Science and AI/ML Career Scope in 2025

    Is investing time in learning Python for Data Science and AI/ML a smart career move? Absolutely! Data scientists and AI professionals are among the highest earners in the tech industry. This is because Python is widely used across diverse industries—from startups to large enterprises—for building data-driven solutions and AI-powered applications, providing immense earning potential and career stability.

    1. Python Developer: Build applications, automate tasks, and integrate systems with Python.
    2. Data Scientist: Design predictive models and analyze datasets using Python.
    3. AI/ML Engineer: Build machine learning models and AI solutions with TensorFlow and PyTorch.
    4. Data Analyst: Analyze and visualize data using Python libraries like pandas and Matplotlib.
    5. Backend Developer: Create scalable server-side applications with Django and Flask.

    Course Curriculum

    • 9 modules
    • 80 lessons
    • 12 live sessions
    1. Module 1 Python Programming Foundations 10 lessons
      • What is Python & Why Python for Data Science
      • Installing Python, Anaconda & VS Code
      • Python Syntax, Variables & Data Types
      • Operators & Expressions
      • Conditional Statements (if, elif, else)
      • Loops (for, while)
      • Functions & Lambda Functions
      • Working with Modules & Packages
      • Exception Handling & Debugging
      • Writing Clean & Readable Python Code
    2. Module 2 Python Data Structures & OOPs 10 lessons
      • Lists, Tuples, Sets & Dictionaries
      • Indexing & Slicing
      • List & Dictionary Comprehensions
      • Built-in Functions (map, filter, reduce)
      • String Handling & Formatting
      • File Handling (CSV, TXT, JSON)
      • Introduction to Object-Oriented Programming (OOP)
      • Classes & Objects
      • Methods & Constructors
      • Inheritance (Basics)
    3. Module 3 Mathematics & Statistics for Data Science 8 lessons
      • Types of Data (Numerical, Categorical)
      • Mean, Median & Mode
      • Variance & Standard Deviation
      • Percentiles & Quartiles
      • Correlation & Covariance
      • Probability Basics
      • Data Distribution (Normal, Skewed)
      • Why Math & Statistics Matter in AI/ML
    4. Module 4 NumPy for Numerical Computing 8 lessons
      • Introduction to NumPy
      • Arrays vs Python Lists
      • Array Creation & Indexing
      • Vectorized Operations
      • Broadcasting
      • Mathematical & Statistical Functions
      • Reshaping & Aggregation
      • Performance Benefits of NumPy
    5. Module 5 Pandas for Data Analysis 9 lessons
      • Introduction to Pandas
      • Series & DataFrame
      • Reading Data (CSV, Excel, JSON)
      • Data Inspection & Cleaning
      • Handling Missing Values
      • Filtering, Sorting & Grouping
      • Data Aggregation
      • Merging & Joining DataFrames
      • Simple Data Analysis Case Studies
    6. Module 6 Matplotlib for Data Visualization 7 lessons
      • Why Data Visualization is Important
      • Matplotlib Basics
      • Line, Bar, Pie & Histogram Charts
      • Scatter Plots
      • Subplots & Figure Customization
      • Introduction to Seaborn
      • Visualizing Data for Insights
    7. Module 7 Introduction to Machine Learning 10 lessons
      • Data Science vs AI vs Machine Learning
      • Types of Machine Learning
      • Supervised Learning (Overview)
      • Unsupervised Learning (Overview)
      • Reinforcement Learning (Overview)
      • Machine Learning Workflow
      • Training vs Testing Data
      • Overfitting & Underfitting
      • Evaluation Metrics (Accuracy, Precision, Recall)
      • Real-world ML Use Cases
    8. Module 8 Introduction to Artificial Intelligence 7 lessons
      • What is Artificial Intelligence?
      • Rule-Based Systems vs ML-Based AI
      • What is Natural Language Processing?
      • Understanding Computer Vision
      • Recommendation Systems
      • Generative AI Basics
      • Ethical AI & Responsible AI
    9. Module 9 Generative AI and GitHub Copilot 11 lessons
      • What is Artificial Intelligence?
      • What is Generative AI?
      • Large language models (LLMs)
      • Ethics and biases in AI
      • Introduction to Prompt Engineering
      • Advanced Prompt Engineering Strategies
      • Overview of AI Security Threats
      • AI Security Challenges
      • What is GitHub Copilot?
      • Setting up GitHub Copilot in VS Code
      • Configuring GitHub Copilot in Visual Studio
    DOWNLOAD DETAILED CURRICULUM
    • 5 modules
    • 54 videos
    • 3h 17m total
    1. Module 1Introduction to Python: getting Started 13 videos 58m

