Python Programming and Data Science Foundations for AI
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Python Programming and Data Science Foundations for AI
This course is part of AI & Python Development Megaclass Specialization
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What you'll learn
Master Python fundamentals and apply them to real-world problems.
Utilize NumPy and Pandas for data manipulation and analysis in AI projects.
Implement object-oriented programming concepts for scalable Python applications.
Build AI-driven applications using Python, web frameworks, and data science techniques.
Skills you'll gain
- Statistical Machine Learning
- Statistical Methods
- Machine Learning Methods
- Artificial Intelligence and Machine Learning (AI/ML)
- Data Manipulation
- Object Oriented Design
- Data Visualization
- Computer Programming
- Back-End Web Development
- Object Oriented Programming (OOP)
- Plot (Graphics)
- Application Development
- Web Development
- Programming Principles
- Data Analysis Software
- Probability & Statistics
Tools you'll learn
Details to know
February 2026
13 assignments
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There are 11 modules in this course
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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course provides a comprehensive foundation in Python programming and data science, essential for building AI applications. You will gain hands-on experience in Python fundamentals, explore essential data science tools like NumPy and Pandas, and develop an understanding of core machine learning concepts. Throughout the course, youβll progress step by step, starting with Python basics such as control flow, functions, and data structures, then moving on to more advanced topics like object-oriented programming (OOP), data science libraries, and visualization tools. The course integrates interactive exercises to deepen your understanding, with real-world projects to apply what you've learned. The course is designed to be approachable for beginners, with no prior experience required. As you advance, youβll build practical skills and a portfolio of projects, including Python applications, web apps, data analysis, and more. This hands-on approach ensures that youβll not only learn but also apply the concepts to real-world AI challenges. By the end of the course, you will be able to write Python programs, manipulate data with libraries like Pandas, use statistical and machine learning techniques, and build data-driven applications to solve real-world problems.
In this module, we will dive into Python programming basics, focusing on the foundational knowledge needed for AI development. You will learn essential Python syntax, control flow, functions, and data structures. The module also covers file handling and Pythonic coding practices, equipping you with practical skills for building AI applications.
What's included
8 videos2 readings1 assignment
8 videosβ’Total 194 minutes
- Introduction to the Specializationβ’1 minute
- Day 1: Introduction to Python and Development Setupβ’21 minutes
- Day 2: Control Flow in Pythonβ’33 minutes
- Day 3: Functions and Modulesβ’23 minutes
- Day 4: Data Structures (Lists, Tuples, Dictionaries, Sets)β’31 minutes
- Day 5: Working with Stringsβ’24 minutes
- Day 6: File Handlingβ’23 minutes
- Day 7: Pythonic Code and Project Workβ’39 minutes
2 readingsβ’Total 20 minutes
- Introduction to the Course 'Python Programming and Data Science Foundations for AI'β’10 minutes
- Full Specialization Resourcesβ’10 minutes
1 assignmentβ’Total 15 minutes
- Python Programming Basics for Artificial Intelligence - Assessmentβ’15 minutes
In this module, we will introduce the core concepts of data science, essential for artificial intelligence. You will explore libraries like NumPy and Pandas for data manipulation and analysis, learn how to clean and prepare data, and visualize data using powerful tools like Matplotlib and Seaborn. By the end of the module, youβll be ready to perform exploratory data analysis on real datasets.
What's included
8 videos1 assignment
8 videosβ’Total 155 minutes
- Introduction to Week 2 Data Science Essentialsβ’1 minute
- Day 1: Introduction to NumPy for Numerical Computingβ’23 minutes
- Day 2: Advanced NumPy Operationsβ’22 minutes
- Day 3: Introduction to Pandas for Data Manipulationβ’20 minutes
- Day 4: Data Cleaning and Preparation with Pandasβ’24 minutes
- Day 5: Data Aggregation and Grouping in Pandasβ’15 minutes
- Day 6: Data Visualization with Matplotlib and Seabornβ’27 minutes
- Day 7: Exploratory Data Analysis (EDA) Projectβ’23 minutes
1 assignmentβ’Total 15 minutes
- Data Science Essentials for Artificial Intelligence - Assessmentβ’15 minutes
In this module, we will cover the mathematical foundations essential for machine learning and AI. You will explore linear algebra, calculus, probability theory, and statistics, all of which are vital for understanding and optimizing machine learning algorithms. The module will also include a hands-on project to apply these concepts in building a linear regression model from scratch.
