12 min read
One of the biggest factors that helped me save $100,000 by age 24 was landing a job as a data scientist with an entry level salary of $80,000. This high income along with some frugality is the main reason I’ve been able to increase my net worth so rapidly over the past two years.
Since sharing some of the details on my journey to becoming a data scientist, I’ve received quite a few emails from people asking how they can also land a job as a data scientist.
So, today I’ll share:
- What a data scientist does
- Why data science is such a great field to be in
- The skills you need to become a data scientist
- Whether or not you need a college degree to become a data scientist
- The actual resumes that I used to successfully land interviews
- The actual questions I had to answer during my interviews
- What type of salary you can expect as a data scientist
Let’s jump in!
What Does a Data Scientist Do?
A data scientist is a mix between a statistician and a computer scientist. In a nutshell, we use data to help a company achieve some business goal.
To know what to do with data, we rely on statistics.
To know how to do stuff with data, we rely on computer programming.
For example, if you’re a data scientist for a retail store, you might have access to customer data like age, gender, income, and past transaction history. Some business owner might come to you and ask you to help them decide which coupons they should send to which customers.
So, you might use a statistical technique like cluster analysis to group customers into unique “clusters” and send them specific coupons based on which cluster they belong to.
In order to do this, you’ll rely on your statistical knowledge to know that you should do cluster analysis in the first place and you’ll rely on your programming skills to actually write the code to perform the cluster analysis.
This is just one example of what you might do as a data scientist. In general, business owners at a company will come to you with some business question or problem they want to solve. Then, you’ll use data to help them solve that specific problem.
Sometimes this involves providing some simple summary stats like average sales at each store during each day of the week. Other times it will involve a more advanced solution like building a model to predict sales.
Why is Data Science Such a Great Field To Be in?
Data science is a great field to be in because it offers a high income, it’s in high demand, it’s unlikely to be automated, and based on my own experience from the last three years across two unique industries, I have found that it’s a profession that isn’t too stressful or demanding*.
*I have worked as a data scientist in healthcare and retail. It’s certainly possible that data science could be more stressful and demanding in a different industry like finance. I can only speak from my own experience.
As I mentioned earlier, a data scientist is basically a mix between a statistician and a computer scientist. It requires a nice mix of math/stats knowledge along with computer programming skills.
For people who are willing to acquire this unique skill set, they’ll have a ridiculous amount of opportunities available to them.
According to the U.S. Census Bureau, many occupations with the highest expected growth rates over the coming decade include occupations like mathematicians, statisticians, software developers, and operations research analysts, all of whom do work similar to data scientists.
The table below shows the 20 occupations with the highest expected growth rate from 2016 to 2026 from a recent post:
Note: As the word “data scientist” has become more popular recently, many companies who are looking for mathematicians, statisticians, and operations research analysts will advertise for these positions using the title “data scientist” to attract more attention.
For all of these reasons, it’s no wonder that so many people are interested in becoming a data scientist. So, what skills do you actually need to become a data scientist? Great question…
What Skills Do I Need to Become a Data Scientist?
To become a data scientist, you need to cultivate three specific skills:
I personally use R every day at my job. For anyone who wants to learn R and is into taking courses, I recommend this free course: The Analytics Edge. I took it as a senior in college and it helped me learn a lot about R along with many of the most commonly used statistical techniques.
If you do choose to learn R, I highly recommend downloading RStudio, a free software environment where you can write R code easily.
In addition to taking the course above, I’d recommend just going online, downloading some random dataset, and then trying to analyze the data or build a model using R.
By doing so, your learning will be necessity-driven:
How do I even import data into RStudio? Great question. Google it.
How do I find the average value for each column in the dataset? Great question. Google it.
How do I build a multiple linear regression model for this dataset? Great question. Google it.
Notice a pattern here? Each time you don’t know how to do something in R, you’ll be forced to go online and find the answer. This will ensure that you’re learning the skills you actually need. Of course it will be frustrating at first, but just keep going. You’ll get better over time.
Keep in mind that most people quit easily when they’re faced with problems they don’t know how to solve. This is especially true when it comes to learning a new programming language. That’s an advantage for you. More quitters = less competition.
SQL is a programming language that you use to retrieve data from databases. Remember that example from earlier when I said a data scientist for a retail company might want to perform a cluster analysis using customer data? Well, to access that data in the first place, you’ll likely need to use SQL to retrieve it from some database.
You can learn the basics of SQL in about three hours. I recommend this free course that provides interactive lessons and exercises: SQLBolt.
It will take a bit of repetition and practice to hone your SQL skills, but don’t be intimidated by it. It’s one of the easiest languages to learn. And remember, you don’t have to be in the 99th percentile with this skill, you just need to be good enough so that it’s not a hindrance.
