Master Of Data Science Berkeley – How did I choose this master’s program in data science over MOOCs, certificates and other universities, and is it worth it?
This week someone contacted me to ask my opinion on UC Berkeley’s Master of Information and Data Science (MIDS) program. This is a part-time online master’s program that I have been actively pursuing for the past year. “Overall, I’m wondering if you would recommend it from a value-for-money standpoint,” he asked, “and if you were looking at other programs, how did you choose MIDS?”
Master Of Data Science Berkeley
South Hall, home of the UC Berkeley School of Information. Photo: Wikipedia Commons, User: Falcorian / CC BY-SA
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“Overall, I’m interested to know if you would recommend it in terms of price and value – and if you were looking at other programs, how did you choose MIDS?”
Two years ago, when I was evaluating various data science program options, including Berkeley, I had the same question. In an age of MOOCs and self-study, why would anyone pay a premium to study a formal master’s program? Among master’s programs, Berkeley is not the most affordable either. The University of Illinois, Urbana-Champaign (UIUC) offers a similar program for just $20,000, while Berkeley’s program costs about $70,000. So is it justified? Searching the internet for an answer, I only found a few outdated Quora posts on the subject with less than useful information. I did a quick search today and saw the same posts. So I thought I’d give you the inside scoop starting in Summer 2020.
, as a current student who has completed just over half of the program. Whether the program is valuable to you depends on your situation and personal goals, but by sharing I hope to make your decision easier.
About me: My career has always been at the intersection of business decisions and data analysis. As one of the few SQL-savvy people in KPMG’s risk advisory practice and Uber’s internal audit team (in finance and accounting), I translated legal language into queries, crystallizing stories from the data that support audit recommendations. I used Benford’s Law, keyword searches, and basic statistics to identify scams and other risks, and I took pride in solving complex data puzzles.
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And yet I found that simply diagnosing past events was neither effective nor intellectually satisfying in the long run. By 2017, I had a taste of the added flexibility that learning the core Python language had brought to my workflow, allowing me to expand beyond relational databases. I saw that my incredibly talented colleague, Daniel Piers, provided a key proof for the research using an ML algorithm. The risk management industry, which had traditionally focused on descriptive or diagnostic analytics, has adapted to more predictive and prescriptive analytics. The latest fraud monitoring tools have used machine learning to detect fake documents or suspicious expense claims. And I wanted to be at the forefront of this revolution.
I started training in data science through a MOOC, but soon realized its limitations. First, I had to create my own curriculum, but the problem was that I didn’t know what I didn’t know. I also struggled to find quality content that related to my curriculum because I didn’t know how to evaluate it. The courses I tried focused more on tools, while I needed a little more help developing a data science mindset. And ultimately, I didn’t have enough incentive or pressure to be accountable and finish the courses I started. So I decided to look for alternatives — master’s programs in data science.
If I was going to devote time and money, it wasn’t enough for me to learn something, it was for other people.
That I learned something. I considered the shorter and cheaper DS certificate programs, but I didn’t know how much credibility they would add to my resume. Therefore, I decided to focus on master’s programs at established higher education institutions.
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I was on a great career path at Uber and I wasn’t going to quit. While developing DS skills was important, so was developing management skills and domain knowledge. A part-time program would allow me to maintain my income, continue my current career path, and further my education.
I didn’t care where the school was as long as I could access it from my home in San Francisco. This excluded the University of Chicago and the University of Washington, which had part-time programs, but only on campus.
I was a business manager who knew a thing or two about analytics. Sure, I wrote something
In my life but could not comment any data structure and algorithm. The last formal education I had in statistics was in my junior year of college and the last math, IB Calculus, was in high school. Unfortunately for me, several programs required applicants to be familiar with linear algebra. I think I could have taught it myself at LSU or something, but I wanted to get started, so I quickly ruled out those programs. These included UIUC, UCLA and Johns Hopkins.
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I also didn’t want to lean too far into the other end, which was business analysis. I already knew business analysis. So I ruled out programs like Columbia (in person and in San Francisco, but it was also $80,000!) and Indiana.
My philosophy is that I can’t learn everything there is to know, but I can always call my friends. Therefore, I wanted a program where I could interact meaningfully with students and instructors. This is the main reason many MOOCs didn’t work for me.
In 2016, a survey of the program showed that 72% of students agreed with the statement “I feel like a member of my university community” and 83% of students agreed with the statement “This program has helped me develop a network. with fellow students”.
Having spent the last year in it, I completely agree – distance learning did not prevent me from fully integrating into the community. Small live classes (<=15 students), frequent work hours with professors, active Slack channels on various topics, and occasional local meetups provide a sense of unity.
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According to my spreadsheet, there were only three schools that met all of my criteria: Notre Dame, Northwestern, and Berkeley. Since I had done my undergrad at Berkeley and loved it, it automatically ranked higher in my heart. I also knew I could easily visit from San Francisco when there were events on campus. So I decided to apply to Berkeley first and keep the others in my back pocket. And I was accepted.
What was I supposed to do? Stop investing in your career? I didn’t believe it. Photo by Avel Chuklanov on Unsplash
After receiving my acceptance letter in March 2019, I was still unsure. I had only received a small stipend to barely cover tuition and had to pay the rest out of pocket, so I hesitated. I spent many hours browsing Quora and connecting with current or former MIDS students on LinkedIn. Data science seemed like something I could teach myself on YouTube or Coursera, and as long as I can build a strong portfolio, I can build my career in it. But then I thought about my journey over the last few years, how difficult it was to establish what I needed to know in the first place. I needed guidance and community. So I decided to sign up. I can give it up anytime, I thought. At worst, I would have spent a semester networking with students at the nation’s best public university. I left knowing the program syllabus well so that I know what to study on my own next.
Three semesters later, I couldn’t be happier with my experience. I started applying what I learned in school to my work during the first few weeks of the program. Last summer I struggled to explain what a confidence interval is, but so far I’ve written two smaller research papers on statistics. Today, I can train ML models, explain their inner workings, and apply hyperparameter tuning or dimensionality reduction. However, the most important sign of my growth lies in my aspirations. I used to want to become a data science professional, but now I want to become a business scientist. I went into the program thinking that if I could talk intelligently about concepts with ML engineers and discuss how to use them in business, that would be enough. Today, I am considering a career more deeply related to ML algorithms.
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Berkeley did not have strict math or programming requirements at the time of application, but offered bridging courses and support to build a solid foundation on which to build. Berkeley’s ability to help close that gap is an incredible asset and benefit to the program. This allowed businessmen as