Data Science Essentials For Dummies

Introduction

About This Book

Foolish Assumptions

Icons Used in This Book

Where to Go from Here

Chapter 1: Wrapping Your Head Around Data Science

Seeing Who Can Make Use of Data Science

Inspecting the Pieces of the Data Science Puzzle

Collecting, querying, and consuming data

Applying mathematical modeling to data science tasks

Deriving insights from statistical methods

Coding, coding, coding — it’s just part of the game

Applying data science to a subject area

Communicating data insights

Chapter 2: Tapping into Critical Aspects of Data Engineering

Defining the Three Vs

Grappling with data volume

Handling data velocity

Dealing with data variety

Identifying Important Data Sources

Grasping the Differences among Data Approaches

Defining data science

Defining machine learning engineering

Defining data engineering

Comparing machine learning engineers, data scientists, and data engineers

Storing and Processing Data for Data Science

Storing data and doing data science directly in the cloud

Processing data in real-time

Recognizing the Impact of Generative AI

The reshaping of data engineering

Tools and frameworks for supporting AI workloads

Chapter 3: Using a Machine to Learn from Data

Defining Machine Learning and Its Processes

Walking through the steps of the machine learning process

Becoming familiar with machine learning terms

Considering Learning Styles

Learning with supervised algorithms

Learning with unsupervised algorithms

Learning with reinforcement

Seeing What You Can Do

Selecting algorithms based on function

Generating real-time analytics with Spark

Chapter 4: Math, Probability, and Statistical Modeling

Exploring Probability and Inferential Statistics

Probability distributions

Conditional probability with Naïve Bayes

Quantifying Correlation

Calculating correlation with Pearson’s r

Ranking variable pairs using Spearman’s rank correlation

Reducing Data Dimensionality with Linear Algebra

Decomposing data to reduce dimensionality

Reducing dimensionality with factor analysis

Decreasing dimensionality and removing outliers with PCA

Modeling Decisions with Multiple Criteria Decision-Making

Turning to traditional MCDM

Focusing on fuzzy MCDM

Introducing Regression Methods

Linear regression

Logistic regression

Ordinary least squares regression methods

Detecting Outliers

Analyzing extreme values

Detecting outliers with univariate analysis

Detecting outliers with multivariate analysis

Introducing Time Series Analysis

Identifying patterns in time series

Modeling univariate time series data

Chapter 5: Grouping Your Way into Accurate Predictions

Starting with Clustering Basics

Getting to know clustering algorithms

Examining clustering similarity metrics

Identifying Clusters in Your Data

Clustering with the k-means algorithm

Estimating clusters with kernel density estimation

Clustering with hierarchical algorithms

Dabbling in the DBScan neighborhood

Categorizing Data with Decision Tree and Random Forest Algorithms

Drawing a Line between Clustering and Classification

Introducing instance-based learning classifiers

Getting to know classification algorithms

Making Sense of Data with Nearest Neighbor Analysis

Classifying Data with Average Nearest Neighbor Algorithms

Classifying with K-Nearest Neighbor Algorithms

Understanding how the k-nearest neighbor algorithm works

Knowing when to use the k-nearest neighbor algorithm

Exploring common applications of k-nearest neighbor algorithms

Solving Real-World Problems with Nearest Neighbor Algorithms

Seeing k-nearest neighbor algorithms in action

Seeing average nearest neighbor algorithms in action

Chapter 6: Coding Up Data Insights and Decision Engines

Seeing Where Python Fits into Your Data Science Strategy

Using Python for Data Science

Sorting out the various Python data types

Putting loops to good use in Python

Having fun with functions

Keeping cool with classes

Checking out some useful Python libraries

Chapter 7: Generating Insights with Software Applications

Choosing the Best Tools for Your Data Science Strategy

Getting a Handle on SQL and Relational Databases

Investing Some Effort into Database Design

Defining data types

Designing constraints properly

Normalizing your database

Narrowing the Focus with SQL Functions

Making Life Easier with Excel

Using Excel to quickly get to know your data

Reformatting and summarizing with PivotTables

Automating Excel tasks with macros

Chapter 8: Telling Powerful Stories with Data

Data Visualizations: The Big Three

Data storytelling for decision-makers

Data showcasing for analysts

Designing data art for activists

Designing to Meet the Needs of Your Target Audience

Step 1: Brainstorm (All about Eve)

Step 2: Define the purpose

Step 3: Choose the most functional visualization type for your purpose

Picking the Most Appropriate Design Style

Inducing a calculating, exacting response

Eliciting a strong emotional response

Selecting the Appropriate Data Graphic Type

Standard chart graphics

Comparative graphics

Statistical plots

Topology structures

Spatial plots and maps

Testing Data Graphics

Adding Context

Creating context with data

Creating context with annotations

Creating context with graphical elements

Chapter 9: Ten Free or Low-Cost Data Science Libraries and Platforms

Scraping the Web with Beautiful Soup

Wrangling Data with pandas

Visualizing Data with Looker Studio

Machine Learning with scikit-learn

Creating Interactive Dashboards with Streamlit

Doing Geospatial Data Visualization with Kepler.gl

Making Charts with Tableau Public

Doing Web-Based Data Visualization with RAWGraphs

Making Cool Infographics with Infogram

Making Cool Infographics with Canva

Index