INTRODUCTION.................................................................................. 1
About This Book.................................................................................. 2
Foolish Assumptions............................................................................. 3
Icons Used in This Book........................................................................ 3
Where to Go from Here........................................................................ 4
CHAPTER 1: Wrapping Your Head Around Data Science....................... 5
Seeing Who Can Make Use of Data Science....................................... 6
Inspecting the Pieces of the Data Science Puzzle.............................. 8
Collecting, querying, and consuming data................................... 9
Applying mathematical modeling to data science tasks...............11
Deriving insights from statistical methods.................................11
Coding, coding, coding — it’s just part of the game....................12
Applying data science to a subject area......................................12
Communicating data insights.......................................................14
CHAPTER 2: Tapping into Critical Aspects
of Data Engineering..................................................................15
Defining the Three Vs....................................................................15
Grappling with data volume.........................................................16
Handling data velocity...................................................................16
Dealing with data variety..............................................................17
Identifying Important Data Sources..................................................18
Grasping the Differences among Data Approaches.......................18
Defining data science....................................................................19
Defining machine learning engineering......................................20
Defining data engineering............................................................20
Comparing machine learning engineers, data scientists,
and data engineers........................................................................21
Storing and Processing Data for Data Science................................22
Storing data and doing data science directly in the cloud........22
Processing data in real-time.........................................................27
Recognizing the Impact of Generative AI.........................................27
The reshaping of data engineering..............................................28
Tools and frameworks for supporting AI workloads.................28
iv Data Science Essentials For Dummies
CHAPTER 3: Using a Machine to Learn from Data.........................29
Defining Machine Learning and Its Processes.................................29
Walking through the steps of the machine
learning process.............................................................................30
Becoming familiar with machine learning terms.......................30
Considering Learning Styles...............................................................31
Learning with supervised algorithms..........................................31
Learning with unsupervised algorithms.....................................32
Learning with reinforcement........................................................32
Seeing What You Can Do....................................................................32
Selecting algorithms based on function.....................................33
Generating real-time analytics with Spark..................................36
CHAPTER 4: Math, Probability, and Statistical Modeling.........39
Exploring Probability and Inferential Statistics................................40
Probability distributions................................................................42
Conditional probability with Naïve Bayes...................................44
Quantifying Correlation......................................................................45
Calculating correlation with Pearson’s r......................................45
Ranking variable pairs using Spearman’s
rank correlation..............................................................................47
Reducing Data Dimensionality with Linear Algebra........................48
Decomposing data to reduce dimensionality............................48
Reducing dimensionality with factor analysis............................52
Decreasing dimensionality and removing
outliers with PCA............................................................................53
Modeling Decisions with Multiple Criteria Decision-Making.........54
Turning to traditional MCDM.......................................................55
Focusing on fuzzy MCDM..............................................................57
Introducing Regression Methods......................................................57
Linear regression...........................................................................57
Logistic regression.........................................................................59
Ordinary least squares regression methods..............................60
Detecting Outliers...............................................................................60
Analyzing extreme values.............................................................60
Detecting outliers with univariate analysis.................................61
Detecting outliers with multivariate analysis.............................62
Introducing Time Series Analysis......................................................64
Identifying patterns in time series...............................................64
Modeling univariate time series data..........................................65
Table of Contents v
CHAPTER 5: Grouping Your Way into Accurate
Predictions.......................................................................................67
Starting with Clustering Basics..........................................................68
Getting to know clustering algorithms........................................69
Examining clustering similarity metrics......................................71
Identifying Clusters in Your Data.......................................................72
Clustering with the k-means algorithm.......................................72
Estimating clusters with kernel density estimation...................74
Clustering with hierarchical algorithms......................................75
Dabbling in the DBScan neighborhood.......................................77
Categorizing Data with Decision Tree and Random
Forest Algorithms................................................................................79