You don’t need a degree in statistics to become a data scientist, but you do need a solid understanding of stats. It doesn’t matter whether you obtain this knowledge in a formal setting like a university or just through taking online courses and reading textbooks yourself. All that matters is that you know your shit.
Statistics is what you use to do the simple things like summarizing data, finding averages, and identifying outliers along with the advanced things like building statistical models.
To learn the absolute basics of stats, I selfishly recommend my own site I built: statology.org. As someone who has tutored countless college students in introductory stats, I designed this site with students in mind and I aimed to make it an easy way for someone to learn the basic concepts taught in any intro-level stats course.
To learn about more advanced topics in statistics, I highly recommend the free PDF An Introduction to Statistical Learning. If you can understand at least 75% of the techniques outlined in that book, you’ll be able to pass most data scientist interviews.
Do I Need a Specific College Degree to Become a Data Scientist?
I personally earned a bachelor’s degree in statistics and a master’s degree in applied statistics, which helped my resume stand out from the crowd since statistics is such a niche subject that very few people major in. However, you don’t necessarily need a degree in statistics or analytics to become a data scientist.
In my three years of experience on two different data science teams, I have seen people get hired for data scientist roles with the following degrees:
- Information systems
- Computer Science
The common trend among these degrees is that they’re all in STEM (science, technology, engineering, math) fields. If you have a degree in one of these areas, you have demonstrated that you’re able to think analytically and that you can work with numbers and data in some capacity.
A master’s degree in any of these fields is enough to land a data scientist interview. A bachelor’s degree in these fields will be good enough to interview for an entry-level role at some companies, but not all.
If you’re someone who has already earned a bachelor’s degree in a non-STEM field, you have a couple options
Option 1: Complete a Master’s program in a STEM field.
One example of someone who has done this is an old coworker of mine. He earned a bachelor’s degree in philosophy and decided to complete a 1-year master’s program in data analytics. Once he had this degree, he was able to land an interview and eventually a job as a data scientist at my old company.
Option 2: Gain knowledge and skills in programming, stats, and SQL in your own time and use it to land a data analyst position at your current company or a new company.
One example of someone who has done this is a current coworker of mine. She earned a bachelor’s degree in communications and worked in some business department at our company.
However, she took the initiative to learn programming and stats in her own time and was able to make the transition to my department as an entry level data scientist.
She has only been with my department for about a month, but she’s growing her skill set and her knowledge each day. She likely will never need to go get a master’s degree since she’ll be able to learn all the data science skills she needs purely through working on projects at our company and gaining real world experience.
Option 3: Create your own site where you can display a portfolio of data science projects you have worked on in your free time.
This is an option that very few people pursue, but one that can be highly effective. Simply purchase a domain name, buy a hosting plan, and do a few mini-projects where you download a public dataset and analyze it in some way. Share the code you used to analyze the data along with some charts and interesting findings.
To see an example of a site like this, check out the blog of Jake Vanderplas. He downloads datasets, analyzes them using Python, makes some cool charts, and shares some insights. For example, in one post he downloaded data from Seattle’s bike share system and analyzed what time of day people rode bikes most often. He shared the code that he used to analyze the data as well.
By building a site like this where you share little projects that you work on, you demonstrate to a potential employee that you’re able to take initiative, that you’re curious, and that you actually have experiencing writing code and using data to find interesting insights.
I personally built statology.org while I was still in college and put it on my resume as a side project. This got the attention of an employer because it’s so uncommon to see a student (or anyone, really) create their own site and build something interesting in their free time. Eventually this even helped me land my first internship as a data analyst.
By creating your own site and sharing your own projects, you’re virtually guaranteed to stand out from the crowd.
How Can I Land a Job as a Data Scientist?
To land a job as a data scientist, you need a well-crafted resume and you need to be prepared for potential interview questions you’ll have to answer.
Make Your Resume Stand Out
Here are my top tips for creating a resume that will stand out.
1. Keep it simple and short. Ideally your resume should be one page or less. If it’s longer than one page, you’re probably sharing too many details. Keep your sentences and your paragraphs short. You’re not writing a novel. Just make your point and move on.
2. Keep the design bland. No weird cursive fonts. No multi-color schemes. No funky formatting. Keep it bland and simple.
3. Eliminate typos. You can make typos in your tweets, your blog posts, your social media statuses, and your text messages. Do not make typos in your resume. If possible, have a friend read through it and check for grammar mistakes.
4. Don’t list your GPA if it’s less than 3.5.
5. Don’t use personal pronouns like “I”, “Me”, or “My” in your resume. In a study by Talent Works, researchers found that applicants who used even one personal pronoun had a -54.7% lower chance of getting an interview callback.
