If you’re paired with Lauren Washington as your Thinkful mentor, consider yourself lucky. Lauren’s experience in the data science industry spans from a master’s degree in quantitative methods, to research for the CDC, to digital research at Nielsen, to working at Google! Now she balances her career as the Lead Data Scientist at SmartQED with her passion for mentoring future data scientists at Thinkful’s Online Data Science Bootcamp! Read on to learn about Lauren’s teaching style and her expectations for her students.
What were you up to before Thinkful? How did you get started in data science?
I have a bachelor's degree in Economics from Spelman College in Atlanta, Georgia. I actually wasn't sure what I was going to do with that degree until I went through an econometrics course and I realized that I really loved statistical research. My father is a Software Engineer, and he told me to get more serious about math, so I took Calc 1 - 3, Statistics, Linear Algebra, and Discrete Math. Then I went to graduate school in New York at Columbia University to get my master's degree in Quantitative Methods in Social Sciences. Recent graduates now call that “Applied Data Science.” I started my career in Chicago at the National Opinion Research Center at the University of Chicago, then went on to Neilson where I worked on digital research – using frequent shopper data from grocery stores and pharmacies to figure out who to serve ads to online (aka predictive modeling).
I moved to the Bay Area to work for Google Wallet, which is Google Pay now. I left Google two and a half years in as I found that a major pay discrepancy between what I was making at the company and my true market value. I quit and started my own company – a subscription box service that delivers 1-2 books a month on R, Python, data science, and personalized workbooks in each shipment.
At that time, I coded in R, but I didn’t know Python. So I took a break and went to General Assembly’ Part-Time Data Science Immersive. I also started working as an on-site liaison for datascience.com and started specializing in Natural Language Processing (NLP)
I then transitioned to SmartQED where now I'm the Lead Data Scientist and Machine Learning Developer. I started working as a Thinkful Mentor when I started at SmartQED.
Since you’ve had experience at an in-person bootcamp and an online bootcamp – what differences did you find between the two?
I actually went with General Assembly because I was aware of the name. At GA in 2016, I was part of a 30-person cohort and I felt like I never truly got my questions answered. I was actually upset that I didn't attend Thinkful, but they were not offering data science at the time. In a one-on-one mentorship at Thinkful – you're going to get your answers.
You have such a rich career in data science – what inspired you to become a mentor?
Before I left Google, I started teaching classes on Introduction to Data Science in R and Intro to Data Visualization in R to my colleagues – I had always wanted to be a professor. So when I saw an ad for Thinkful, I thought, ‘I could definitely do this’. As a mentor, I started by hosting workshops in my first week and everyone really appreciated them. I'm personally working with five students right now, but I think the cap is supposed to be three. I actually have a waitlist now, because whenever people come into my Q&A's or my workshops, they tell their program managers that I explain things in a way they understand.
What are your responsibilities as a mentor? How can a student expect to be paired with you?
Students have a Program Manager and a Mentor. As a Thinkful mentor, I get access to the curriculum and it's my responsibility to go through it before I start meeting with my students. Once I start working with a mentee, we do one-on-ones (I also allow people to shadow me before we get matched so they know what to expect).
Thinkful matches mentors and students – we pair people based on their background and what they want out of the bootcamp. I tend to get matched with students who are interested in the natural language processing specialization. I've also had quite a few students who have switched over to me just because they want their mentor to be a woman mentor. One thing to note: if you don't like your mentor, you go to your program manager and say that it isn't working, and they switch you to a new one. There's a lot of flexibility at Thinkful.
How do you work with your mentees? Are you teaching them the entire curriculum or are they learning on their own?
Students read the curriculum on their own and then I ask questions to see if they really understood what they read. I'll ask them to share their code with me on their screen, to see if they're coding in a proper way. If they have any errors, I can help them figure out how to get through them. I don't think it's really worth their time to come in and have me teach or lecture them. Students need to figure out how to use Google because that's what they're going to need to know when they're on their job, and I'm not there.
I'll always help students live with their coding, but I don't expect anyone to feel they should completely understand everything. As a Lead Data Scientist, I have a ton of bookmarks and resources that I use, and I immediately know which resources will help my students for each particular learning style. And I think students are really grateful for that. I'm always pointing them to different blogs or YouTube videos that explain things further.
What goes into the Thinkful Online Data Science curriculum?
