Book Image

Managing Data Science

By : Kirill Dubovikov
Book Image

Managing Data Science

By: Kirill Dubovikov

Overview of this book

Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis.
Table of Contents (18 chapters)
Free Chapter
Section 1: What is Data Science?
Section 2: Building and Sustaining a Team
Section 3: Managing Various Data Science Projects
Section 4: Creating a Development Infrastructure

Introducing values and ethics into the interview

We often look at interviews from a one-sided perspective. We want to find a reliable team member who can do the job well. It is easy to forget that bad interviews can scare good candidates. More so, a constant flow of potential candidates make judgments about your company through the interviews. Bad interviews lead to bad publicity. The key to effective and smooth interviews is to think about the candidate experience.

If you have successfully defined honest requirements for the candidate, the next step is to think about the interview experience as a whole. To see a new teammate in the best light, the interview should look like a real working process, not like a graduate examination.

Consider how most technical interviews are conducted:

  • You go through a resume filter. The best people have PhDs and at least 5 years of (questionable...