Kelly P. Vincent

A Friendly Guide to Data Science (eBook, PDF)

Everything You Should Know About the Hottest Field in Tech

• Format: PDF

Produktbeschreibung

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

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Produktdetails

• Verlag: Apress
• Seitenzahl: 884
• Erscheinungstermin: 26. Juni 2025
• Englisch
• ISBN-13: 9798868811692
• Artikelnr.: 74730703

Autorenporträt

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.

Inhaltsangabe

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.