Build a Career in Data Science teaches you what data science courses leave out: from how to land your first job to the lifecycle of a data science project and even how to become a manager. This is a true how-to on obtaining and then navigating a data science career--filled with real stories from data scientists. This podcast is an extension of the similarly named book: Build a Career in Data Science.
Chapter 4: Building a Portfolio
Perhaps the most common piece of advice for aspiring data scientists is to make a project portfolio. Despite this, so few data scientists do so! In this episode, we discuss what exactly a portfolio is, the benefits, and the common reasons people don’t do it and how to overcome them. Spoiler: it's just as much psychological as it is about time and skills.
Chapter 3: Getting the Skills
It seems there are so many “required” skills for a data science job—how can someone possibly learn them? In this episode, we discuss four possible ways to do so: a formal degree program, a boot camp, learning on the job, and teaching yourself. We also share our own very different backgrounds: Jacqueline's math master's and engineering PhD versus Emily's statistics minor, master's in organizational behavior, and a boot camp.
Chapter 2: Data Science Companies
While the popular image of a data scientist is one solving cutting-edge problems at a large tech company, data scientists work in every type of organization. In this episode, we talk through five company archetypes, from small start-ups to government contractors to traditional retail companies. We weigh the pros and cons of working at each and debate the career changing question of: "should you be a company's first data scientist?"
Chapter 1: What is Data Science?
What actually *is* data science, and what does a data scientist do? What kind of backgrounds do data scientists come from and what skills do you need to be one? In this episode we start with the basics—declaring once and for all what is data science anyway and exploring how the hype of the field matches reality. We explore the three main areas of data science - analytics, decision science, and machine learning - and help you figure out which is best for YOU.