Feeling drawn to the “numbers behind-the-scenes,” finance grad Keaton Hutchison recognized that if he wanted to feel satisfied in his job, he needed to be working with data. A friend recommended Thinkful’s online tech courses, and Keaton chose the Data Analytics Flex self-paced program (which is now available full time with the Data Analytics Immersion program). Keaton tells us why he wanted to learn data analytics over data science, how he powered through the online bootcamp in half the usual time, and his new job as a Data Analyst at LAZ Parking!
Could you tell us about your background before you decided to pursue data analytics?
I graduated from CSUN (California State University, Northridge) with a degree in Finance. After college I was unsure of what career I wanted to pursue, so I accepted the first position I was offered with career growth – an insurance job. I pretty much learned on the job at Farmers Insurance and ended up doing auto liability claims there. I realized that wasn’t for me. While I was in the middle of this awkward career change, a good friend of mine who works for Thinkful suggested that I try out a program.
What attracted you to wanting to learn data analytics, specifically? Did you think about data science?
At my last job, I always found myself interested in the numbers behind-the-scenes, more so than the work I was actually doing. I managed to secure a couple of interviews that were geared towards data analysis and realized that I lacked a couple of the necessary skills. I had been trying to get into these kinds of positions; I just didn’t have the skills to get the job.
I did think about data science at one point. After I graduated from college I was looking to go into that field, but I don’t think it’s for me. It’s too much programming, it’s too much in the weeds. I wanted to be more on the extraction and reporting side, rather than learning deep technical skills. My hope now is to get experience in data analytics, then transfer to a role in my current company with a data science skill set, then build upon that.
Did you think about teaching yourself data analytics skills rather than doing a program?
I did think about that. I tried to teach myself coding skills before and found myself derailing. I knew that I was going to be working through the Thinkful program full-time, so the price I ended up paying was only about $3,000 because I finished it in half the time that the course required. I thought, “to have a structured program instead of trying to teach myself, $3,000 is not a huge expense."
Did you research any other data analytics bootcamps?
I definitely did. I read reviews on a bunch of programs. One thing that I really wanted to overcome was employers looking down on bootcamps, so I tried to find the one with the best return on investment – which just so happened to be the one that my friend worked for.
There were a couple of other things that also made me choose Thinkful’s program: namely their tuition guarantee, mentorship, and ongoing career coaching.
What was the Thinkful application and interview process like?
It’s not a particularly difficult interview process. It was just a phone call asking questions like “Why do you want to get into analytics, why do you think you’d be a good fit for the program, do you work well by yourself, can you keep yourself on track?” I think they’re just trying to make sure you’re mutually well-suited for the program. As long as you're someone who has the drive and knows why you want to get into the field, you’ll get into the program.
What was the learning experience like at Thinkful’s online data bootcamp?
In each section, you have a lesson. Excel was the first part of the course, and that’s where they start building your foundation for the rest of the course. There’s Excel, SQL, Tableau, and Python. For Excel, I believe that there were four to five sections in that particular learning module. In each module, we study three or four lessons depending on the length of the section, sometimes with skill tests or little technical challenges that would be graded by someone on their grading team. Then there were capstone assignments. These were graded the most heavily.
What was your study setup like? How did you maintain your focus on the program?
I think it’s very important to keep to a fixed schedule and to try not to deviate too much. From the beginning of the program, I worked from home. I got myself an extra monitor – having dual monitors proved really useful so I highly recommend that.
I worked Monday through Friday and got caught up really quickly. Typically, I would wake up, make breakfast and treat it like a regular job. I’d work from 9am to 12pm, go to the gym, and then work again between 1pm and 3pm. Most days, I put in around 6 hours of study.
There was a point when I was three-and-a-half months ahead of the program and so I didn’t have to use their “pause days” to take time off for things I wanted to do. For example, I went on a week-long vacation and didn’t fall behind during that time. That said, the content came pretty easily to me – that's something to take into consideration.
How often did you interact with mentors and other students?
I had my assigned mentor whom I met twice a week for 45-minute sessions. There are also technical coaches on the Slack channel, so if I miscalculated when I needed my next meeting and my mentor wasn’t available, people were online 12 hours a day. I’m on Pacific time and every time I woke up at 9am and got on the computer, they were there to help – they were easily reachable. I personally didn’t need too much mentorship. A lot of the time I grasped the material well so I could have made do with perhaps seeing a mentor every two weeks.
As far as other students go, there was another Slack channel where we had access to everyone in the community for questions, but I didn’t really interact with them very much. I wasn’t as outgoing as some other students in the program.
Did you have a favorite project that you worked on during the program?