There’s a two-week prep course, which covers Python, Pandas and NumPy, basic visualization and probability. To be admitted into the full-time bootcamp, you have to do a basic data analysis report and pass a technical evaluation.
During the full-time bootcamp, you’ll learn:
Then you go through your first mock interview where you're presented with a case study and you say how you would perform that experiment if you had that case in the real world. It’s pass or fail, and we don't easily pass people. Plenty of students have to retake the test or redo mock interviews.
How often are you meeting with your students throughout the course? Is there a set time you meet each week?
We meet with students twice a week, but they have full access to us whenever they have a question. Students know that they can find me on Slack. I don't know how every mentor is, but I do you give my all when it comes to helping my students. I have students who work part-time at a CVS or an Amazon fulfillment center and I'll meet them at 6:30am or on Saturday/Sunday to get their questions answered. It's a very flexible bootcamp, but you definitely get to set two hours with your mentor no matter what, during the week.
How do you communicate with your mentees throughout the course?
We have an internal system and twice a week we do video chats with our students. You can join and just observe or speak. There's a chat window on the side and you can share your screens as well. They can share their screens and I’ll help them troubleshoot their code and fix the error. In our workshops, there’s live learning on how to plot data and basic data science info. It's basically a video call and people can choose to join the stage, chat with me via microphone or by typing in the chat. Students can ask me any and all questions. And afterward, I sometimes share my personal workbook that I've created.
How are you able to balance being a mentor and your full-time role as a data scientist?
In the beginning, I thought it was a little bit out of hand, but I've managed to find a good balance. My boss is pretty flexible and actually loves that I work for Thinkful because I'm learning new things. I'm wise enough not to over-promise and under-deliver.
What do you do if a mentee is falling behind? How do Thinkful mentors assess student progress?
Thinkful has a dashboard on student goals, so if students are falling behind, it tells me what's going on. Every student’s goals are different and there are different milestones they need to hit. An example of a goal would be – go interview a data scientist that you found on LinkedIn. As mentors, we make notes after each of our sessions as well. At the end of each Chapter, students have drills. Students need to know how to build models five different ways, which is amazing because that teaches people what to do with models that go awry in production (something I also didn't know when I first started as a data scientist). At the end of each chapter, students have to submit a Github link with the assignment. Their Program Manager and I can assess all of their code before I meet with them. It’s expected that students spend a good 25 to 30 hours a week on learning at Thinkful.
As a mentor, are you able to contribute to the Thinkful curriculum?
Mentors have a whole Slack channel dedicated to updating the curriculum and Thinkful has a pretty quick turnaround. And for students, at the end of each lesson, there's a link for them to provide feedback on the lesson and whether or not it was easy for them to learn. Students grade the mentors every week through surveys. Thinkful is constantly using data to improve upon the program and curriculum.
Is there a certain type of student that does really well in Thinkful’s online data science program?
You have to be determined to switch careers and have your own goals – those are the students that do well in the bootcamp. The fastest learners already have some pivotal applied math background, are naturally creative, or really driven to make it through. There's no set cookie-cutter type. My most successful students tend to be a little bit older and a little more serious about their career in the data science course versus the web development course where people tend to be younger.
I've only had one student who I actually had to remove from my roster. They would meet me on video chat in their bed – it was obvious they were not very dedicated to the bootcamp.
On the other hand, I have a student who works as a full-time pharmacist and she'll randomly video call me from the pharmacy. I always think, ‘What are you doing? You're going to get fired!’ But she’s dedicated! If you're not passionate about a subject and you don't care about the data insights that you're getting, then data science is not for you. Data science is for you if you truly want to help to change the world.
What are some online resources or meetups that you would suggest for aspiring data science bootcampers?
Is there any other advice that you want to share with our readers about Thinkful?
In this day and age, you need to have an online presence showing what you're able to do. When you leave Thinkful, you have at least 30 to 40 different analytics repos in your GitHub. If you really want a bootcamp that's truly going to prepare you and give you an impressive visual resume of everything that you can do with machine learning, Thinkful is the way to go. If you want a bootcamp that is truly invested in your experience, Thinkful gives you a mentor and a bunch of workshops in local cities. You get to go to family dinners once a month with all the students and mentors that live in that city. If you're looking for a community experience with a lot of support, choose Thinkful.