      Overview of Python

      • Python Overview 0h 02m 42s
      • Python Advantages and Disadvantages 0h 03m 51s
      • Python an Interpreted Language 0h 02m 15s
      • Python History 0h 03m 24s
      • Downloading and Installing Python 0h 04m 15s
      • Python First Program 0h 03m 53s

      Variables and Data Types

      • Variables 0h 06m 03s
      • Data Types 0h 07m 04s
      • String and String Methods 0h 06m 07s
      • String Formatting 0h 03m 09s
      • Escapse Sequences 0h 03m 32s
      • Python Operators 0h 07m 28s
      • Reading from Keyboard 0h 04m 36s
    2. Module 2Python Conditional Statement and Loops 13 videos 38m

      Conditional Constructs

      • Conditional Statements 0h 01m 37s
      • If Statement 0h 03m 47s
      • If else statement 0h 02m 33s
      • If-Elif-Else-Statement 0h 03m 43s
      • Nested Conditions 0h 03m 42s
      • Match-Case Conditions 0h 02m 59s

      Looping Constructs

      • Loops in Python 0h 00m 44s
      • For Loop 0h 05m 50s
      • While Loop 0h 04m 23s
      • Jump Statements 0h 00m 53s
      • Break Statement 0h 02m 00s
      • Continue Statement 0h 03m 22s
      • Return Statement 0h 02m 28s
    3. Module 3Functions, Modules in Python 9 videos 29m

      Working with Functions

      • Python Functions - Session Agenda 0h 00m 36s
      • Functions Introduction 0h 01m 20s
      • Defining and Calling Functions 0h 02m 57s
      • Function Arguments 0h 04m 50s
      • Lambda Functions 0h 04m 28s
      • Recursion Function 0h 04m 34s
      • Built-In Functions 0h 04m 10s
      • Anonymous-Functions 0h 02m 41s
      • Global, Local and NonLocal 0h 03m 55s
    4. Module 4Object-Oriented Programming (OOP) with Python 9 videos 36m

      Object Oriented Programming

      • Object-Oriented-Programming-Module-Introduction 0h 01m 00s
      • OOPS Introduction 0h 01m 45s
      • Classes and Objects 0h 03m 56s
      • Inheritance 0h 05m 52s
      • Polymorphism 0h 04m 22s
      • Encapsulation 0h 04m 42s
      • Abstraction 0h 04m 41s
      • Access Modifiers 0h 05m 21s
      • Constructor 0h 05m 11s
    5. Module 5File Handling And Exception Handling in Python 10 videos 35m

