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CMSC320 Section Dr. Alam

CMSC320 Textbook

About the Book

Preface

Chapter 1 - Intro to Data Science

Introduction to Data Science
How Data Science Relates to AI, ML, Deep Learning, and Statistics
The Data Science Life Cycle
BONUS SECTION: Career Paths in Data Science and What You Need to Learn
Summary of the Chapter

Chapter 2 - Intro to Data Science

The Many Shapes of Data
How Is Data Organized or Stored?
Data Formats in Data Science
Where Does Data Come From?
Summary of the Chapter: The Point of All This

Chapter 3 - Experimental Design

Experimental Design
Key Ideas in Experimental Design
Methods for Collecting Data
Bias in Experiments: Placebo & Blinding
Summary of the Chapter

Chapter 4 - Pandas

Pandas: Turning Raw Data into Usable Data
What is Pandas?
A Beginner’s Working Model
Inspecting Data: First Contact
Filtering Data in Pandas
Applying Aggregation Functions Directly to a DataFrame
Exporting Pandas Data in Google Colab
Summary of the Chapter
Interactive Pandas Playground

Chapter 5 - SQL

Chapter 6: Mastering SQL for Data Science with Python
The “Big 6” Elements of a SQL Select Statement
Some More SQL Essentials
SQL JOINs: Combining Data from Multiple Tables
Summary of the Chapter
Interactive SQL Playground

Chapter 6 - Probability and Distributions

Why Probability Matters in Data Science
Sample Space and Events: Defining the World
Conditional Probability: Updating Beliefs With Context
Law of Total Probability
Conditional Independence
Expected Value: Summarizes Average Behavior
Probability Distribution: From Single Events to Patterns
Types of Probability Distributions and how it connects to Data Science
Statistical Distribution Explorer
Central Limit Theorem (Why Normal Appears Everywhere)
Summary of the Chapter
Mathematical Examples (with Solutions)

Chapter 7 - Descriptive Statistics

Descriptive Statistics: Understanding Data Before Modeling
The Four Pillars of Describing Data
(I). Measures of Location: Where Is the Center?
Pythagorean Means: Three Ways to Average
(II) Measures of Shape: Skewness, Modality, and Distribution Behavior
(III) Measures of Variability: How Spread Out Is the Data?
(IV) Correlation and Relationships Between Variables
Summary of the Chapter

Chapter 12 - Intro to ML

Introduction to Machine Learning
Section 5. Your First Algorithm: k-Nearest Neighbors (KNN)
Sectioin 5. Evaluation, Boundaries, and Generalization (Some more ML Concepts)
Distance Metrics and Variants in KNN
Interactive K-Nearest Neighbors

Chapter 14 - Decision Tree

Introduction: The Power of Simple Questions
Section 2: The Tree-Building Algorithm: A Greedy, Recursive Process
Information Gain (IG)
How Decision Trees Decide Which Feature to Split (Using Entropy and Information Gain)
Detailed Example 01: How Decision Trees Decide Which Feature to Split
Interactive Entropy and Information Gain

Chapter 17 - Clustering and PCA

Interactive K Means
Interactive PCA

Chapter 18. Neural Network

Chapter 18: Introduction to the Neural Network
18.1 Activation Functions
Interactive Activation Functions
Gradient Descent
18.2 Optimizers
Interactive Forward Propagation
Interactive Backward Propagation
Putting It All Together: How Neural Network Training Happens

Chapter 19 - Convolutional Neural Network

Introduction
Interactive CNN

Chapter 20 - NLPs

Introduction: What is Natural Language Processing?
Interactive N-Gram
Interactive Text Preprocessing
Interactive Text Frequency

Chapter 21 - Graph Theory

Interactive Topologies

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