Роман с Data Science. Как монетизировать большие данные

by Roman Zykov

Как выжать все из своих данных? Как принимать решения на основе данных? Как организовать анализ данных (data science) внутри компании? Кого нанять аналитиком? Как довести проекты машинного обучения (machine learning) и искусственного интеллекта до топового уровня? На эти и многие другие вопросы Роман Зыков знает ответ, потому что занимается анализом данных почти двадцать лет. В послужном списке Романа - создание с нуля собственной компании с офисами в Европе и Южной Америке, ставшей лидером по применению искусственного интеллекта (AI) на российском рынке. Кроме того, автор книги создал с нуля аналитику в Ozon.
Эта книга предназначена для думающих читателей, которые хотят попробовать свои силы в области анализа данных и создавать сервисы на их основе. Она будет вам полезна, если вы менеджер, который хочет ставить задачи аналитике и управлять ею. Если вы инвестор, с ней вам будет легче понять потенциал стартапа. Те, кто "пилит" свой стартап, найдут здесь рекомендации, как выбрать подходящие технологии и набрать команду. А начинающим специалистам книга поможет расширить кругозор и начать применять практики, о которых они раньше не задумывались, и это выделит их среди профессионалов такой непростой и изменчивой области.

Genres: Nonfiction, Business

320 pages, Paperback

First published August 16, 2021

About the author

- 20 years of experience in data analysis, holds a master’s degree in applied mathematics and physics.
- Founded Retail Rocket, the leading Russian provider of e-commerce recommendation systems (SaaS) with offices in Europe and South America.
- Created analytics from scratch for the Russian online retailer Ozon.ru (worth $10 billion Nasdaq).

Roman Zykov was born in 1981. After completing his undergraduate studies in 2004, Roman went on to earn a master’s in Applied Physics and Mathematics at the Moscow Institute of Physics and Technology (MIPT).

Roman started his career in data science in 2002 as a technical consultant at StatSoft Russia– the Russian office of the U.S. developer of the STATISTICA statistical data analysis software. In 2004, he was hired as head of the analytical department of the Ozon.ru online store, where he created analytical systems from scratch, including web analytics, database analytics and management reporting, while also contributing to the recommendation system.

In 2009, he advised on a number of projects for Fast Lane Ventures investment fund and the gaming industry.

In 2010, Roman was hired to lead to analytics department of the online retailer Wikimart.ru.

In late 2012, he co-founded RetailRocket.ru, a marketing platform for online stores. The company is currently the undisputed market leader in Russia and successfully operates in Chile, the Netherlands, Spain and several other countries.

Roman ran the blog Analytics in Practice on the now defunct KPIs.ru from 2007 where he evangelized data analysis as it applies to business problems in ecommerce. He has spoken at numerous industry conferences, including the Russian Internet Forum, iMetrics and Gec 2014 (with Arkady Volozh of Yandex), as well as at business conferences in Dublin and London, the U.S. Embassy (American Center in Moscow), and 10 About the Author Sberbank University. He has also published in PwC Technology Forecast, ToWave, Vedomosti and Sekret firmy.

In 2016, Roman has talked about on hypothesis testing at MIT in Boston (ACM RecSys).

In 2020, he was nominated for a CDO Award.

Reviews

[Tammy Cook · Rating 4 out of 5]

This book is about data analysis. It is meant to be used by those who are considering a career in the field or implementing a data collection process in their business. Although it contains a good deal of jargon, I found it to be a great overview of the field and relatively easy to follow. I was particularly impressed with the recognition of ethical concerns in data science, because it is increasingly being used as a political weapon. We are less likely to be negatively influenced by manipulated data when we understand how it should be collected and used.

[Dwayne Roberts · Rating 4 out of 5]

An excellent source for persons interested in data analysis, machine learning, etc.

[Aika · Rating 5 out of 5]

Because I'm a native Russian speaker, I read this book in Russian. However, it is also available in English.

As a data scientist, this book was valuable and insightful for me. Currently, I am involved in developing two data-driven solutions in my company. Therefore, I was curious about creating a product, which will solve the problems of our customers and bring them value. I am glad that this book could answer some of my questions and helped me to navigate in asking the right questions in the product developing stage. Moreover, the book highlights what is crucial when you work on developing a data-driven product. Apart from that, I have learned some valuable advice on building a career in data science: from negotiating salary to training machine learning models. So I would recommend this book to data scientists at different stages in their careers. I believe everyone can find something valuable and insightful.

[Ruslan Halaunia · Rating 4 out of 5]

Книга хороша, но ее нужно изучать в формате книги. Я слушал в формате аудиобука, и было довольно прикольно слышать фразы а-ля: "...и мы строим график, изображенный на рисунке 10". А что там, собственно, нарисовано, непонятно:) Ну и в целом, были неясные моменты, которые следовало бы перегуглить пару раз, мб несколько раз перечитать. Но не в формате аудиобука...

[Aleksey Vishnev · Rating 5 out of 5]

An excellent and very useful book. Primarily for managers who want to create an analytics department in their company, but don't know where to start and what to expect.

I sincerely recommend it to professionals, managers, and startup owners.

[Vitaliy K · Rating 5 out of 5]

One of the best books I’ve read recently. It covers, in a very clear way, everything the starting data analyst should take into consideration, including the hiring process and what tools to master.
I had so many insights reading this book, huge respect and thanks to the author!

[biblioteca · Rating 3 out of 5]

I think this is a good book to junior data analysis, have interesting commentary on system recommend, and several source to junior data analysis have an idea how to grow up his career.