Vineet Raina - Building an Effective Data Science Practice: A Framework to Bootstrap and Manage a Successful Data Science Practice

Our experience working closely with technology leaders from diverse backgrounds to solve problems using data science and addressing the previously mentioned aspects while doing so gave rise to this book.

This book is primarily intended for technology leaders who are considering incubating data science in their organization or are in the early stages of their data science journey: this book is intended to act as a guide for you and your formative team. You would need an intuitive understanding of techniques/technologies of data science, on one side, and, on the other side, the skill to apply this understanding to achieve the business goals. Based on our experience, we believe that it is possible to inculcate this intuitive understanding and the data science thought process as well as the skill to apply it to business, without getting into coding or advanced mathematics. This book accordingly aims at imparting all these skills in an intuitive way a focus of our technical coverage in this book (including the examples given) is to make it easier to grasp the underlying concepts.

We also anticipate this book to be useful to the members of a data science team: you will broaden your awareness of the overall technical ecosystem required to create an end-to-end solution using data science. Also, you can find examples that indicate how the business goals influence the choice of techniques and technologies synergy between the business and data science is essential to apply data science effectively to the business.

Finally, this book especially Part 4 would also be useful to project managers who coordinate end-to-end projects that leverage data science. Similarly, technology evangelists interested in the areas of data and analytics, AI (artificial intelligence), etc., would find this book useful in broadening their horizons.

This book is organized into four parts as follows:

Part 1: Fundamentals (Chapters)

This part introduces data science and attempts to dispel the terminology chaos around data science. It discusses the applicability and usefulness of data science for business and how the data science culture depends on the business.

Part 2: Classes of Problems (Chapters)

After talking about the general benefits of data science for business in Part 1, this part intends to give a more concrete picture of the various classes of problems that can be solved using data science. It has one chapter dedicated to one class of problem each chapter establishes a business motive and then transforms that motive to a concrete data science problem and shows how it could be solved. This part is primarily intended to illustrate and inculcate the thought process that goes into mapping a business problem to a data science problem and the steps involved in solving it.

Part 3: Techniques and Technologies (Chapters)

The previous part demonstrates how solving data science problems effectively depends on a good intuitive understanding of various techniques belonging to the field of data science. This part attempts to impart this intuitive understanding of the basic principles of various techniques in data science. It also talks about the technologies (libraries, tools, etc.) that help you apply these techniques. This conceptual understanding is also important for you to understand which type of techniques/technologies could be more applicable to the data science culture in your organization.

Part 4: Building Teams and Executing Projects (Chapters)

After covering the business and technical aspects of data science, this concluding part discusses practical aspects that are important for doing data science effectively. It talks about the various skills essential for different roles in data science teams and how such teams are typically structured for effective execution. It also talks about the different types of data science projects that your data science team would work on and various aspects of ensuring data quality that are indispensable for success in data science. It concludes with some legal and regulatory aspects that are important for you to be aware of while working on data science projects, including the recent thrust toward explainable AI.