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 Modeling Concepts
Was this section helpful?
- An Introduction to Statistical Learning: With Applications in R, Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani, 2021 (Springer) - Excellent foundational text covering statistical modeling, inference, prediction, and various machine learning methods from an introductory perspective.
- CS229 Machine Learning Course Notes, Andrew Ng, TM, 2018 (Stanford University) - Provides comprehensive lecture notes covering fundamental machine learning concepts, including model definition, types, and applications in prediction and inference.
- Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, Foster Provost, Tom Fawcett, 2013 (O'Reilly Media) - Provides a framework for understanding data science, focusing on business applications and the purpose of models for inference and prediction.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Aurélien Géron, 2022 (O'Reilly Media) - Offers a practical introduction to machine learning concepts, defining models, their purpose, inputs, outputs, and various types with hands-on examples.