      File Handling

      • File Handling Session Agenda 0h 00m 38s
      • Python File Handling Introduction 0h 01m 59s
      • How-python-talk-to-files 0h 05m 01s
      • Text vs. Binary Files 0h 04m 45s
      • Python-Directory 0h 05m 49s
      • Exception Handling Agenda 0h 01m 00s
      • Python Exceptions 0h 02m 56s
      • Python Exception Handling 0h 03m 29s
      • Exception Handling Hands-On 0h 04m 11s
      • User Defined Exception 0h 05m 30s
    1. 01 Introduction to Python 0:06:30
    2. 02 Introduction to Python for AI 0:07:30
    3. 03 Introduction to Python for Data Science 0:07:00
    4. 04 Variables & Data Types in Python 0:05:00
    5. 05 Operators in Python 0:04:00
    6. 06 Types of Data in Data Science 0:06:00
    7. 07 Introduction to NumPy 0:05:30
    8. 08 Conditional Statements in Python 0:03:00
    9. 09 Loops in Python 0:02:30
    10. 10 Mathematical NumPy Functions 0:05:00
    11. 11 Introduction to Pandas 0:05:00
    12. 12 Jump Statements in Python 0:03:30
    13. 13 Functions in Python 0:06:00
    14. 14 Data Processing with Pandas 0:04:00
    15. 15 Data Preprocessing for AI & ML 0:05:00
    16. 16 Lambda Functions in Python 0:04:00
    17. 17 Recursion in Python 0:05:30
    18. 18 Mathematics & Statistics Fundamentals 0:06:00
    19. 19 Probability Data Distributions 0:04:00
    20. 20 Modules and Packages in Python 0:04:00
    21. 21 Object-Oriented Programming (OOP) with Python 0:07:00
    22. 22 Correlation, Covariance, and Percentiles 0:05:00
    23. 23 Introduction to Machine Learning 0:08:00
    24. 24 File Handling in Python 0:04:00
    25. 25 Errors & Exception Handling in Python 0:09:30
    26. 26 Supervised and Unsupervised Learning 0:05:00
    27. 27 Introduction to Generative AI 0:07:00
    28. 28 Python Strings 0:06:30
    29. 29 Python Tuples 0:06:00
    30. 30 Introduction to Prompt Engineering 0:05:00
    31. 31 GitHub Copilot for AI & Data Science 0:07:00
    32. 32 Python Dictionary 0:03:30
    33. 33 Python Sets 0:05:00
    34. 34 Python Lists 0:05:00
    1. 01 Prime Number Generator 00:30:00
    2. 02 Inventory Management with Collections 00:30:00
    3. 03 Temperature Converter Module 00:30:00
    4. 04 CSV Reader and Filter 00:30:00
    5. 05 Student Grade Tracker using Dictionary 00:30:00
    6. 06 Library Book Management System 00:30:00
    7. 07 Custom Calculator with Error Handling 00:30:00
    8. 08 Email Validator 00:30:00
    9. 09 Data Cleaner with map, filter, and lambda 00:30:00
    10. 10 Parallel File Download Simulation 00:30:00
    11. 11 Simple Weather API Fetcher 00:30:00
    12. 12 Logging Decorator 00:30:00
    13. 13 NumPy Basics – Arrays vs Python Lists, Creation & Indexing 01:00:00
    14. 14 Introduction to Pandas – Series, DataFrame & Basic Operations 01:00:00
    15. 15 Reading Data from Various Formats (CSV, Excel, JSON) in pandas 01:00:00
    16. 16 Data Inspection & Initial Cleaning – Titanic Dataset 01:00:00
    17. 17 Handling Missing Values – Titanic Dataset 01:00:00
    18. 18 GroupBy, Aggregation, Pivot Tables & Chaining in pandas 01:00:00
    19. 19 Exploring Types of Data & Measures of Central Tendency in Python 00:30:00
    20. 20 Measuring Spread – Variance, Standard Deviation, and Quartiles 00:30:00
    21. 21 Exploring Correlation & Covariance + Visual Exploration (Tips Dataset) 01:00:00
    22. 22 Probability Basics & Simple Distributions 01:00:00
    23. 23 Identifying & Analyzing Data Distributions – Normal vs Skewed 01:00:00
    24. 24 Exploring ML Types & Workflow – Iris Classification 01:00:00
    25. 25 Regression vs Classification – California Housing Dataset 01:00:00
    26. 26 Unsupervised Learning – Clustering & Dimensionality Reduction 01:00:00
    27. 27 Rule-Based vs ML-Based Sentiment Analysis 01:00:00
    28. 28 Computer Vision Basics – Handwritten Digit Recognition 01:00:00
    29. 29 Recommendation Systems – Collaborative Filtering Basics 01:00:00
    30. 30 Introduction to Reinforcement Learning with Q-Learning 01:00:00
    31. 31 Exploring Generative AI & Large Language Models (LLMs) 01:00:00
    32. 32 Introduction to Prompt Engineering 01:00:00
    33. 33 Applied Prompt Engineering with Modern LLMs 01:00:00
    34. 34 AI Coding Assistants – GitHub Copilot Alternatives 01:00:00
    35. 35 Setting Up & Using Windsurf (AI-Powered IDE) for Code Generation 01:00:00
    1. Q&A guideIntroduction to Python: getting Started 11 questions
      • Python Basics 0:30:00
      • Introduction to Python 0:20:00
      • Variables and Data Types in Python 0:30:00
      • Variables & Data Types 0:15:00
      • Strings in Python 0:30:00
      • Operators in Python 0:18:00
      • Operators in Python 0:30:00
      • Dictionaries & Sets in Python 0:19:00
      • List in Python 0:30:00
      • Tuples in Python 0:30:00
      • Dictionary & Sets in Python 0:30:00
    2. Q&A guidePython Conditional Statement and Loops 3 questions
      • Conditional Statements & Loops in Python 0:20:00
      • Conditional Statements in Python 0:30:00
      • Looping Statements in Python 0:30:00
    3. Q&A guideFunctions, Modules in Python 4 questions
      • Function in Python 0:30:00
      • Python Modules & Files 0:16:00
      • Recursion in Python 0:30:00
      • File Handling in Python 0:30:00
    4. Q&A guideObject-Oriented Programming (OOP) with Python 1 questions
      • Python Functions & Classes 0:17:00

