Data Science Essentials For Dummies

Title Page....2 Copyright Page....3 Table of Contents....4 Introduction....10 About This Book....11 Foolish Assumptions....12 Icons Used in This Book....12 Where to Go from Here....13 Chapter 1 Wrapping Your Head Around Data Science....14 Seeing Who Can Make Use of Data Science....15 Inspecting the Pieces of the Data Science Puzzle....17 Collecting, querying, and consuming data....18 Applying mathematical modeling to data science tasks....20 Deriving insights from statistical methods....20 Coding, coding, coding — it’s just part of the game....21 Applying data science to a subject area....21 Communicating data insights....23 Chapter 2 Tapping into Critical Aspects of Data Engineering....24 Defining the Three Vs....24 Grappling with data volume....25 Handling data velocity....25 Dealing with data variety....26 Identifying Important Data Sources....27 Grasping the Differences among Data Approaches....27 Defining data science....28 Defining machine learning engineering....29 Defining data engineering....29 Comparing machine learning engineers, data scientists, and data engineers....30 Storing and Processing Data for Data Science....31 Storing data and doing data science directly in the cloud....31 Using serverless computing to execute data science....32 Containerizing predictive applications within Kubernetes....33 Sizing up popular cloud-warehouse solutions....34 Introducing NoSQL databases....35 Processing data in real-time....36 Recognizing the Impact of Generative AI....36 The reshaping of data engineering....37 Tools and frameworks for supporting AI workloads....37 Chapter 3 Using a Machine to Learn from Data....38 Defining Machine Learning and Its Processes....38 Walking through the steps of the machine learning process....39 Becoming familiar with machine learning terms....39 Considering Learning Styles....40 Learning with supervised algorithms....40 Learning with unsupervised algorithms....41 Learning with reinforcement....41 Seeing What You Can Do....41 Selecting algorithms based on function....42 Generating real-time analytics with Spark....45 Chapter 4 Math, Probability, and Statistical Modeling....48 Exploring Probability and Inferential Statistics....49 Probability distributions....51 Conditional probability with Naïve Bayes....53 Quantifying Correlation....54 Calculating correlation with Pearson’s r....54 Ranking variable pairs using Spearman’s rank correlation....56 Reducing Data Dimensionality with Linear Algebra....57 Decomposing data to reduce dimensionality....57 Reducing dimensionality with factor analysis....61 Decreasing dimensionality and removing outliers with PCA....62 Modeling Decisions with Multiple Criteria Decision-Making....63 Turning to traditional MCDM....64 Focusing on fuzzy MCDM....66 Introducing Regression Methods....66 Linear regression....66 Logistic regression....68 Ordinary least squares regression methods....69 Detecting Outliers....69 Analyzing extreme values....69 Detecting outliers with univariate analysis....70 Detecting outliers with multivariate analysis....71 Introducing Time Series Analysis....73 Identifying patterns in time series....73 Modeling univariate time series data....74 Chapter 5 Grouping Your Way into Accurate Predictions....76 Starting with Clustering Basics....77 Getting to know clustering algorithms....78 Examining clustering similarity metrics....80 Identifying Clusters in Your Data....81 Clustering with the k-means algorithm....81 Estimating clusters with kernel density estimation....83 Clustering with hierarchical algorithms....84 Dabbling in the DBScan neighborhood....86 Categorizing Data with Decision Tree and Random Forest Algorithms....88 Drawing a Line between Clustering and Classification....89 Introducing instance-based learning classifiers....90 Getting to know classification algorithms....90 Making Sense of Data with Nearest Neighbor Analysis....93 Classifying Data with Average Nearest Neighbor Algorithms....95 Classifying with K-Nearest Neighbor Algorithms....98 Understanding how the k-nearest neighbor algorithm works....99 Knowing when to use the k-nearest neighbor algorithm....100 Exploring common applications of k-nearest neighbor algorithms....101 Solving Real-World Problems with Nearest Neighbor Algorithms....101 Seeing k-nearest neighbor algorithms in action....101 Seeing average nearest neighbor algorithms in action....102 Chapter 6 Coding Up Data Insights and Decision Engines....104 Seeing Where Python Fits into Your Data Science Strategy....104 Using Python for Data Science....105 Sorting out the various Python data types....107 Numbers in Python....108 Strings in Python....108 Lists in Python....109 Tuples in Python....110 Sets in Python....110 Dictionaries in Python....110 Putting loops to good use in Python....110 Having fun with functions....112 Keeping cool with classes....113 Checking out some useful Python libraries....116 Saying hello to the NumPy library....116 Getting up close and personal with the SciPy library....119 Peeking into the pandas offering....120 Bonding with Matplotlib for data visualization....120 Learning from data with scikit-learn....122 Chapter 7 Generating Insights with Software Applications....124 Choosing the Best Tools for Your Data Science Strategy....125 Getting a Handle on SQL and Relational Databases....127 Investing Some Effort into Database Design....132 Defining data types....132 Designing constraints properly....133 Normalizing your database....133 Narrowing the Focus with SQL Functions....136 Making Life Easier with Excel....140 Using Excel to quickly get to know your data....141 Filtering in Excel....141 Using conditional formatting....143 Excel charting to visually identify outliers and trends....144 Reformatting and summarizing with PivotTables....146 Automating Excel tasks with macros....148 Chapter 8 Telling Powerful Stories with Data....152 Data Visualizations: The Big Three....153 Data storytelling for decision-makers....154 Data showcasing for analysts....154 Designing data art for activists....155 Designing to Meet the Needs of Your Target Audience....155 Step 1: Brainstorm (All about Eve)....156 Step 2: Define the purpose....157 Step 3: Choose the most functional visualization type for your purpose....158 Picking the Most Appropriate Design Style....159 Inducing a calculating, exacting response....159 Eliciting a strong emotional response....160 Selecting the Appropriate Data Graphic Type....161 Standard chart graphics....163 Comparative graphics....166 Statistical plots....170 Topology structures....171 Spatial plots and maps....173 Testing Data Graphics....176 Adding Context....177 Creating context with data....178 Creating context with annotations....178 Creating context with graphical elements....178 Chapter 9 Ten Free or Low-Cost Data Science Libraries and Platforms....180 Scraping the Web with Beautiful Soup....180 Wrangling Data with pandas....181 Visualizing Data with Looker Studio....181 Machine Learning with scikit-learn....181 Creating Interactive Dashboards with Streamlit....182 Doing Geospatial Data Visualization with Kepler.gl....182 Making Charts with Tableau Public....182 Doing Web-Based Data Visualization with RAWGraphs....183 Making Cool Infographics with Infogram....183 Making Cool Infographics with Canva....183 Index....184 EULA....194