Data science essentials
Book - 2025
"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 c...ertification, 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"--
Saved in:
- Subjects
- Published
-
Hoboken, NJ :
John Wiley and Sons, Inc
[2025]
- Language
- English
- Main Author
- Physical Description
- vii, 182 pages : illustrations ; 22 cm
- Bibliography
- Includes index.
- ISBN
- 9781394297009
- 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 Naive 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 Stones 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