My favorite project was the Python capstone. The brief for the final project required me to build a statistical analysis based on a data set I found interesting. They didn’t provide us with the data set – we each had to find our own. I chose credit card default rates based out of Taiwan. We had to get approval on the data set first before beginning work to ensure we weren’t wasting time on a data set that would set us up for failure. We each had to put together a project proposal and when that was approved, we could start on it.
I was hoping that I’d find something doing some correlation analysis (perhaps the relationship between age and credit card limits in a different country) but everything was pretty much exactly as you’d think it would be: older people are usually associated with the higher credit limit. Income is relative to age – with some outliers.
That project tied all my knowledge together and was a really good showcase to put on my resume as I was transitioning into a new field. It meant I could say: “This is what I’ve built, these are my technical skills, I’ll be able to get the job done.” That’s why it was my favorite project – having something to show to employers to prove I have the skills and that they should hire me, has been very, very nice to have.
How did Thinkful prepare you for job hunting? What kind of career advice did you receive?
As a component of one of the modules, there was a builder for your resume, cover letter, and Linkedin profile. The course also taught us how to give an elevator pitch, and how to network at events. I didn’t have a cover letter, so having somebody help me produce that was very useful. After I graduated, Thinkful set me up with a career coach, who’s there to answer any questions I have, provide me with more interview practice, and just give me the lay of the land. For example, I had only heard back from about 5% of employers that I applied to, and the Thinkful coach reassured me that this was normal.
What are you doing now? What was your process after you’d finished the program?
I was unemployed in February, finished the Thinkful program in June and secured a job by the end of August – so about two months after I graduated. I applied to jobs left and right, probably about 10 per day. I would say that I got lucky in my job search. I came across an opening posted on Linkedin by LAZ Parking, a company I used to valet for. I sent a message to my old boss asking who I should talk to about the data position. He connected me with the recruiter and gave me a glowing recommendation. I interviewed with the recruiter, then the regional manager of IT, followed by SVPs, and was hired! But I have to say: if it wasn’t for that stroke of luck, I probably would still be unemployed. It’s a very, very competitive job market out there.
What’s your role and what have you worked on so far?
I’m the West Coast Regional Analyst for LAZ Parking. It’s a new position, so it’s a lot of learning. As of right now, I’m building out reports, doing analysis, and wrangling and cleaning data. I’m using a lot of the skills I learned through the Thinkful program – I’m using Excel a lot, and SQL to do my analysis. One thing I’m not using is Tableau. Nowadays, a lot of companies use Power BI so there was a learning curve to pick that up. If you are taking Thinkful’s program, I recommend you make the effort to learn Power BI. It seems that a lot of companies use it and it’s a very powerful tool.
How do you learn new technology like Power BI on the job?
I took a couple of free online courses to try and familiarize myself with it and watched a lot of YouTube videos. So I will say this: even after the program, there’s still a lot of self-learning to do. Thinkful is not going to give you everything you need to succeed in the job. You’re still going to have to adapt, and learn that not everything on the job is the same as in the classroom. I think that’s one important takeaway: don’t let yourself be frustrated by the process.
How are you finding your background in finance useful in your new data role?
I think that it’s extremely useful because a lot of what we’re doing is internal rate-of-return analysis; we’re doing year-over-year sales analysis. There are a lot of nitty-gritty calculations that we work with that I learned from finance, and I’m able to talk on the same level as the SVPs and VPs at the company. Just having that lingo, that background, and understanding of how they’re thinking really helps me excel.
How have your first few months at the company been? Are you enjoying being a data analyst?
I’m absolutely loving the career change. As an analyst, the lifestyle is a lot more relaxed than my previous career. I am working a lot, and it is hard hours, but it’s different. Rather than being at a 9am to 5pm job where it’s very customer focused and driven, it’s more “here’s the project, work on this, give us the end result and we’ll critique it from there.” Having that freedom and ability to craft something that I think other people will find meaningful has opened a lot of doors for me and definitely made me enjoy going into work every day.
What would you say has been the biggest challenge or roadblock in your journey to becoming a data analyst?
It has to be the job hunt. Like I said, the data skills all came very easily to me – even Python, but once I got to the job hunt, I definitely got knocked down a couple of pegs. Applying to all these jobs, getting to the interview stage and then having no one even call me back. That was difficult.
What’s your advice for anyone thinking about making a career change through an online data analytics bootcamp?
If you’re going to do it online, make sure you’re the type of person that has the drive and self-discipline to be able to finish the program. I’ve found that when I pay for something, I tend to have more discipline because I want to finish it and get my value out of it. Also, do your research. There are in-class options out there too.
Also, if someone’s attracted to Thinkful for its tuition guarantee, I’d suggest reading the fine print on it. You do have to attend five networking events per month in order to keep the guarantee. I certainly don’t think that it’s unattainable, but for me, it was too much.