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 thats 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 books focus on laying strong foundations makes it highly accessible to anyone interested in taking part in the data science revolution, even if they dont yet have programming or business experience. Its 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.
Chapter 1: Working with Numbers: What Is Data,
Really?.
Chapter 2: Figuring Out Whats 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: Whats 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:
Howd 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 Aint
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.
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 jobdata scientistat 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 hasnt let their love of data and programming get in the way of their other lovewriting. Theyre 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.
