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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
54 Videos
34 Notes
35 Hands-on Labs
19 Q&A Guides

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

    Python Programming Foundations
    • 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
    Python Data Structures & OOPs
    • 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)
    Mathematics & Statistics for Data Science
    • 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
    NumPy for Numerical Computing
    • Introduction to NumPy
    • Arrays vs Python Lists
    • Array Creation & Indexing
    • Vectorized Operations
    • Broadcasting
    • Mathematical & Statistical Functions
    • Reshaping & Aggregation
    • Performance Benefits of NumPy
    Pandas for Data Analysis
    • 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
    Matplotlib for Data Visualization
    • Why Data Visualization is Important
    • Matplotlib Basics
    • Line, Bar, Pie & Histogram Charts
    • Scatter Plots
    • Subplots & Figure Customization
    • Introduction to Seaborn
    • Visualizing Data for Insights
    Introduction to Machine Learning
    • 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
    Introduction to Artificial Intelligence
    • 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
    Generative AI and GitHub Copilot
    • 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

    Overview of Python
    Python Overview
    Preview 0h 02m 42s
    Python Advantages and Disadvantages
    Preview 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
    Preview 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

    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

    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

    Object Oriented Programming
    Object-Oriented-Programming-Module-Introduction
    Preview 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

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

    Q&A Guides

    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

    Conditional Statements & Loops in Python
    0:20:00
    Conditional Statements in Python
    0:30:00
    Looping Statements in Python
    0:30:00

    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

    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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      Labs 35+
      Q&A 19+
      Notes 34+
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      Python For Data Science and AI/ML Certification

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      Verifiable Credential Python For Data Science and AI/ML Certification certificate

      Frequently Asked Questions

      Q1. 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.
      Q2. 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
      Q3. 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.
      Q4. 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.
      Q5. 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.
      Q6. 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.
      Q7. 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.
      Q8. 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.
      Q9. 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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