A Friendly Guide to Data Science : Everything You Should Know About the Hottest Field in Tech / by Kelly P. Vincent.

Format:

Book

Author/Creator:

Vincent, Kelly P.

Series:

Friendly Guides to Technology, 2731-9369

Language:

English

Subjects (All):

Big data.

Database management.

Physical Description:

1 online resource (712 pages)

Edition:

1st ed. 2025.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2025.

Summary:

Curious about data science but not sure where to start? This book is a beginner-friendly guide to what data science is and how people use it. It walks you through the essential topics—what data analysis involves, which skills are useful, and how terms like “data analytics” and “machine learning” connect—without getting too technical too fast. Data science isn’t just about crunching numbers, pulling data from a database, or running fancy algorithms. It’s about asking the right questions, understanding the process from start to finish, and knowing what’s possible (and what’s not). This book teaches you all of that, while also introducing important topics like ethics, privacy, and security—because working with data means thinking about people, too. Whether you're a student exploring new skills, a professional navigating data-driven decisions, or someone considering a career change, this book is your friendly gateway into the world of data science, one of today’s most exciting fields. No coding or programming experience? No problem. You'll build a solid foundation and gain the confidence to engage with data science concepts— just as AI and data become increasingly central to everyday life. What You Will Learn Know what foundational statistics is and how it matters in data analysis and data science Understand the data science project life cycle and how to manage a data science project Examine the ethics of working with data and its use in data analysis and data science Understand the foundations of data security and privacy Collect, store, prepare, visualize, and present data Identify the many types of machine learning and know how to gauge performance Prepare for and find a career in data science.

Contents:

Part I: Foundations

Chapter 1: Working with Numbers: What Is Data, Really?

Chapter 2: Figuring Out What’s Going on in the Data: Descriptive Statistics

Chapter 3: Setting Us Up for Success: The Inferential Statistics Framework and Experiments

Chapter 4: Coming to Complex Conclusions: Inferential Statistics and Statistical Testing

Chapter 5: Figuring Stuff Out: Data Analysis

Chapter 6: Bringing It into the 21st Century: Data Science

Chapter 7: A Fresh Perspective: The New Data Analytics

Chapter 8: Keeping Everyone Safe: Data Security and Privacy

Chapter 9: What’s Fair and Right: Ethical Considerations

Part II: Doing Data Science

Chapter 10: Grasping the Big Picture: Domain Knowledge

Chapter 11: Tools of the Trade: Python and R

Chapter 12: Trying Not to Make a Mess: Data Collection and Storage

Chapter 13: For the Preppers: Data Gathering and Preprocessing

Chapter 14: Ready for the Main Event: Feature Engineering, Selection, and Reduction

Chapter 15: Not a Crystal Ball: Machine Learning

Chapter 16: How’d We Do? Measuring the Performance of ML Techniques

Chapter 17: Making the Computer Literate: Text and Speech Processing

Chapter 18: A New Kind of Storytelling: Data Visualization and Presentation

Chapter 19: This Ain’t Our First Rodeo: ML Applications

Chapter 20: When Size Matters: Scalability and the Cloud

Chapter 21: Putting It All Together: Data Science Solution Management

Chapter 22: Errors in Judgment: Biases, Fallacies, and Paradoxes

Part III: The Future

Chapter 23: Getting Your Hands Dirty: How to Get Involved in Data Science

Chapter 24: Learning and Growing: Expanding Your Skillset and Knowledge

Chapter 25: Is It Your Future?: Pursuing a Career in Data Science

Appendix A.

Notes:

Includes index.

Description based on publisher supplied metadata and other sources.

ISBN:

979-88-6881-169-2

OCLC:

1525682782