Data Science

Master the concepts and strategies underlying success and progress in data science.
From the author of the bestsellers, Data Scientist and Julia for Data Science, this book covers four foundational areas of data science. The first area is the data science pipeline including methodologies and the data scientist's toolbox. The second are essential practices needed in understanding the data including questions and hypotheses. The third are pitfalls to avoid in the data science process. The fourth is an awareness of future trends and how modern technologies like Artificial Intelligence (AI) fit into the data science framework.
The following chapters cover these four foundational areas:

Chapter 1 - What Is Data Science?

Chapter 2 - The Data Science Pipeline

Chapter 3 - Data Science Methodologies

Chapter 4 - The Data Scientist's Toolbox

Chapter 5 - Questions to Ask and the Hypotheses They Are Based On

Chapter 6 - Data Science Experiments and Evaluation of Their Results

Chapter 7 - Sensitivity Analysis of Experiment Conclusions

Chapter 8 - Programming Bugs

Chapter 9 - Mistakes Through the Data Science Process

Chapter 10 - Dealing with Bugs and Mistakes Effectively and Efficiently

Chapter 11 - The Role of Heuristics in Data Science

Chapter 12 - The Role of AI in Data Science

Chapter 13 - Data Science Ethics

Chapter 14 - Future Trends and How to Remain Relevant

Targeted towards data science learners of all levels, this book aims to help the reader go beyond data science techniques and obtain a more holistic and deeper understanding of what data science entails. With a focus on the problems data science tries to solve, this book challenges the reader to become a self-sufficient player in the field.