Python for Data Science with AI: Beginner to Data Analyst

Learnkart Technology Pvt. Ltd.

• Welcome to Python for Data Science: How This Course Works 38:45

  • 1: Course Introduction 03:40
  • 2: Learning Objectives 01:14
  • 3: Demo: Hands-on Reality Check: What Data Work Actually Looks Like 04:57
  • 4: Why Python Still Dominates Data Science and AI 04:07
  • 5: The Learning Approach Used in This Course 04:17
  • 6: Demo: Getting Started with Google Colab 10:43
  • 7: Demo: Writing Your First Python Program on Google Colab 04:47
  • 8: Checkpoint Quiz 04:00
  • 9: Summary 01:00

• Real-World Data Science Case Study: What You’ll Build 16:01

  • 10: Learning Objectives 01:21
  • 11: Introduction to the Case Study 01:28
  • 12: How the Case Study Connects Every Concept You’ll Learn 00:38
  • 13: The End-to-End Data Science Workflow 02:26
  • 14: Planning Your Solution Approach 02:07
  • 15: Checkpoint Quiz 03:00
  • 16: Proficiency Quiz 04:00
  • 17: Summary 01:01

• Python for Data Science vs Programming: What Really Matters 36:42

  • 18: Learning Objectives 00:52
  • 19: A Tale of Two Pythons 01:19
  • 20: Python for Software Development vs Data Science 05:14
  • 21: What Matters for Programming for Data Science 01:40
  • 22: Code vs. Pseudocode 03:38
  • 23: Importance of Pseudo Code 02:12
  • 24: Writing Pseudo Code 01:37
  • 25: Demo : Writing Pseudocode for Data Problems 04:45
  • 26: Checkpoint Quiz 07:00
  • 27: Proficiency Quiz 07:00
  • 28: Summary 01:25

• Using Generative AI to Learn Python Effectively 32:57

  • 29: Learning Objectives 00:53
  • 30: Common Mistakes Learners Make with GenAI 02:47
  • 31: The Illusion of “Knowing Code” 02:14
  • 32: A Smarter Way to Use Genai for Learning 02:39
  • 33: Demo: Using GenAI as a Coding Assistant 07:49
  • 34: Reflection & Next Steps 02:35
  • 35: Checkpoint Quiz 05:00
  • 36: Proficiency Quiz 08:00
  • 37: Summary 01:00

• Python Concepts Needed for Real Data Analysis 08:34

  • 38: Learning Objective 01:00
  • 39: Revisiting the Case Study Requirements 01:33
  • 40: Identifying the Exact Python Concepts Needed 02:01
  • 41: Checkpoint Quiz 01:00
  • 42: Proficiency Quiz 02:00
  • 43: Summary 01:00

• Python Variables, Data Types, and Lists for Data Work 51:06

  • 44: Learning Objectives 01:13
  • 45: Understanding Variables Used in Data Problems 01:18
  • 46: Numbers and Strings in Python 04:00
  • 47: Demo : Working with Numbers 06:26
  • 48: Demo : Working with Strings 08:51
  • 49: Indexing in Strings 01:59
  • 50: Demo : Indexing and String Operations 10:43
  • 51: Lists and List Indexing 03:27
  • 52: Demo: Indexing and Access in Lists 02:06
  • 53: List Functions 01:23
  • 54: Demo: Useful List Functions for Data Work 03:27
  • 55: Checkpoint Quiz 05:00
  • 56: Summary 01:13

• Control Flow and Decision Making 58:40

  • 57: Learning Objectives 00:39
  • 58: Why Control Structures Matter in Data Logic 02:17
  • 59: 'if' Statements in Python 05:08
  • 60: Demo : Simple 'if' Conditions 06:00
  • 61: 'if-else' Logic 03:34
  • 62: Demo : Using "if-else" 03:33
  • 63: Multiple Conditions (if-elif-else) 03:56
  • 64: Demo: Using "if-elif-else" 04:51
  • 65: Nested Decision Logic 05:23
  • 66: Demo: Nested 'if' Statements 07:19
  • 67: Checkpoint Quiz 06:00
  • 68: Proficiency Quiz 09:00
  • 69: Summary 01:00

• Applying Loops and Functions to the Case Study 21:00

  • 70: Learning Objectives 01:17
  • 71: Reviewing the Problem Logic 02:08
  • 72: Identifying Repeated Patterns 03:03
  • 73: Demo: Writing Pseudocode Before Coding 10:32
  • 74: Proficiency Quiz 04:00

