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Data Analysis Foundations with Python

28 chapters and 123 canonical sections synced from the Cuantum content database.

Author
Cuantum Tech.
Chapters
28
Reading time
~ 25h
Level
Professional
Language
English
Edition
2025
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Chapters & sections

28 chapters - 123 sections
Chapter 01

Chapter 1: Introduction to Data Analysis and Python

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11.1 Importance of Data Analysis12m21.2 Role of Python in Data Analysis12m31.3 Overview of the Data Analysis Process12m5Chapter 1 Conclusion12m4Practical Exercises for Chapter 112m
Chapter 02

Chapter 2: Getting Started with Python

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12.1 Installing Python12m22.2 Your First Python Program12m32.3 Variables and Data Types12m5Chapter 2 Conclusion12m4Practical Exercises for Chapter 212m
Chapter 03

Quiz for Part I: Setting the Stage

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3Answer Key12m1Multiple Choice Questions12m2True or False Questions12m
Chapter 04

Chapter 3: Basic Python Programming

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13.1 Control Structures12m23.2 Functions and Modules12m33.3 Python Scripting12m83Chapter 3 Conclusion12m109Practical Exercises Chapter 312m
Chapter 05

Chapter 4: Setting Up Your Data Analysis Environment

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14.1 Installing Anaconda12m24.2 Jupyter Notebook Basics12m34.3 Git for Version Control12m5Chapter 4 Conclusion12m4Practical Exercises Chapter 412m
Chapter 06

Quiz for Part II: Python Basics for Data Analysis

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3Answer Key:12m1Multiple-Choice Questions12m2True/False Questions12m
Chapter 07

Chapter 5: NumPy Fundamentals

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15.1 Arrays and Matrices12m25.2 Basic Operations12m35.3 Advanced NumPy Functions12m5Chapter 5 Conclusion12m4Practical Exercises for Chapter 512m
Chapter 08

Chapter 6: Data Manipulation with Pandas

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16.1 DataFrames and Series12m36.2 Data Wrangling12m26.2 Data Wrangling12m46.4 Real-World Examples: Challenges and Pitfalls in Handling Missing Data12m6Chapter 6 Conclusion12m5Practical Exercises Chapter 612m
Chapter 09

Chapter 7: Data Visualization with Matplotlib and Seaborn

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17.1 Basic Plotting with Matplotlib12m27.2 Advanced Visualizations12m37.3 Introduction to Seaborn12m5Chapter 7 Conclusion12m4Practical Exercises - Chapter 712m
Chapter 10

Chapter 8: Understanding EDA

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18.1 Importance of EDA12m28.2 Types of Data12m38.3 Descriptive Statistics12m5Chapter 8 Conclusion12m4Practical Exercises for Chapter 812m
Chapter 11

Quiz for Part III: Core Libraries for Data Analysis

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4Answer Key12m1Chapter 5: NumPy Fundamentals12m2Chapter 6: Data Manipulation with Pandas12m3Chapter 7: Data Visualization with Matplotlib and Seaborn12m
Chapter 12

Chapter 9: Data Preprocessing

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19.1 Data Cleaning12m29.2 Feature Engineering12m39.3 Data Transformation12m5Chapter 9 Conclusion12m4Practical Exercises: Chapter 912m
Chapter 13

Chapter 10: Visual Exploratory Data Analysis

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110.1 Univariate Analysis12m210.2 Bivariate Analysis12m310.3 Multivariate Analysis12m5Chapter 10 Conclusion12m4Practical Exercises Chapter 1012m
Chapter 14

Quiz for Part IV: Exploratory Data Analysis (EDA)

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2Answers12m1Questions12m
Chapter 15

Project 1: Analyzing Customer Reviews

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11.1 Data Collection12m21.2: Data Cleaning12m31.3: Data Visualization12m41.4: Basic Sentiment Analysis12m
Chapter 16

Chapter 11: Probability Theory

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111.1 Basic Concepts12m211.2: Probability Distributions12m311.3: Specialized Probability Distributions12m411.4 Bayesian Theory12m6Chapter 11 Conclusion12m5Practical Exercises for Chapter 1112m
Chapter 17

Chapter 12: Hypothesis Testing

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112.1 Null and Alternative Hypotheses12m212.2 t-test and p-values12m312.3 ANOVA (Analysis of Variance)12m5Chapter 12 Conclusion12m4Practical Exercises Chapter 1212m
Chapter 18

Quiz for Part V: Statistical Foundations

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3Answers12m1Chapter 11: Probability Theory12m2Chapter 12: Hypothesis Testing12m
Chapter 19

Chapter 13: Introduction to Machine Learning

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113.1 Types of Machine Learning12m213.2 Basic Algorithms12m313.3 Model Evaluation12m5Chapter 13 Conclusion12m4Practical Exercises Chapter 1312m
Chapter 20

Chapter 14: Supervised Learning

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114.1 Linear Regression12m214.2 Types of Classification Algorithms12m314.3 Decision Trees12m5Chapter Conclusion12m4Practical Exercises Chapter 1412m
Chapter 21

Chapter 15: Unsupervised Learning

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115.1 Clustering12m215.2 Principal Component Analysis (PCA)12m315.3 Anomaly Detection12m5Chapter 15 Conclusion12m4Practical Exercises Chapter 1512m
Chapter 22

Quiz Part VI: Machine Learning Basics

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4Answer Key12m1Chapter 13: Introduction to Machine Learning12m2Chapter 14: Supervised Learning12m3Chapter 15: Unsupervised Learning12m
Chapter 23

Project 2: Predicting House Prices

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2Data Collection and Preprocessing12m3Feature Engineering12m4Model Building and Evaluation12m1Problem Statement12m
Chapter 24

Chapter 16: Case Study 1: Sales Data Analysis

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116.1 Problem Definition12m216.2 EDA and Visualization12m316.3 Predictive Modeling12m5Chapter 16 Conclusion12m4Practical Exercises: Sales Data Analysis12m
Chapter 25

Chapter 17: Case Study 2: Social Media Sentiment Analysis

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117.1 Data Collection12m217.2 Text Preprocessing12m317.3 Sentiment Analysis12m5Chapter 17 Conclusion12m4Practical Exercises12m
Chapter 26

Quiz Part VII: Case Studies

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3Answers of Quiz Part VII: Case Studies12m1Chapter 16: Case Study 1 - Sales Data Analysis12m2Chapter 17: Case Study 2 - Social Media Sentiment Analysis12m
Chapter 27

Project 3: Capstone Project: Building a Recommender System

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2Data Collection and Preprocessing12m4Evaluation and Deployment12m3Model Building12m1Problem Statement12m
Chapter 28

Chapter 18: Best Practices and Tips

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118.1 Code Organization12m218.2 Documentation12m