Katie-FergusonA few weeks ago, the ASA published this blog piece from one of our entry-level analytics specialists, Katie Ferguson, which I wanted to re-post here since she has some great tips for analytics professionals looking to get their foot in the door in this hot industry.


It’s no secret that analytics and data science jobs are the hottest areas to get into right now. But getting into the field can be tough, and even with a stellar degree it can be tricky to navigate the quantitative job landscape as an entry-level professional. Here at Burtch Works we’ve worked with many great clients, and have developed a checklist of things they look for in early career professionals, as well as tips that may help you in your initial search.

1. Complete an internship – A great way to test your skills, continue learning, and expand your network is to complete an internship. Without previous work experience, prospective employers will look at internships (as well as coursework) to determine if you might be a good fit for their organization.

2. Get experience with large, real-world data sets – One of the biggest challenges students will face in their first analytics job is their lack of experience with messy, large, real-world data sets. It is crucial that you find a way to add this to your experience, either through MOOCs (massive online open curriculum) such as Coursera or Udacity, internships, coursework, or Kaggle competitions.

3. Try Kaggle competitions – Kaggle hosts data crunching competitions where you can practice your skills, compete against other members, and gain access to large, real-world data sets similar to the ones you might use at your first job. It is a great resource, and employers often view Kaggle experience similar to how they would view coursework or internships.

4. Get a SAS certification – Although the availability of tools to wrangle Big Data has been diversifying, many employers still use SAS, and many look to the certification to verify credibility of analytical skills. Even though, according to our recent SAS vs. R flash survey, R has been gaining in popularity among analytics professionals and is especially preferred among data scientists, a SAS certification can still add credibility to your resume.

5. Create a complete LinkedIn profile – Over 90% of recruiters who recruit using social media use LinkedIn. It has become the go-to resource for many companies to check your references and resume, and having an updated, professional profile allows companies to see you as a person they might want to hire, not just an anonymous resume.

6. Look into an advanced degree – In our Burtch Works Study: Salaries of Predictive Analytics Professionals report last year we discovered that 86% of analytics professionals have at least a Master’s degree, and 18% have a Ph.D. In data science, 88% of data scientists hold at least a Master’s degree, and 46% have a Ph.D.

7. Familiarize yourself with the industry – Learn about the key players in your industry, what the latest tools and techniques are, and stay aware of industry news that may affect your opportunities or the companies you’re applying to.

8. Research companies – As well as knowing more about the industry you’re targeting, you should make sure to research the companies you’re applying to. Knowing about changes in business strategies, corporate goals, and current events are all ways to show that you have business savvy as well as technical chops. Companies like to hear that you are well-informed, because this shows that you are committed, interested, and willing to learn as much as you can about their needs and concerns.

9. Read job descriptions – Looking through job descriptions is a great way to get a feel for what technical skills companies are looking for, and can help inform what else you may need to learn. You may learn that the companies you’re applying to are all looking for certain tools like R, SQL, Python, etc. and can look for online resources to learn those skills.

10. Network, network, network! – Although it may be daunting, networking is a great way to learn about new opportunities. Check out your local chapter of the ASA, join other industry groups, or attend local meetups to network with professionals in your field and industry. Once you have completed your LinkedIn profile, you can also add everyone you meet as connections and join relevant LinkedIn groups.


For more information about job interviews, references, and strengthening your communication skills, check out our Career Resource Center. And check out the Burtch Works blog for posts like the biggest job search mistakes and biggest career strategy mistakes we see quantitative professionals making, as well as our take on current events in analytics and data science careers. Best of luck with all your endeavors, and make sure to connect with us on LinkedIn!

Interested in our salary research on data scientists and predictive analytics professionals? Download our studies using the button below.

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Want to learn more about data science and analytics career planning? Watch the webinar recording below to hear our recruiters’ best advice on interview preparation, tools and skills to learn, salaries, and more!


18 Responses to “10 Key Tips for Entry-Level Analytics Professionals”

  1. Nick

    Great article! What is Burtch Works opinion on which master’s degree to pursue? I am currently looking at University of Maryland University College’s master’s of data analytics. Any advice?

    • Justin

      Very good article.

      Nick I asked them about the same program on Twitter but did not get a response. I am consider applying as well. I spoke to an Analyst who is currently enrolled who answered some questions for me. If you email me at i will point you towards her so you can reach out.

      I would also be very interested to hear if anyone on the Burtch team knows about the UMUC program or have worked with any of the graduates yet.