6. Start your sentences with distinct action verbs. From the same study by Talent Works:
If you did anything worthy at a company, you’ll have done something.
If you start the sentence describing what you did with an action verb, you’re off to a strong start. And if you describe the different things that you did at that company with different action verbs, you’ll have finished strong.
In short, say this:
“Developed a world-positive, high-impact student loan product that didn’t screw over people after 100+ customer interviews.”
“After 100+ customer interviews, the world-positive, high-impact student loan product was developed by me.”
While it helps to know these simple ways to improve your resume, it’s even more helpful to see real example of resumes that have landed data scientist interviews.
Fortunately, I have three real examples of resumes that did successfully land interviews and eventually three job offers as well: my own resumes!
I have compiled a package of my exact resumes that helped me land three data science jobs, all at different levels.
The first resume helped me land my first data science internship as a senior in college with no full-time job experience. This job paid $15 per hour.
The second resume helped me land my first full-time job as an entry level data analyst at the same company. This job came with a salary of $52k.
And the third resume is the one that helped me land my current job as a data scientist. This came with a salary of $80k.
This package of resumes offers serious value to aspiring data scientists because I’m not just giving you generic tips on how to improve your resume; I’m showing you the exact resumes that help me land real jobs.
This package is a resource that I wish I had access to three years ago when I was clueless on what employers were actually looking for in a candidate.
If you’re an aspiring data scientist, click on the button below and download this package of resumes for $15 to speed up your path to landing a position as a data scientist.
Be Prepared for Interview Questions
Once you actually land an interview, you need to be prepared for potential questions. From going through data scientist interviews myself and from interviewing potential candidates, I can tell you that there are two types of questions you’ll face:
1. Non-technical questions
Examples of these questions include:
- “Tell us about your work history”
- “Tell us about a time you went above and beyond what was asked”
- “Tell us about your favorite project you have worked on in the past”
- “Why do you want to join our team?”
- “What makes you interested in being a data scientist?”
2. Technical questions
Examples of these questions include:
- “How many ping pong balls can fit on a school bus?”
For this question, the actual number you provide as an answer doesn’t matter. You simply need to show that you’re capable of solving the problem by thinking mathemetically. Ask questions like, “What are the dimensions of the bus?”, “What are the dimensions of a ping pong ball?” and “Is the bus completely empty?” and walk through how you would solve the problem.
- “Two numbers multiply to 180 and add up to 27. What are the two numbers?”
The two numbers are 15 and 12.
- “Two trains are 150 miles apart traveling towards each other. One is moving at 50 miles per hour and the other is moving at 30 miles per. How many hours until they converge?”
This is a test of your ability to solve linear equations. Google something like “train math problems” and you’ll find some examples of how to solve problems like this.
- “What is the definition of ‘correlation’?”
You’ll be expected to know some basic stats terms.
- “Give us an example of a variable that is normally distributed.”
Some examples are height and weight.
How Much Can I Expect to Make as a Data Scientist?
If you successfully make it through an interview, you will receive a salary offer. The amount you’re offered will largely depend on your experience, your location, and your industry. Obviously salary offers in San Francisco will be higher than offers in Cincinnati.
If you want to become a data scientist because you like the idea of earning a high income, keep in mind that cities that offer higher incomes also tend to come with a higher cost of living.
In a recent post, I analyzed the median data scientist salary in 15 major U.S. cities along with the taxes I would pay on that salary and the equivalent cost of living to my current city of Cincinnati:
Although the median data scientist salary in Cincinnati is considerably less than San Francisco, the amount of that salary I’d actually be able to save would be quite similar due to the high cost of living in San Francisco.
In general, if you’re interviewing for an entry level data scientist position, you can expect to earn anywhere from $70k to $90k depending on your experience, location, and industry.
The field of data science is growing rapidly. If you’re able to pick up the right skill set, polish your resume, and prepare for the right interview questions, you’ll be in a great position to land your first job as a data scientist.
Zach is the author behind Four Pillar Freedom, a blog that teaches you how to build wealth and gain freedom in life.
Zach's favorite free financial tool he's been using since 2015 to manage his net worth is Personal Capital. Each month he uses their free Investment Checkup tool and Retirement Planner to track his investments and ensure that he's on the fast track to financial freedom.
Although the bulk of his net worth is invested in index funds, his favorite place to invest in individual stocks is M1 Finance, a site that allows you to build a custom portfolio of stocks for free.
His favorite way to save money each month on his recurring bills is by using Trim, a free financial app that negotiates lower cable, internet, and phone bills with any provider on your behalf.
His favorite place to find new personal finance articles to read is Collecting Wisdom, a site that collects the best personal finance articles floating around the web on a daily basis.
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