Unlock the world of data science—no coding required.
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
Grasp foundational statistics 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
Who This Book is for
A wide range of readers who are curious about data science and eager to build a strong foundation. Perfect for undergraduates in the early semesters of their data science degrees, as it assumes no prior programming or industry experience. Professionals will find particular value in the real-world insights shared through practitioner interviews. Business leaders can use it to better understand what data science can do for them and how their teams are applying it. And for career changers, this book offers a welcoming entry point into the field—helping them explore the landscape before committing to more intensive learning paths like degrees or boot camps.
By:
Kelly P. Vincent
Imprint: APress
Country of Publication: Germany
Dimensions:
Height: 235mm,
Width: 155mm,
ISBN: 9798868811685
Series: Friendly Guides to Technology
Pages: 884
Publication Date: 27 June 2025
Audience:
Professional and scholarly
,
Undergraduate
Format: Paperback
Publisher's Status: Active
Part I: Foundations.- 1. What is Data, Really?.- 2. Figuring Stuff Out: Data Analysis.- 3. Coming to Complex Conclusions: Statistics and Actuarial Science.- 4. Bringing It into the 21st Century: Data Science.- 5. A Fresh Perspective: The New Data Analytics.- 6. What’s Fair and Right: Legal and Ethical Considerations.- 7. Keeping Everyone Safe: Data Security and Privacy.- PART II: Doing Data Science.- 8. Grasping the Big Picture: Domain Knowledge.- 9. Tools of the Trade: Python and R.- 10. Trying Not to Make a Mess: Data Collection and Storage.- 11. For the Preppers: Data Preparation.- 12. Ready for the Main Event: Feature Engineering, Selection, and Reduction.- 13. Not A Crystal Ball: Machine Learning.- 14. How’d We Do? Measuring the Performance of ML Techniques.- 15. Making the Computer Literate: Text and Speech Processing.- 16. This Ain’t Our First Rodeo: ML Applications.- 17. A New Kind of Storytelling: Data Visualization.- 18. When Size Matters: Scalability and the Cloud.- 19. Putting It All Together: A Data Science Project Map.- 20. Getting Your Hands Dirty: How to Get Involved in Data Science.- Part III: The Future.- 21. Pushing the Envelope: Cutting Edge Projects in Data Science and AI.- 22. Ever Optimistic: Problems Data Science Can Help Solve.- 23. What’s Fair and Right Again: Last Thoughts on Ethical Considerations.- 24. Is It Your Future?: Pursuing a Career in Data Science.- Appendix A.- Appendix B.
Kelly P. Vincent is a data nerd. As soon as they saw their first spreadsheet, they knew they had to fill it with data and figure out how to analyze it. After doing software engineering work in data science and natural language processing spaces, Kelly landed their dream job—data scientist—at a Fortune 500 company in 2017, before moving on in 2022 to another Fortune 500 company. They have specialized in the at-first-barely-used programming language Python for nearly 20 years. Kelly has a BA degree in Mathematical Sciences, an MSc degree in Speech and Language Processing, and an MS degree in Information Systems. Kelly is also in the Doctor of Technology program at Purdue University. They keep their skills up to date with continuing education. They have worked in many different industries that have given them a range of domain knowledge, including education, bioinformatics, microfinancing, B2B tech, and retail. Kelly hasn’t let their love of data and programming get in the way of their other love—writing. They’re a novelist in multiple genres and have won several awards for their novels. Kelly considered how they could combine writing and data science, and finally spotted an untapped market with the growth of undergraduate data science and analytics degrees.
