How I Got Into Data Science

plantCupWhite2.JPG
4 min read

Earning decent salaries straight out of college is the most important factor that enabled me to save $100k by age 24

Specifically, I landed a job as a data analyst at age 22 that paid $52,000 per year.

Then I landed my current job as a data scientist at age 23 that pays $80,000 per year.

I often receive emails from readers asking how I got into the field of data science, what exactly I do as a data scientist, and advice for anyone looking to get into the field themselves. Here are my answers to those questions.

How did I get into the field of data science?

I graduated with a bachelor’s degree in statistics in 2016. The semester before I graduated, I asked one of my professors if she knew of any data science / analytics jobs in the area. She referred me to a friend who worked at a local healthcare company in their analytics department, who was able to score me an interview.

After a successful interview, I landed an internship with this company and worked 20 hours per week while taking classes during that final undergrad semester. Once I graduated, the company offered me a full-time position as a data analyst with a salary of $52,000. I accepted. 

Over the course of the next year, I worked 40 hours per week at this company and also completed a one-year Master’s program in Applied Statistics at the same college. During the day I would work and in the evenings I would drive straight to campus for classes. This was a grueling schedule, but looking back on it I’m glad I made the decision to embrace it.

When I graduated in the spring of 2017, I tried to negotiate a promotion but was told that I would have to wait one more year to be eligible. Upon hearing this news, I sent out my resume to ten different companies for various data scientist jobs.

Only one company responded, which turned out to be all I needed. They brought me in for two rounds of interviews and ended up offering me a position as a data scientist with a salary of $76k. Despite my inexperience with negotiation, I was able to bump that number up to $80k before accepting the position. This is the same job that I have today.

What exactly do I do as a data scientist?

Most companies have a bunch of data they collect on their customers, their business processes, and their sales numbers. The job of a data scientist is to take this data and make sense of it. A data scientist answers questions like:

What items sell the best in different regions?

What types of customers buy the most products?

What types of promotions work best?

How can we segment customers into groups so we can advertise to them more effectively?

How can we save money on our logistics processes?

How can we predict how many items we’ll sell during this holiday season?

The basic process that I go through to answer questions like these is:

Find out where the data is stored (usually in some type of database)

“Clean” the data, so it’s in a usable format to analyze and summarize.

Depending on the business question, I’ll either summarize the data using descriptive statistics, charts, and visualizations, or I’ll create a model using the data to predict future trends or patterns. 

A typical 8-hour day in the office usually consists of:

-5 hours of working with data using Excel, SQL, and R

-2 hours of meetings

-1 hour of administrative stuff, email, etc.

How can you get into the field of data science?

To be a data scientist, you need three basic skills:

1. knowledge of statistics

2. knowledge of some coding language (SAS, R, Python, etc.)

3. basic knowledge of SQL and how databases work

To be honest, most of these skills can be acquired on your own time using free resources on the internet that don’t require a college degree. The problem is that a degree is often required to get your foot in the door at most companies. 

If you’re a prospective or current college student and you want to get into the field of data science, you don’t necessarily need a degree in statistics. But it does help to have a degree in some technical field like math, engineering, computer science, economics, information systems, etc. This demonstrates that you can work with numbers in some capacity. 

If you already have a full-time job in a different field and you simply want to make a transition into the field of data science, you have a couple options:

1. The formal route: complete a 1-2 year Master’s program in business analytics, data science, computer science, or statistics. Then apply for data scientist positions at various companies.

2. The informal route: learn basic statistics, basic SQL, and a programming language (R and Python are the most popular) in your free time. In your current job, attempt to take on an analytics project or volunteer to shadow someone at your work who already does analytics. Then, either attempt to transition into an analytics role or simply apply for data analyst jobs at other companies. After working as an analyst for a year or two, start applying for data scientist jobs.

Conclusion

Data science is a booming field filled with opportunity right now. Most companies are dying to find talented people who know how to work with and interpret data. If you’re able to pick up some basic statistics, programming, and SQL skills, you could boost your income substantially.

If you have specific questions for me about data science that I didn’t cover in this post, feel free to shoot me an email at fourpillarfreedom@gmail.com.


My favorite free financial tool I’ve been using since 2015 to manage my net worth is Personal Capital. Each month I use their free Investment Checkup tool and Retirement Planner to track my investments and ensure that I’m on the fast track to financial freedom.

Sign up to have my most recent articles sent straight to your email inbox for free ?


 

Full Disclosure: Nothing on this site should ever be considered to be advice, research or an invitation to buy or sell any securities, please see my Terms & Conditions page for a full disclaimer.

10 Replies to “How I Got Into Data Science”

  1. Hi Zach,

    Reading this post makes me wonder what kind of statistical tool you use at work to interpret data. For example, maybe TOP 3 concepts that are frequently used to make sense of the data?

    I’m not very familiar with the terms, read a little on K Clustering before, and it is interesting. So would like to read about what are some of the common concepts used!

    Regards,
    Henry

    1. When I first start working with a dataset, I usually try to visualize the data with histograms, scatterplots, etc.

      If I’m just trying to describe a dataset, I’ll use common descriptive stats like mean, median, standard deviation etc.

      And if I’m building a model, I’ll commonly use regression or clustering depending on the business question.

    1. In my limited experience as a data scientist over the past ~2 years I would not consider it stressful at all. That said, it probably varies by industry. I have been in healthcare and retail. I’m sure finance/banking or other more demanding industries could present more stressful situations.

  2. What would you define as ‘basic’ statistics? What theories or methods should I know before evening attempting to interview in that field?

    1. I consider basic stats to be the concepts you would learn in an intro to statistics course like confidence intervals, hypothesis testing, regression, ANOVA, and probability.

  3. I spent about three years as a manager of a bunch of data scientists. (I moved on about three years ago) It truly is a growing and in demand field. The big thing to note is the next phase that’s coming is automation of everything. Well you can’t automate what you don’t have data on. They both feed each other.

    1. Completely agreed – it’s in high demand right now and the income opportunities reflect that. Very cool to hear from someone who used to be in the field. Thanks for sharing!

  4. What are your thoughts on the coding bootcamps that offer coursework on Data Science and Analytics? I know some can get really expensive.

    1. Most of them aren’t worth it. The free equivalent courses on Coursera are just as good.

      An alternative to taking courses is to simply download a free dataset online and try to do something with it: summarize it, visualize it, etc. using R, Python, or whatever coding language you’re trying to learn. This will force you to google “How to import data”, “how to do X, Y, Z”, etc. and you’ll learn much quicker this way.

Leave a Reply

Your email address will not be published. Required fields are marked *