Data Science For Dummies

Monetize your company's data and data science expertise without spending a fortune on hiring independent strategy consultants to help

What if there was one simple, clear process for ensuring that all your company's data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that's most prime for achieving profitability while also moving your company closer to its business vision? There is.

Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework - A simple, proven process for leading profit-forming data science projects.

Not sure what data science is yet? Don't worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you're already a data science expert? Then you really won't want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book.

Data Science For Dummies demonstrates:

• The only process you'll ever need to lead profitable data science projects

• Secret, reverse-engineered data monetization tactics that no one's talking about

• The shocking truth about how simple natural language processing can be

• How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise

Whether you're new to the data science field or already a decade in, you're sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company's data by picking up your copy today.

Auteur

Lillian Pierson is the CEO of Data-Mania, where she supports data professionals in transforming into world-class leaders and entrepreneurs. She has trained well over one million individuals on the topics of AI and data science. Lillian has assisted global leaders in IT, government, media organizations, and nonprofits.

Contenu

Introduction 1

About This Book 3

Foolish Assumptions 3

Icons Used in This Book 4

Beyond the Book 4

Where to Go from Here 4

Part 1: Getting Started with Data Science 5

Chapter 1: Wrapping Your Head Around Data Science 7

Seeing Who Can Make Use of Data Science 8

Inspecting the Pieces of the Data Science Puzzle 10

Collecting, querying, and consuming data 11

Applying mathematical modeling to data science tasks 12

Deriving insights from statistical methods 12

Coding, coding, coding it's just part of the game 13

Applying data science to a subject area 13

Communicating data insights 14

Exploring Career Alternatives That Involve Data Science 15

The data implementer 16

The data leader 16

The data entrepreneur 17

Chapter 2: Tapping into Critical Aspects of Data Engineering 19

Defining Big Data and the Three Vs 19

Grappling with data volume 21

Handling data velocity 21

Dealing with data variety 22

Identifying Important Data Sources 23

Grasping the Differences among Data Approaches 24

Defining data science 25

Defining machine learning engineering 26

Defining data engineering 26

Comparing machine learning engineers, data scientists, and data engineers 27

Storing and Processing Data for Data Science 28

Storing data and doing data science directly in the cloud 28

Storing big data on-premise 32

Processing big data in real-time 35

Part 2: Using Data Science to Extract Meaning from Your Data 37

Chapter 3: Machine Learning Means Using a Machine to Learn from Data 39

Defining Machine Learning and Its Processes 40

Walking through the steps of the machine learning process 40

Becoming familiar with machine learning terms 41

Considering Learning Styles 42

Learning with supervised algorithms 42

Learning with unsupervised algorithms 43

Learning with reinforcement 43

Seeing What You Can Do 43

Selecting algorithms based on function 44

Using Spark to generate real-time big data analytics 48

Chapter 4: Math, Probability, and Statistical Modeling 51

Exploring Probability and Inferential Statistics 52

Probability distributions 53

Conditional probability with Naïve Bayes 55

Quantifying Correlation 56

Calculating correlation with Pearson's r 56

Ranking variable-pairs using Spearman's rank correlation 58

Reducing Data Dimensionality with Linear Algebra 59

Decomposing data to reduce dimensionality 59

Reducing dimensionality with factor analysis 63

Decreasing dimensionality and removing outliers with PCA 64

Modeling Decisions with Multiple Criteria Decision-Making 65

Turning to traditional MCDM 65

Focusing on fuzzy MCDM 67

Introducing Regression Methods 67

Linear regression 67

Logistic regression 69

Ordinary least squares (OLS) regression methods 70

Detecting Outliers 70

Analyzing extreme values 70

Detecting outliers with univariate analysis 71

Detecting outliers with multivariate analysis 73

Introducing Time Series Analysis 73

Identifying patterns in time series 74

Modeling univariate time series data 75

Chapter 5: Grouping Your Way into Accurate Predictions 77

Starting with Clustering Basics 78

Getting to know clustering algorithms 79

Examining clustering similarity metrics 81

Identifying Clusters in Your Data 82

Clustering with the k-means algorithm 82 Estimating clusters wit...