Master Data Analytics Hands-On by Solving Fascinating
Problems You’ll Actually Enjoy!
Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’s been nothing easy about learning data science–until now.
Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell’s Outliers: It teaches through a powerful narrative packed with unforgettable stories.
Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything’s software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you’ll always know why you’re doing what you’re doing.
You’ll master data science by answering fascinating questions, such as:
• Are religious individuals more or less likely to have extramarital affairs?
• Do attractive professors get better teaching evaluations?
• Does the higher price of cigarettes deter smoking?
• What determines housing prices more: lot size or the number of bedrooms?
• How do teenagers and older people differ in the way they use social media?
• Who is more likely to use online dating services?
• Why do some purchase iPhones and others Blackberry devices?
• Does the presence of children influence a family’s spending on alcohol?
For each problem, you’ll walk through defining your question and the answers you’ll need; exploring how
others have approached similar challenges; selecting your data and methods; generating your statistics;
organizing your report; and telling your story. Throughout, the focus is squarely on what matters most:
transforming data into insights that are clear, accurate, and can be acted upon.
Table of contents
- About This E-Book
- Title Page
- Copyright Page
- Praise for Getting Started with Data Science
- Dedication Page
- About the Author
Chapter 1. The Bazaar of Storytellers
- Data Science: The Sexiest Job in the 21st Century
- Storytelling at Google and Walmart
- Getting Started with Data Science
- What Makes Someone a Data Scientist?
- Beyond the Big Data Hype
- What’s Beyond This Book?
Chapter 2. Data in the 24/7 Connected World
- The Liberated Data: The Open Data
- The Caged Data
- Big Data Is Big News
- It’s Not the Size of Big Data; It’s What You Do with It
- Free Data as in Free Lunch
- Search-Based Internet Data
- Survey Data
Chapter 3. The Deliverable
- The Final Deliverable
- The Narrative
- Building Narratives with Data
Chapter 4. Serving Tables
- 2014: The Year of Soccer and Brazil
- Seeing Whether Beauty Pays
- Generating Output with Stata
- Chapter 5. Graphic Details
Chapter 6. Hypothetically Speaking
- Random Numbers and Probability Distributions
- Casino Royale: Roll the Dice
- Normal Distribution
- The Student Who Taught Everyone Else
- Statistical Distributions in Action
- Hypothetically Yours
- The Mean and Kind Differences
- Worked-Out Examples of Hypothesis Testing
- Exercises for Comparison of Means
- Regression for Hypothesis Testing
- Analysis of Variance
- Significantly Correlated
Chapter 7. Why Tall Parents Don’t Have Even Taller Children
- The Department of Obvious Conclusions
- Introducing Regression Models
- Regression in Action
- Advanced Topics
Chapter 8. To Be or Not to Be
- To Smoke or Not to Smoke: That Is the Question
- Exploratory Data Analysis
- What Makes People Smoke: Asking Regression for Answers
- The Logit Model
- Interpreting Odds in a Logit Model
- Probit Model
- Estimating Logit Models for Grouped Data
- Using SPSS to Explore the Smoking Data Set
Chapter 9. Categorically Speaking About Categorical Data
- What Is Categorical Data?
- Analyzing Categorical Data
- Econometric Models of Binomial Data
- How I Met Your Mother? Analyzing Survey Data
- Multinomial Logit Models
- Conditional Logit Models
Chapter 10. Spatial Data Analytics
- Fundamentals of GIS
- GIS Platforms
- GIS Applications in Business Research
- Spatial Analysis of Urban Challenges
- Adding Spatial Analytics to Data Science
- Race and Space in Chicago
Chapter 11. Doing Serious Time with Time Series
- Introducing Time Series Data and How to Visualize It
- How Is Time Series Data Different?
- Starting with Basic Regression Models
- What Is Wrong with Using OLS Models for Time Series Data?
- Time Series Econometrics
- Econometric Models for Time Series Data
- Applying Time Series Tools to Housing Construction
- Estimating Time Series Models to Forecast New Housing Construction
Chapter 12. Data Mining for Gold
- Can Cheating on Your Spouse Kill You?
- Data Mining: An Introduction
- Seven Steps Down the Data Mine
Rattle Your Data
- What Does Religiosity Have to Do with Extramarital Affairs?
- The Principal Components of an Extramarital Affair
- Will It Rain Tomorrow? Using PCA For Weather Forecasting
- Do Men Have More Affairs Than Females?
- Two Kinds of People: Those Who Have Affairs, and Those Who Don’t
- Models to Mine Data with Rattle
- Code Snippets
- Title: Getting Started with Data Science: Making Sense of Data with Analytics
- Release date: December 2015
- Publisher(s): IBM Press
- ISBN: 9780133991246
You might also like
Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data
Data Science and Big Data Analytics is about harnessing the power of data for new insights. …
Fundamentals of Data Visualization
Effective visualization is the best way to communicate information from the increasingly large and complex datasets …
SQL for Data Analysis
With the explosion of data, computing power, and cloud data warehouses, SQL has become an even …
Python Data Science Handbook, 2nd Edition
Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, …