• Python Loops Explained: For and While Loops in Data Analysis 1:06:54

  • 75: Learning Objectives 01:25
  • 76: Why Loops Are Essential in Data Processing 05:06
  • 77: Pseudocode for "for" Loops 07:11
  • 78: Demo: Writing "for" Loops - Part-1 08:19
  • 79: Demo: Writing "for" Loops - Part-2 05:31
  • 80: “while” Loops and Use Cases 05:56
  • 81: Demo: Conditional Execution Inside “while” Loop - Part-1 07:39
  • 82: Demo: Conditional Execution Inside “while” Loop - Part-2 03:47
  • 83: Demo: Conditional Execution Inside “for” Loop 05:52
  • 84: Checkpoint Quiz 07:00
  • 85: Proficiency Quiz 08:00
  • 86: Summary 01:08

• Python Functions and Reusable Logic for Data Analysis 45:49

  • 87: Learning Objectives 01:21
  • 88: Why Abstraction Improves Clarity 03:17
  • 89: How Functions Work in Python 05:14
  • 90: Python Inbuilt Functions 01:00
  • 91: Demo: Inbuilt Functions 03:03
  • 92: Write Pseudocode for Custom Functions 05:45
  • 93: Writing Custom Functions 06:17
  • 94: Demo: Custom Functions 07:43
  • 95: Checkpoint Quiz 05:00
  • 96: Proficiency Quiz 06:00
  • 97: Summary 01:09

• Implementing Complete Data Logic Using Python Code 21:11

  • 98: Learning Objectives 00:48
  • 99: Reviewing Solution Approach 01:22
  • 100: Demo : Reviewing Pseudo Code 06:08
  • 101: Demo : Using Gemini to Verify Pseudo Code 03:53
  • 102: Checkpoint Quiz 04:00
  • 103: Proficiency Quiz 04:00
  • 104: Summary 01:00

• Pandas Series and DataFrames: Working with Real Datasets 1:01:04

  • 105: Learning Objectives 00:44
  • 106: Why Pandas Is the Core Data Tool 05:13
  • 107: Understanding Panda Series 03:18
  • 108: Demo: Pandas Series & Useful Functions 09:42
  • 109: Pandas DataFrames: Collection of Series 03:24
  • 110: Demo: Creating Dataframes 09:14
  • 111: Importing and Exporting Data 03:34
  • 112: Demo: Importing Data 06:24
  • 113: Demo: Exporting Data 03:14
  • 114: Checkpoint Quiz 05:00
  • 115: Proficiency Quiz 10:00
  • 116: Summary 01:17

• Accessing, Filtering, and Sorting Data with Pandas 54:04

  • 117: Learning Objectives 01:27
  • 118: Data Access in Pandas (Loc, Iloc) 01:01
  • 119: Demo: Data Access in Pandas 13:57
  • 120: Demo: Filtering and Querying Data 11:05
  • 121: Demo: Modifying Values 08:01
  • 122: Demo: Sorting Datasets 08:30
  • 123: Checkpoint Quiz 03:00
  • 124: Proficiency Quiz 06:00
  • 125: Summary 01:03

• Data Transformation and Aggregation Using Pandas 50:07

  • 126: Learning Objectives 01:08
  • 127: Demo: Combining Datasets with Concat 05:55
  • 128: Demo: Data Merging 05:55
  • 129: Grouping and Aggregation 04:04
  • 130: Demo: Grouping and Summarizing Data 07:14
  • 131: Pivot Tables for Insights 01:53
  • 132: Demo : Pivot Tables 07:03
  • 133: Checkpoint Quiz 08:00
  • 134: Proficiency Quiz 08:00
  • 135: Summary 00:55

• Solving the Case Study with Pandas 36:14

  • 136: Learning Objectives 00:44
  • 137: Demo: Using Pandas for Our Case Study 02:50
  • 138: Demo: Writing Pseudocode for Our Case Study 03:56
  • 139: Demo - Verifying Pesudocode with Gemini 02:44
  • 140: Demo - Converting Pseudo Code to Python Code 08:07
  • 141: Demo - Evaluating the Code with Gemini 02:53
  • 142: Checkpoint Quiz 07:00
  • 143: Proficiency Quiz 07:00
  • 144: Summary 01:00

• Data Visualization with Matplotlib: Core Plot Types 51:10

  • 145: Learning Objectives 01:00
  • 146: Overview of Python Visualization Libraries 03:35
  • 147: Why Matplotlib Is Foundational 02:13
  • 148: Common Plot Types for Analysis 03:00
  • 149: Demo: Creating Line, Bar, Pie Charts 08:44
  • 150: Demo: Creating Histograms, Box Plots, Scatter Plots 06:11
  • 151: Demo: Many Ways to Plot 06:17
  • 152: Demo: Saving and Exporting Visualizations 04:02
  • 153: Checkpoint Quiz 07:00
  • 154: Proficiency Quiz 08:00
  • 155: Summary 01:08

