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
Introduction to Data Cleaning
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- Data Quality: Concepts, Methodologies and Techniques, Carlo Batini, Monica Scannapieco, 2016 (Springer) DOI: 10.1007/978-3-319-27514-6 - This book provides a comprehensive academic foundation for understanding data quality problems and the various methodologies used for data quality management and improvement.
- Python for Data Analysis: Data Wrangling with pandas, NumPy, and IPython, Wes McKinney, 2022 (O'Reilly Media) - This practical guide, while using Python, effectively illustrates common data cleaning challenges and their general solutions, making it valuable for understanding the concepts of data preparation.