Rövid leírás:

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

Hosszú leírás:

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

Tartalomjegyzék:

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.