      • sbosowski

        Hi Justin and Nick,

        I’ve spoken with our entry-level specialist recruiters, and they have not worked with any candidates from this program. This program only started in fall 2013, so we have not seen any of the graduates come through yet. It is an online program, so that’s always something to keep in mind when considering how rigorous the program may be. Thanks for your questions and best of luck in your endeavors!


        • Nick

          Hi Stacey,
          Now that is has been almost two years, what is Burtch’s view on such programs now?

          • sbosowski

            Hi Nick,

            Since you’re asking about specific programs, it sounds like you could benefit from connecting directly with one of our early career specialists. If you’d like more information, you can reach out to Flora Jiang at, I’ve forwarded her your request so she’ll be waiting to hear from you!


  2. Renan

    Thanks for the great article! Help me a lot with insights.

    I realized that will not be possible to enter in Quant career as a Data Scientist without a master degree. Do you believe that are some preferences courses in this carrer? I am more comfortable to study Computer Science, in the research line Artificial Intelligence. However, I could see that there are a lot of Statistics as majority.

    I also realized that in US there are some great offers of master degrees already specialized in Data Science, with a mix of skills like math, statistics, computer science, etc – e.g. Standford has a great course! In Brazil, we don’t have this kind of course yet. We have great classical academic master degrees courses (Capes: 6 or 7), but they are focused on Computer Science or Statistics Courses.

    Do you believe that Computer Science Master Degree is a good course to get in Data Science?

    Thanks a lot!

    • sbosowski

      Hi Renan,

      Many of the Data Science and data analytics programs are actually very new, new enough that we have not come across any graduates from those programs, so it would be very difficult for us to fairly judge the rigor of those programs. To learn more about the academic backgrounds of the data scientists that we work with, I would recommend downloaded our data science salary study, found here:

      As for skills that data scientists need, this blog piece goes into more detail about the skills that employers are looking for in a data scientist:

      And this blog piece goes into more detail about how we define data scientists, what skills and tools they typically posses:

      Best of luck!


    • sbosowski

      Here is some additional input from our data science recruiter, Adam Flugel:

      Data Science roles require substantial quantitative depth in both quantitative analytics and more generalized/low level computer programming. It’s hard to say what specific program or courses you should take, as it really depends on your current skillset, but I can give you a list of key skills that you’ll need to work with the unstructured Big Data problems expected of a Data Scientist. This isn’t by any means an exhaustive list, but if you build all of these skills, you’ll be on the right track:

      • Deep statistical knowledge, predictive modeling, regression analysis, etc. – expertise with statistical software (R is best at this point; SAS is still used by some firms, but often that’s an indication that their DS capabilities aren’t very developed and you may be looking at a mistitled Predictive Analytics role)

      • Ability to code in Python, Java, C/C++, etc. – You don’t need all of these, but more never hurts. Python is by far the most used, and therefore marketable. My advice would be to get to expert-level Python coding, both general programming and the analytics packages like PANDAS, numpy, scipy, sklearn, etc.

      • Ability to write and execute complex queries on SQL databases

      • Ability to work with Big Data frameworks like the Hadoop ecosystem (Hive, Pig, etc.). You most likely will not need base MapReduce Java programming, but experience pulling data and running analytics on data stored in HDFS using tools like HIVE is important. Spark is becoming the big name in the Hadoop ecosystem, so I strongly advise working with that as well.

      • In addition to Hadoop, working with cloud-based data will strengthen your resume as well – become familiar with things like Amazon S3

      • Understand machine learning algorithms and methodologies – at the very least understand how to implement them, though being able to create novel algorithms is ideal. This is where sklearn in Python comes in.

      At the end of the program, your goal should be to have the skills needed to wrangle massive unstructured data from multiple sources and perform advanced predictive analytics, resulting in business-relevant insights. Ideally, the course will also give you opportunities to undertake real-world projects of that nature. Look for programs that partner with big names in industry on capstone projects and internships for their students.

      Becoming a Data Scientist requires expertise in a variety of areas, and without either advanced statistical knowledge or strong programming skills at the start, you’ll essentially have to learn 2 complex disciplines. Neither Statistics or Computer Science on its own will give you the full scope of skills, so pay close attention to the curriculum of any programs you’re considering and always evaluate if the set of courses will adequately prepare you for projects like I mentioned above.

      I hope this is helpful, and best of luck!

      • Renan

        Thank you so much for the additional inputs, Adam! It also helped me a lot to make a decision!

  3. Rachel Feldman

    Thanks for the article! I am interested and in moving my career towards business analytics (I am currently an email marketing analyst) and was wondering if you have any updates on masters programs that you recommend as it has been a couple years since the previous comments were posted. Also, do you know of any companies that have some sort of training program for the desired skills for more on the job learning? Thanks!



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