• Statistical Data Visualization with Seaborn 54:10

  • 156: Learning Objectives 00:36
  • 157: Why Seaborn Is Powerful for Analysis 02:51
  • 158: Statistical Visualizations in Seaborn 05:04
  • 159: Demo: Boxplots, Violin Plots, Histograms 09:57
  • 160: Demo: Scatter Plots, Regression Plots, and Facets 06:05
  • 161: Demo: Heatmaps for Correlations 03:49
  • 162: Customizing Visuals in Seaborn 02:04
  • 163: Demo: Customizing Visualizations 07:18
  • 164: Checkpoint Quiz 08:00
  • 165: Proficiency Quiz 07:00
  • 166: Summary 01:26

• Case Study – Complete Data Analysis Solution 53:58

  • 167: Learning Objectives 00:45
  • 168: Demo: Reviewing the Full Data Workflow 04:12
  • 169: Demo: Final Pseudocode Walkthrough 04:12
  • 170: Demo: Validating Pseudocode with Gemini 04:12
  • 171: Demo: Writing the Complete Python Solution - Part-1 11:46
  • 172: Demo: Writing the Complete Python Solution - Part-2 09:15
  • 173: Demo: Validating and Running the Full Process 07:36
  • 174: Checkpoint Quiz 06:00
  • 175: Proficiency Quiz 05:00
  • 176: Summary 01:00

You'll begin the Python with Data Science course by learning why Python is the preferred language for data science and AI, along with what working with data actually looks like in the real world.

Next, you'll set up Google Colab, write your first Python program, and dive into a real-world case study that will guide your learning throughout the course. Before writing any code, you'll also learn how to plan your solution using pseudocode, which is a practical approach that helps you think through the logic before you start coding.

Then you’ll learn how to use GenAI tools effectively as part of your learning journey. You'll also learn the common mistakes beginners make, understand why writing code is not the same as understanding, and discover how to use AI as an assistant in coding rather than a shortcut. This way you will develop good problem-solving habits before you begin writing Python confidently.

Next up, you will learn about variables, numbers, strings, and how indexing works. Then you will learn about lists and the built-in functions that make lists useful to handle data. From there, you'll explore control structures, beginning with simple conditions, then progressing to if, if-else, and if-elif-else statements, before finally tackling nested decision-making for more complex, real-world scenarios.

You'll then learn how to write efficient, reusable Python code using loops and functions. You'll build for and while loops, apply conditional logic within them, and continue turning pseudocode into working Python programs. As you proceed, you'll find how functions make your code cleaner and easier to reuse, starting with Python's built-in functions before creating your own custom functions. Along the way, you'll also learn how to use AI tools to review your logic and refine your approach before turning it into code.

From there, you'll move into practical data analysis using Pandas. You will learn how to work with Series and DataFrames, how to import and export data, and how to access, filter, sort and modify datasets using loc and iloc among others. You'll then build on these skills by combining datasets through concatenation and merging. You'll also learn how to group and aggregate data, as well as create pivot tables to organize information and uncover meaningful insights.

Finally, you will turn your analysis into clear visual insights with Matplotlib and Seaborn. You will begin with Matplotlib and learn to create line, bar, pie, histogram, box, and scatter plots, and how to export your visualizations for reports or presentations. Next, you will jump into Seaborn for some more advanced statistical visuals including regression plots, faceted plots, and correlation heatmaps, along with ways to customize your visuals, so they are ready for presentation.

• Complete beginners with no prior coding experience who want to break into data science using Python
• Students exploring a career path in data, analytics, or AI
• Working professionals in fields like marketing, sales, operations, finance, HR, or supply chain
• Career changers aiming for roles such as data analyst, junior data scientist, business analyst, BI analyst, or reporting/MIS executive
• Freelancers and consultants who want to offer data analysis and visualization as part of their service offerings
• Entrepreneurs and small business owners who want to make sense of their own business data without relying on a dedicated analyst
• Anyone who has tried learning Python before but found it too theoretical or disconnected from real-world use
• Learners curious about GenAI tools and how to use them responsibly to support coding and data work, rather than as a shortcut

No prior programming or coding experience is required as the Python for Data Science course starts from the basics.

This Python course equips you with skills that can lead to a variety of data-focused career opportunities, including:

• Data Analyst: Turn raw data into powerful insights
• Business Analyst: Drive smarter business decisions
• Junior Data Scientist: Build advanced models with Python
• BI/Reporting Analyst: Craft dashboards that tell a story
• Data-Driven Ops/Marketing Pro: Level up your role with data

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