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
Understanding Data Attributes and Features
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- An Introduction to Statistical Learning: With Applications in R, Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani, 2021 (Springer) - Foundational textbook defining variables, features, and data types crucial for understanding structured data and statistical modeling.
- Python for Data Analysis, Wes McKinney, 2022 (O'Reilly Media) - Practical guide that introduces data structures and fundamental concepts of attributes/features as columns in tabular data, often with discussion on data types.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Aurélien Géron, 2022 (O'Reilly Media) - Explains features in the context of machine learning, how they are used as inputs for models, and clarifies the relationship between attributes and features.
- CS229 Lecture Notes: Supervised Learning, Andrew Ng, 2023 Stanford University (Stanford University) - Introduces the concept of features in the context of machine learning, providing an academic definition and framework.