    Python For Data Science and AI/ML Course Eligibility

    Pre-requisites

    There are no prerequisites to join the Python For Data Science and AI/ML course. However, having a basic understanding of programming or data concepts can be helpful.

    Who can Join?

      This course is recommended for any students, beginners and freshers interested in creating end-to-end applications.

    1. Students and Beginners: Perfect for those starting their journey in programming or data science.
    2. Freshers in Data Science: Ideal for newcomers interested in data analysis and AI.
    3. Experienced Developers: Diversify your skills with Python to explore data science and AI opportunities.
    4. Data Analysts: Learn Python to enhance your data manipulation and visualization capabilities.
    5. AI/ML Enthusiasts: Build machine learning and AI models with Python’s robust libraries.
    6. IT Professionals: Gain skills in automation and data-driven decision-making with Python.
    7. Entrepreneurs and Innovators: Leverage Python to develop AI-powered and data-driven solutions.

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      Python For Data Science and AI/ML Certification

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      Frequently Asked Questions

      Q1. What is the Azure AI Cloud Sandbox?
      The Azure AI Cloud Sandbox is a secure, isolated environment that lets you practice and experiment with Azure AI services without affecting production resources. It provides hands-on access to real Azure AI capabilities in a risk-free setup.
      Q2. Can I Attend a Demo Session before Enrolment?
      Yes, you can Attend a Demo Session before Enrolment in angular certification course. It gives you the opportunity to assess whether the training program aligns with your learning objectives. So, don't hesitate! Take advantage of this opportunity and attend a demo session before making your decision.
      Q3. Can I request for a support session if I need to better understand the topics?
      Yes, of course you can request for a support session if you need to better understand the topics. For that, you need to be in touch with the counsellor. Contact on +91- 999 9123 502 or you can mail us at hello@scholarhat.com
      Q4. Who are your mentors?
      All our mentors are highly qualified and experience professionals. All have at least 8-10 yrs of development experience in various technologies and are trained by ScholarHat to deliver interactive training to the participants.
      Q5. What If I miss my online training class?
      All online training classes are recorded. You will get the recorded sessions so that you can watch the online classes when you want. Also, you can join other class to do your missing classes.
      Q6. Can I share my course with someone else?
      In short, no. Check our licensing that you agree to by using ScholarHat LMS. We track this stuff, any abuse of copyright is taken seriously. Thanks for your understanding on this one.
      Q7. Do you provide any course material or live session videos?
      Yes we do. You will get access to the entire content including class videos, mockups, and assignments through LMS.
      Q8. Do you provide training on latest technology version?
      Yes we do. As the technology upgrades we do update our content and provide your training on latest version of that technology.
      Q9. Do you prepare me for the job interview?
      Yes, we do. We will discuss all possible technical interview questions and answers during the training program so that you can prepare yourself for interview.
      Q10. Will I get placement assistance after receiving my course completion certificate?
      Yes, you’ll get placement assistance after receiving your course completion certificate. The placement assistance provided by the US will guide you through the job search process, help you polish your resume, and connect you with potential employers. For that, you need to be in touch with the counsellor. Contact on +91- 999 9123 502 or you can mail us at hello@scholarhat.com
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