Build a Career in Data Science

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Episodes of Build a Career in Data Science

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Stakeholders! The people who data scientists help by creating analyses and models. Stakeholders have so many needs, and keeping them satisfied is one of the most important parts of being a successful data scientist. In this episode Jacqueline and Emily discuss how to make your stakeholders happy with good communication and empathy.
Putting data science into production can mean a ton of things: from customer-facing models run millions of times a day to continuously live dashboards for stakeholders. But writing code for production and getting it to work can be intimidating for many data scientists, and lots of us have never tried. In this episode we talk about production, why it can be scary, and what you can do to get started! We also have a game that just might rip off a much more popular TV show.
An analysis is the work of taking data and turning it into a PowerPoint presentation (more or less), and in this episode we talk all about it. What makes an analysis sharp, how to sell data stories to stakeholders, and the useful tool of an "analysis plan" are all discussed. The episode ends with Emily giving Jacqueline an analysis game that tangentially relates to puppies.
Welcome to your new data science role! You just started a new data science job, maybe your first one, and wow there is a lot to do! In this episode, we cover what to expect in the first few months of the job and what to do if it’s not what you hoped. We also discuss how to handle the inevitable heartbreak as you find technical skeletons in the closet like production databases that are just csv files on a SharePoint.
Technically still just in time for the last of the holidays - a special bonus episode! Here we cover three small mini-topics. First, whether you should get a PhD to become a data scientist (spoiler alert: almost certainly not), delving deep into the culture of academia and our own PhD experiences. Then we share how we decide if we should learn a new topic/technology. Finally, we talk about how to design good data science interviews as a hiring manager. Do we advocate for white boarding, take-homes, and unstructured interviews? Listen to hear our hot takes!
Stop! Don’t just accept that coveted data science job offer right off the bat. Instead, listen to this episode where we discuss negotiating: what’s on the table, what salaries to expect at different levels of data scientist, what the heck RSUs and stock options are, and much more. By reflecting on what’s important to you and how to ask for it, you’ll start your new job in the best way. Includes stories from Jacqueline and Emily's pasts!
The interview: possibly the most nerve-wracking part of a data science job search. In this episode, we’ll help you prepare by covering the full interview process, including the different types of interviews, what questions you might be asked, and what the interviewers are looking for. And since interviewing is a two-way street, we also do a mock interview where Emily as the interviewee tries to see if there are red flags in a few jobs.
“You only get one chance at a first impression.” When applying for data science jobs, that’s usually your resume (and sometimes a cover letter). So how can you give your data science resume the polish it deserves? In this episode, we discuss what hiring managers and recruiters are looking for in a resume, how and why to customize it for different jobs, and weigh in on the always hotly contested: “how many pages should it be?”
Before you can get a data science job, you need to find one! In this episode, we discuss how to decode job descriptions, use your network, and find jobs that don’t need 3+ years of experience. We share how we’ve found our previous jobs and we do some on-the-spot judging of job posts that Emily found on LinkedIn.
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.
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.
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?"
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.
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Podcast Details

Created by
Jacqueline Nolis and Emily Robinson
Podcast Status
Sep 10th, 2020
Latest Episode
Feb 25th, 2021
Release Period
2 per month
Avg. Episode Length
About 1 hour

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