What's included
8 videos1 assignment
8 videosβ’Total 136 minutes
- Introduction to Week 3 Mathematics for Machine Learningβ’1 minute
- Day 1: Linear Algebra Fundamentalsβ’21 minutes
- Day 2: Advanced Linear Algebra Conceptsβ’20 minutes
- Day 3: Calculus for Machine Learning (Derivatives)β’18 minutes
- Day 4: Calculus for Machine Learning (Integrals and Optimization)β’16 minutes
- Day 5: Probability Theory and Distributionsβ’25 minutes
- Day 6: Statistics Fundamentalsβ’19 minutes
- Day 7: Math-Driven Mini Project β Linear Regression from Scratchβ’15 minutes
1 assignmentβ’Total 15 minutes
- Mathematics for Machine Learning and Artificial Intelligence - Assessmentβ’15 minutes
In this module, we will dive into the probability and statistics methods used in machine learning to make predictions and draw conclusions. You will learn how to apply probability distributions, perform hypothesis testing, and analyze data relationships. The module also includes a project where youβll analyze real-world data using statistical methods.
What's included
8 videos1 assignment
8 videosβ’Total 125 minutes
- Introduction to Week 4 Probability and Statistics for Machine Learningβ’1 minute
- Day 1: Probability Theory and Random Variablesβ’19 minutes
- Day 2: Probability Distributions in Machine Learningβ’17 minutes
- Day 3: Statistical Inference β Estimation and Confidence Intervalsβ’16 minutes
- Day 4: Hypothesis Testing and P-Valuesβ’12 minutes
- Day 5: Types of Hypothesis Testsβ’19 minutes
- Day 6: Correlation and Regression Analysisβ’17 minutes
- Day 7: Statistical Analysis Project β Analyzing Real-World Dataβ’25 minutes
1 assignmentβ’Total 15 minutes
- Probability and Statistics for Machine Learning and AI - Assessmentβ’15 minutes
In this module, we will introduce the basics of Python programming, starting with simple syntax and building up to creating interactive programs. You will learn about variables, data types, loops, and functions, all while working on hands-on projects like a calculator, number comparison tool, and more.
What's included
8 videos1 assignment
8 videosβ’Total 138 minutes
- Learn Python from Scratch β Quick Tutorialβ’39 minutes
- Day 1: Welcome Message Generator β Print Statements & "Hello World"β’12 minutes
- Day 2: Personalized Greeting Program β Variables & Data Typesβ’14 minutes
- Day 3: Simple Calculator β User Input & String Formattingβ’13 minutes
- Day 4: Number Comparison Tool β If-Else Statementsβ’13 minutes
- Day 5: Countdown Timer β Loops (for & while)β’12 minutes
- Day 6: Basic Math Quiz Game β Functionsβ’16 minutes
- Day 7: Shopping List App β Listsβ’18 minutes
1 assignmentβ’Total 15 minutes
- Python Basics - Assessmentβ’15 minutes
In this module, we will delve into intermediate Python concepts, enhancing your skills in application development. Youβll work with advanced data structures, error handling, file manipulation, and external libraries, applying your knowledge to projects like a student grade manager and contact book app.
What's included
7 videos1 assignment
7 videosβ’Total 115 minutes
- Day 8: Contact Book β Dictionariesβ’21 minutes
- Day 9: Ingredient Checker β Tuples & Setsβ’16 minutes
- Day 10: Note-Taking App β File Handlingβ’17 minutes
- Day 11: Safe Calculator β Exception Handlingβ’16 minutes
- Day 12: Temperature Converter β Functions with Return Valuesβ’15 minutes
- Day 13: Student Grade Manager β List Comprehensionsβ’14 minutes
- Day 14: Random Password Generator β Modules & Librariesβ’16 minutes
1 assignmentβ’Total 15 minutes
- Intermediate Python - Assessmentβ’15 minutes
In this module, we will explore how to work with data using Pythonβs file handling capabilities and web scraping techniques. Youβll develop projects like a weather app, event countdown timer, and stock price tracker, gaining hands-on experience with reading, writing, and processing real-world data.
What's included
7 videos1 assignment
7 videosβ’Total 120 minutes
- Day 15: Recipe Viewer App β Reading Filesβ’16 minutes
- Day 16: Daily Journal Logger β Writing Filesβ’16 minutes
- Day 17: Student Report Generator β CSV Filesβ’16 minutes
- Day 18: Mini To-Do App β JSON Filesβ’18 minutes
- Day 19: Weather App Using API β APIs (Basics)β’19 minutes
- Day 20: Event Countdown Timer β Dates & Timeβ’15 minutes
- Day 21: Wikipedia Article Scraper β Web Scrapingβ’22 minutes
1 assignmentβ’Total 15 minutes
- Working with Data - Assessmentβ’15 minutes
In this module, we will focus on object-oriented programming (OOP) principles, including classes, inheritance, polymorphism, and encapsulation. Youβll build real-world applications like a bank account simulator, library management system, and mini ATM machine, all while mastering OOP concepts.
