Sinan Ozdemir - Principles of Data Science

Sinan Ozdemir is a data scientist, startup founder, and educator living in the San Francisco Bay Area with his dog, Charlie; cat, Euclid; and bearded dragon, Fiero. He spent his academic career studying pure mathematics at Johns Hopkins University before transitioning to education. He spent several years conducting lectures on data science at Johns Hopkins University and at the General Assembly before founding his own start-up, Legion Analytics, which uses artificial intelligence and data science to power enterprise sales teams.

After completing the Fellowship at the Y Combinator accelerator, Sinan has spent most of his days working on his fast-growing company, while creating educational material for data science.

I would like to thank my parents and my sister for supporting me through life, and also, my various mentors, including Dr. Pam Sheff of Johns Hopkins University and Nathan Neal, the chapter adviser of my collegiate leadership fraternity, Sigma Chi.

Thank you to Packt Publishing for giving me this opportunity to share the principles of data science and my excitement for how this field will impact all of our lives in the coming years.

Samir Madhavan has over six years of rich data science experience in the industry and has also written a book called Mastering Python for Data Science . He started his career with Mindtree, where he was a part of the fraud detection algorithm team for the UID (Unique Identification) project, called Aadhar, which is the equivalent of a Social Security number for India. After this, he joined Flutura Decision Sciences and Analytics as the first employee, where he was part of the core team that helped the organization scale to an over a hundred members. As a part of Flutura, he helped establish big data and machine learning practice within Flutura and also helped out in business development. At present, he is leading the analytics team for a Boston-based pharma tech company called Zapprx, and is helping the firm to create data-driven products that will be sold to its customers.

Oleg Okun is a machine learning expert and an author/editor of four books, numerous journal articles, and conference papers. His career spans more than a quarter of a century. He was employed in both academia and industry in his mother country, Belarus, and abroad (Finland, Sweden, and Germany). His work experience includes document image analysis, fingerprint biometrics, bioinformatics, online/offline marketing analytics, and credit-scoring analytics.

He is interested in all aspects of distributed machine learning and the Internet of Things. Oleg currently lives and works in Hamburg, Germany.

I would like to express my deepest gratitude to my parents for everything that they have done for me.

The topic of this book is data science, which is a field of study and application that has been growing rapidly for the past several decades. As a growing field, it is gaining a lot of attention in both the media as well as in the job market. The United States recently appointed its first ever chief data scientist, DJ Patil. This move was modeled after tech companies who, honestly, only recently started hiring massive data teams. These skills are in high demand and their applications extend much further than today's job market.

This book will attempt to bridge the gap between math/programming/domain expertise. Most people today have expertise in at least one of these (maybe two), but proper data science requires a little bit of all three. We will dive into topics from all three areas and solve complex problems. We will clean, explore, and analyze data in order to derive scientific and accurate conclusions. Machine learning and deep learning techniques will be applied to solve complex data tasks.