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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
~ 12h
Level
Professional
Language
English
Edition
2025
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Chapters & sections

28 chapters - 123 sections

Chapter 1: Introduction to Data Analysis and Python

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1.1 Importance of Data Analysis13m1.2 Role of Python in Data Analysis21m1.3 Overview of the Data Analysis Process16mPractical Exercises for Chapter 13mChapter 1 Conclusion3m

Chapter 2: Getting Started with Python

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2.1 Installing Python4m2.2 Your First Python Program7m2.3 Variables and Data Types11mPractical Exercises for Chapter 22mChapter 2 Conclusion3m

Quiz for Part I: Setting the Stage

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Multiple Choice Questions1mTrue or False Questions1mAnswer Key1m

Chapter 3: Basic Python Programming

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3.1 Control Structures9m3.2 Functions and Modules11m3.3 Python Scripting10mPractical Exercises Chapter 33mChapter 3 Conclusion3m

Chapter 4: Setting Up Your Data Analysis Environment

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4.1 Installing Anaconda6m4.2 Jupyter Notebook Basics11m4.3 Git for Version Control9mPractical Exercises Chapter 42mChapter 4 Conclusion3m

Quiz for Part II: Python Basics for Data Analysis

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Multiple-Choice Questions1mTrue/False Questions1mAnswer Key:1m

Chapter 5: NumPy Fundamentals

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5.1 Arrays and Matrices12m5.2 Basic Operations8m5.3 Advanced NumPy Functions9mPractical Exercises for Chapter 52mChapter 5 Conclusion3m

Chapter 6: Data Manipulation with Pandas

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6.1 DataFrames and Series10m6.2 Data Wrangling10m6.3 Handling Missing Data10m6.4 Real-World Examples: Challenges and Pitfalls in Handling Missing Data5m6.5 Practical Exercises Chapter 6: Data Manipulation with Pandas2m6.6 Chapter 6 Conclusion of Data Manipulation with Pandas3m

Chapter 7: Data Visualization with Matplotlib and Seaborn

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7.1 Basic Plotting with Matplotlib10m7.2 Advanced Visualizations12m7.3 Introduction to Seaborn16mPractical Exercises - Chapter 72mChapter 7 Conclusion3m

Chapter 8: Understanding EDA

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8.1 Importance of EDA13m8.2 Types of Data13m8.3 Descriptive Statistics10mPractical Exercises for Chapter 82mChapter 8 Conclusion3m

Quiz for Part III: Core Libraries for Data Analysis

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Chapter 5: NumPy Fundamentals1mChapter 6: Data Manipulation with Pandas1mChapter 7: Data Visualization with Matplotlib and Seaborn1mAnswer Key1m

Chapter 9: Data Preprocessing

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9.1 Data Cleaning15m9.2 Feature Engineering13m9.3 Data Transformation9mPractical Exercises: Chapter 92mChapter 9 Conclusion3m

Chapter 10: Visual Exploratory Data Analysis

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10.1 Univariate Analysis11m10.2 Bivariate Analysis10m10.3 Multivariate Analysis11mPractical Exercises Chapter 102mChapter 10 Conclusion3m

Quiz for Part IV: Exploratory Data Analysis (EDA)

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Questions2mAnswers1m

Project 1: Analyzing Customer Reviews

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1.1 Data Collection5m1.2: Data Cleaning5m1.3: Data Visualization5m1.4: Basic Sentiment Analysis4m

Chapter 11: Probability Theory

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11.1 Basic Concepts9m11.2: Probability Distributions10m11.3: Specialized Probability Distributions8m11.4 Bayesian Theory7mPractical Exercises for Chapter 113mChapter 11 Conclusion3m

Chapter 12: Hypothesis Testing

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12.1 Null and Alternative Hypotheses11m12.2 t-test and p-values13m12.3 ANOVA (Analysis of Variance)11mPractical Exercises Chapter 122mChapter 12 Conclusion3m

Quiz for Part V: Statistical Foundations

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Chapter 11: Probability Theory1mChapter 12: Hypothesis Testing1mAnswers1m

Chapter 13: Introduction to Machine Learning

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13.1 Types of Machine Learning13m13.2 Basic Algorithms18m13.3 Model Evaluation16mPractical Exercises Chapter 132mChapter 13 Conclusion3m

Chapter 14: Supervised Learning

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14.1 Linear Regression12m14.2 Types of Classification Algorithms20m14.3 Decision Trees14mPractical Exercises Chapter 142mChapter Conclusion3m

Chapter 15: Unsupervised Learning

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15.1 Clustering16m15.2 Principal Component Analysis (PCA)12m15.3 Anomaly Detection12mPractical Exercises Chapter 153mChapter 15 Conclusion3m

Quiz Part VI: Machine Learning Basics

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Chapter 13: Introduction to Machine Learning1mChapter 14: Supervised Learning1mChapter 15: Unsupervised Learning1mAnswer Key1m

Project 2: Predicting House Prices

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Problem Statement4mData Collection and Preprocessing3mFeature Engineering3mModel Building and Evaluation3m

Chapter 16: Case Study 1: Sales Data Analysis

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16.1 Problem Definition3m16.2 EDA and Visualization3m16.3 Predictive Modeling4mPractical Exercises: Sales Data Analysis3mChapter 16 Conclusion3m

Chapter 17: Case Study 2: Social Media Sentiment Analysis

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17.1 Data Collection4m17.2 Text Preprocessing4m17.3 Sentiment Analysis5mPractical Exercises2mChapter 17 Conclusion3m

Quiz Part VII: Case Studies

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Chapter 16: Case Study 1 - Sales Data Analysis1mChapter 17: Case Study 2 - Social Media Sentiment Analysis1mAnswers of Quiz Part VII: Case Studies1m

Project 3: Capstone Project: Building a Recommender System

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Problem Statement3mData Collection and Preprocessing5mModel Building5mEvaluation and Deployment4m

Chapter 18: Best Practices and Tips

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18.1 Code Organization5m18.2 Documentation5m