What is data and how does it fit into data science? What does the field of data science cover? What is data analysis and what skills are involved? What does data analytics refer to in the context of data analysis and data science?
Data science involves far more than pulling data out of a database and running machine learning. This book teaches you what data science can and cannot do. You also will learn the importance of ethics, security, and privacy considerations. And you will understand the many steps in a data science project and how the project life cycle works.
Data science is an important field that’s here to stay, especially as artificial intelligence (AI) and data become part of the everyday conversation in modern society for both their positive and negative impacts. This book’s focus on laying strong foundations makes it highly accessible to anyone interested in taking part in the data science revolution, even if they don’t yet have programming or business experience. It’s perfect for undergraduate and graduate students in data science programs as well as for business leaders and potential career-changers in need of an inviting way into the field.
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
Who This Book is for
Undergraduates in the early semesters of their data science degrees (as it assumes no industry or programming experience); professionals (the practitioner interviews will be helpful); business leaders who want to understand what data science can do for them and the data science work being done by their teams; and career changers who want to get a good foundational understanding of the field before committing to other learning paths such as degrees or boot camps
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.
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