Drawing a Line between Clustering and Classification...................80
Introducing instance-based learning classifiers.........................81
Getting to know classification algorithms...................................81
Making Sense of Data with Nearest Neighbor Analysis.................84
Classifying Data with Average Nearest Neighbor Algorithms........86
Classifying with K-Nearest Neighbor Algorithms............................89
Understanding how the k-nearest neighbor
algorithm works.............................................................................90
Knowing when to use the k-nearest neighbor algorithm.........91
Exploring common applications of k-nearest neighbor
algorithms.......................................................................................92
Solving Real-World Problems with Nearest
Neighbor Algorithms...........................................................................92
Seeing k-nearest neighbor algorithms in action........................92
Seeing average nearest neighbor algorithms in action............93
CHAPTER 6: Coding Up Data Insights and
Decision Engines..........................................................................95
Seeing Where Python Fits into Your Data Science Strategy...........95
Using Python for Data Science..........................................................96
Sorting out the various Python data types.................................98
Putting loops to good use in Python.........................................101
Having fun with functions...........................................................103
Keeping cool with classes...........................................................104
Checking out some useful Python libraries..............................107
vi Data Science Essentials For Dummies
CHAPTER 7: Generating Insights with Software
Applications..................................................................................115
Choosing the Best Tools for Your Data Science Strategy.............116
Getting a Handle on SQL and Relational Databases.....................118
Investing Some Effort into Database Design.................................123
Defining data types......................................................................123
Designing constraints properly..................................................124
Normalizing your database........................................................124
Narrowing the Focus with SQL Functions......................................127
Making Life Easier with Excel...........................................................131
Using Excel to quickly get to know your data...........................132
Reformatting and summarizing with PivotTables....................137
Automating Excel tasks with macros.........................................139
CHAPTER 8: Telling Powerful Stories with Data.............................143
Data Visualizations: The Big Three..................................................144
Data storytelling for decision-makers.......................................145
Data showcasing for analysts.....................................................145
Designing data art for activists...................................................146
Designing to Meet the Needs of Your Target Audience...............146
Step 1: Brainstorm (All about Eve).............................................147
Step 2: Define the purpose.........................................................148
Step 3: Choose the most functional visualization type
for your purpose..........................................................................149
Picking the Most Appropriate Design Style....................................150
Inducing a calculating, exacting response................................150
Eliciting a strong emotional response.......................................151
Selecting the Appropriate Data Graphic Type...............................152
Standard chart graphics..............................................................154
Comparative graphics.................................................................157
Statistical plots.............................................................................161
Topology structures.....................................................................162
Spatial plots and maps................................................................164
Testing Data Graphics.................................................................167
Adding Context.........................................................................168
Creating context with data.........................................................169
Creating context with annotations............................................169
Creating context with graphical elements................................169
Table of Contents vii
CHAPTER 9: Ten Free or Low-Cost Data Science
Libraries and Platforms.......................................................171
Scraping the Web with Beautiful Soup...........................................171
Wrangling Data with pandas............................................................172
Visualizing Data with Looker Studio................................................172
Machine Learning with scikit-learn.................................................172
Creating Interactive Dashboards with Streamlit...........................173
Doing Geospatial Data Visualization with Kepler.gl......................173
Making Charts with Tableau Public.................................................173
Doing Web-Based Data Visualization with RAWGraphs...............174
Making Cool Infographics with Infogram.......................................174
Making Cool Infographics with Canva............................................174
INDEX .....................................................................................175

 Feel confident navigating the fundamentals of data science

 Data Science Essentials For Dummies is a quick reference on the core concepts of the exploding and in-demand data science field, which involves data collection and working on dataset cleaning, processing, and visualization. This direct and accessible resource helps you brush up on key topics and is right to the point―eliminating review material, wordy explanations, and fluff―so you get what you need, fast.

• Strengthen your understanding of data science basics

• Review what you've already learned or pick up key skills

• Effectively work with data and provide accessible materials to others

• Jog your memory on the essentials as you work and get clear answers to your questions

 Perfect for supplementing classroom learning, reviewing for a certification, or staying knowledgeable on the job, Data Science Essentials For Dummies is a reliable reference that's great to keep on hand as an everyday desk reference.