What's included
7 videos1 assignment
7 videosβ’Total 128 minutes
- Day 22: Bank Account Simulator β Classes & Objectsβ’20 minutes
- Day 23: Library Management System β Constructors & Methodsβ’17 minutes
- Day 24: Employee Management System β Inheritanceβ’23 minutes
- Day 25: Animal Sound Simulator β Polymorphismβ’15 minutes
- Day 26: Secure User Profile App β Encapsulationβ’19 minutes
- Day 27: Inventory Management System β Static & Class Methodsβ’17 minutes
- Day 28: Mini ATM Machine β Final OOP Projectβ’17 minutes
1 assignmentβ’Total 15 minutes
- Object-Oriented Programming - Assessmentβ’15 minutes
In this module, we will introduce graphical user interface (GUI) programming using Pythonβs Tkinter library. You will learn how to build interactive applications, including a click counter, BMI calculator, and to-do list app, and finish the module with a complete expense tracker project.
What's included
7 videos1 assignment
7 videosβ’Total 142 minutes
- Day 29: Simple GUI App β Tkinter Basicsβ’20 minutes
- Day 30: Click Counter App β Buttons & Eventsβ’21 minutes
- Day 31: BMI Calculator β Input Fieldsβ’21 minutes
- Day 32: Drawing Pad App β Canvas Widgetsβ’22 minutes
- Day 33: Simple Login System β Message Boxesβ’18 minutes
- Day 34: To-Do List GUI β Advanced Widgetsβ’19 minutes
- Day 35: Expense Tracker App β GUI Capstoneβ’20 minutes
1 assignmentβ’Total 15 minutes
- GUI Programming - Assessmentβ’15 minutes
In this module, we will dive into web development with Python using Flask. Youβll learn how to build web applications from scratch, handle user input with forms, and integrate databases. By the end of the module, youβll deploy your projects to the web, including a personal portfolio website.
What's included
7 videos1 assignment
7 videosβ’Total 135 minutes
- Day 36: Hello Flask App β Flask Basicsβ’20 minutes
- Day 37: Personal Blog Website β Routes & Templatesβ’29 minutes
- Day 38: Contact Form App β Forms & User Inputβ’14 minutes
- Day 39: User Registration App β Database Integrationβ’19 minutes
- Day 40: Mini Weather API β REST APIsβ’18 minutes
- Day 41: Deploy Flask App β Deploymentβ’19 minutes
- Day 42: Portfolio Website β Flask Capstoneβ’16 minutes
1 assignmentβ’Total 15 minutes
- Web Development with Python - Assessmentβ’15 minutes
In this module, we will focus on the basics of data science, teaching you how to work with libraries like NumPy, Pandas, and Matplotlib. Youβll build projects such as a stock price tracker and COVID-19 dashboard, using data analysis techniques to track and visualize trends.
What's included
7 videos1 reading3 assignments
7 videosβ’Total 132 minutes
- Day 43: Matrix Calculator β NumPyβ’19 minutes
- Day 44: Data Cleaner β Pandasβ’18 minutes
- Day 45: Graph Plotter β Matplotlibβ’18 minutes
- Day 46: Sales Report Analyzer β Data Analysisβ’24 minutes
- Day 47: Temperature Plotter β Plotting Trendsβ’18 minutes
- Day 48: Stock Price Tracker β Data Scrapingβ’17 minutes
- Day 49: COVID-19 Dashboard β Capstone Projectβ’19 minutes
1 readingβ’Total 10 minutes
- Conclusion to the Course 'Python Programming and Data Science Foundations for AI'β’10 minutes
3 assignmentsβ’Total 90 minutes
- Data Science Basics - Assessmentβ’15 minutes
- Full Course Assessmentβ’60 minutes
- Full Course Practice Assessmentβ’15 minutes
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This course introduces the foundational skills necessary for working with Python programming and data science in the context of artificial intelligence (AI). Python is a widely used language in AI development due to its simplicity and powerful libraries. Understanding the basics of Python programming and data science tools like NumPy, Pandas, and Matplotlib is crucial for anyone interested in AI, as they form the building blocks for data analysis and machine learning.
This course covers essential topics to build a strong foundation in both Python programming and data science. Youβll start by learning Python basics and progress to more complex topics, such as data manipulation, visualization, and the mathematics underlying AI. Throughout the course, you will gain hands-on experience with key tools and libraries, such as NumPy, Pandas, and Matplotlib, which are fundamental for AI and machine learning tasks.
After completing the course, you will be able to write efficient Python code, handle and manipulate datasets, clean and visualize data, and apply mathematical concepts like linear algebra, calculus, and statistics to machine learning and AI models. You will also be equipped to perform basic data analysis, develop AI-related projects, and understand the data science workflow.
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