Hello all! A few subscribers asked how I transitioned from software engineering to data analytics. I thought it’d be helpful to document my steps for others in the same boat.
Whether you just started your career in data analytics or are looking to transition, I think this post would still be helpful.
A little background about me – I got my graduate and undergraduate in Computer Science program. I earned my undergraduate degree in Jakarta, Indonesia, and my master’s in the US.
Since college, I have always been interested in data analytics and data warehouses. While in college, I worked as a full-time teaching assistant – but as an HSP and introvert, teaching a few programming classes daily made me tremendously exhausted. So, I applied for an SQL DBA role internally and worked for a year and a half in this role.
Fast forward a few years later, I landed a role as a software engineer. I worked in this role for a couple of years and found myself burned out and unfulfilled.
After taking some time to explore different subjects, I decided to pay attention to what interested me in college and made a lateral transition to a data analytics role.
A few years into this role, I quit my job without a real plan due to the stress and burnout in this role. But I would not recommend quitting without a plan unless you need to. Although I did not have financial pressure then, I suffered from a self-identity crisis and maybe even borderline depression.
It took me about four months to make the change. But this happened fairly quickly because of my SQL and previous experience. First – I applied for a business analyst role. This position is a contract role. Compensation-wise, I had to take a pay cut, unfortunately. I found a full-time business analytic role two months later and quickly applied.
Although this route works for me, I’d encourage people to attempt to transition internally. Even though you hate your job, you should try to make the most of your job if you can. This way, too, you won’t lose health coverage (thanks to the American healthcare system), and hopefully, you don’t need to take a pay cut as I did.
In this role, I felt so much better. Being a data analyst allows me to use my technical skills, connect with people and employ my creativity when designing the dashboard. So far, I’ve worked with R&D, sales, product management, and marketing teams, which has been an interesting learning process.
Is this my dream role? I thought so in the first two years of my tenure, but I have a childhood dream I’d like to fulfill in the future, maybe when I have reached FIRE. Maybe I’ll still keep my day job, or maybe I’ll quit completely. I am not sure, but I still enjoy what I do as a data analyst.
On a side note, I wish people would normalize more non-linear career moves and put less pressure on themselves (including me). I think this world would be filled with so much happier people.
Anyway, here are the classes and/or certifications I took:
Data visualization
As a start, I’d recommend picking one of these data visualization tools. But because I am biased 🙂 I’d recommend Tableau. I found that PowerBI has a clunky user interface, and I am not a big fan of DAX. Many data visualization tools exist, such as Alteryx, IBM Cognos, Qlikview, etc., and it helps to develop a specialization.
I suggested these two tools because they seem more in demand than others. Thus, there will be more free resources and communities to learn from.
2. Business Analytic Certification Program by Wharton
The material is interesting and easy to digest, covering subjects such as linear regression and customer segmentation. But frankly, I don’t recommend taking it unless your employer is paying for it. It is very pricey (I paid about $1,800 three years ago). I’d aim for certifications such as Tableau Desktop and Google Analytics certifications. You can save the rest to buy an index fund or travel instead.
Data Wrangling
- SQL
SQL is almost always required in a data analytic position. This would be the first thing you want to learn if you are entirely new. Learn things like how to join subqueries and group by. If you never write code, this may sound intimidating at first. But I found the language intuitive and much easier to learn than traditional programming languages. - Python / R
Once you master SQL and one of the data visualization tools, start learning Python or R. Depending on the position, Python or R is not always required in a data analyst role. I like Python more because it is more intuitive to use than R (maybe because I am used to the sequential programming language?). Python is also more used by companies, while researchers and scientists often use R. A few things you can learn are how to pull data directly from the database server and how to pivot and unpivot data.
As far as technical skills, these would be the priority of study if you are starting:
- SQL
- One of the data visualization tools
- Python / R
Other helpful courses
- https://www.coursera.org/learn/analytics-business-metrics (It’s an exciting course, you’ll learn typical metrics used in the various departments, such as the marketing and finance department. It’s great to develop business metric ideas)
- https://www.coursera.org/learn/analytics-tableau (You’ll learn the data analysis process and develop visualization in Tableau. Last time I checked, you’ll get a free Tableau desktop for six months)
More tips
- If you have transferrable skills such as customer support or leadership experience, I’d mention it in the resume. Soft skills such as communication and leadership skills are helpful in this role.
- You will most likely have projects from school or some online courses. I’d create a GitHub repository, write readable code, and attach the GitHub link to your resume.
- If you pick Tableau, publish your work on Tableau Public. Consider taking the Makeover Monday challenge or Workout Wednesday. This would be a plus for your profile.
- Connect with people. The more you connect with people, the more chances you’ll have. Networking events also exhaust me, but they may work in your favor at the end of the day.
- Take advantage of your internal contacts. If you know any data analysts at work, ask them to grab a coffee and learn what they do daily. Express your interest in data analytics, and you might get helpful feedback (or they might introduce you to someone). I did this when I was on the quest for my next role. I spoke to a few product analysts and learned that what they do doesn’t seem to align with what I was looking for.
- Attend data analytic meetups in your area. For instance, I found pretty helpful meetups such as PowerBI and SQL in my city. Connecting with like-minded people is nice, and I was lucky to get a few job referrals.
- Don’t hesitate to ask for an introduction from a friend, colleague, or relative. On LinkedIn, you can find people by their job titles. LinkedIn will show the closest connection and the shared connection. I will go for it if you feel comfortable asking for an introduction through the shared connection.
- Try to get more than one offer whenever possible, and always negotiate the salary. Negotiation by phone is preferable, but it can also work over email.
That’s it, guys. I hope you found this post helpful!
Vi, a software engineer with a keen interest in personal finance, had planned to retire once she reached her lean FI/RE (Financial Independence/Retire Early) goal. However, after achieving the goal, she took few months of a mini-retirement filled with travel and adventure and decided to continue her career.
For the past five years, Vi has been using Personal Capital (Empower), a free financial tool. Her favorite features include the dashboard for net worth, allocation, and planning, which help track her FI/RE goal and keep those investment fees in check.