Introduction to Data Science
Chapter 1: Getting Started with Data Science
Defining Data Science
Why Data Science Matters
The Role of a Data Scientist
Essential Skills Overview
Common Tools in Data Science
Quiz for Chapter 1
Chapter 2: Understanding Data Fundamentals
What Constitutes Data?
Structured vs Unstructured Data
Quantitative vs Qualitative Data
Common Data Formats
Understanding Data Attributes and Features
Introduction to Metadata
Quiz for Chapter 2
Chapter 3: The Data Science Process
Overview of the Data Science Workflow
Defining the Problem or Question
Data Acquisition Approaches
Data Preparation Overview
Understanding Exploratory Data Analysis
Introduction to Modeling Concepts
Communicating Findings
Quiz for Chapter 3
Chapter 4: Gathering and Preparing Data
Identifying Data Sources
Importing Data
Introduction to Data Cleaning
Handling Missing Values
Identifying Potential Outliers
Basic Data Transformation Needs
Hands-on Practical: Simple Data Loading
Quiz for Chapter 4
Chapter 5: Performing Basic Data Analysis
Starting Exploratory Data Analysis (EDA)
Calculating Summary Statistics
Measuring Data Spread
Understanding Frequency Distributions
Distinguishing Correlation and Causation
Introduction to Hypothesis Concepts
Hands-on Practical: Calculating Basic Statistics
Quiz for Chapter 5
Chapter 6: Introduction to Data Visualization
The Purpose of Visualizing Data
Common Chart Types Explained
Choosing Appropriate Charts
Principles of Effective Visualization
Overview of Visualization Tools
Hands-on Practical: Creating Basic Charts
Quiz for Chapter 6
Handling Missing Values
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- Statistical Analysis with Missing Data, Roderick J. A. Little, Donald B. Rubin, 2019 Vol. 793 (John Wiley & Sons) DOI: 10.1002/9781119482260 - This text details the theoretical underpinnings and methodologies for handling missing data, covering various missing data mechanisms and imputation techniques.
- Python for Data Analysis, Wes McKinney, 2022 (O'Reilly Media) - A practical guide to data manipulation in Python using pandas, essential for identifying, inspecting, and managing missing values in datasets.
- Imputation of missing values, scikit-learn developers, 2024 - Official documentation describing scikit-learn's tools for handling missing values, including
SimpleImputerand other imputation strategies. - Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Aurélien Géron, 2022 (O'Reilly Media) - This book covers data preprocessing methods for machine learning pipelines, providing examples of how to address missing data effectively.