Want to Study Data Analytics But Don’t Have Relevant Experience or a Quantitative Degree?

By - Jul 17, 09:30 AM Comments [0]

Want to Study Data Analytics But Don't Have Relevant Experience or a Quantitative Degree

In my experience guiding applicants to data and business analytics programs, I have often encountered applicants who really want to get into data but have little to no experience in the field. Others might have some experience, but lack the quantitative background that is preferred for these programs. If you are in such a position, do you still have any chance to attend a data-oriented program?

Absolutely. However, you need to acquire the experience and exposure you currently lack, transforming yourself into a compelling, competitive applicant.

Tips for gaining the experience you need for data analytics programs

Here are my tips for gaining data experience and academic background in the field:

  1. Do an internship or volunteer work where you can get exposure to managing and analyzing large amounts of data. If you are already working, get involved in projects within your organization that will strengthen your data analysis skills. It’s not too late to do it, but the time to start is now.
  2. Top up your academic credentials. If your undergraduate background does not lean towards quantitative fields, enroll in classes that are strong in quantitative content: statistics, calculus, linear algebra, and other advanced math subjects are ideal. If you already graduated, that doesn’t mean that you cannot take more classes now; those classes will still show up on a transcript. If you are still in college, try taking a couple of those classes before you graduate.
  3. Enroll in computer language courses, such as R, Python, C++, Java, SQL, or similar. You can take these courses online and, given their relatively short duration, they can be completed in conjunction with other items included in this list.
  4. At least one (preferably all) of your letters of recommendation should talk about your analytical ability. Ask at least one recommender to discuss your quantitative skills and experience.
  5. Invest time and effort in scoring high on the GRE or GMAT, giving particular emphasis to the quant section.

The importance of demonstrating your commitment

I have had many clients who have successfully secured admission at a top data analytics program without holding a STEM or other quant undergraduate degree. Many others have gained admission to these programs without relevant experience. Their secret? Demonstrating through concrete actions that their commitment to the field goes above and beyond words.

Your best course of action is to prepare your analytical toolkit with courses, projects, and paid or unpaid work relevant to the field of data analysis. All this will showcase your comfort in handling data, your commitment to the field, your academic potential, and your technical and analytical skills.

Do you need help creating an application strategy that will position you for admissions success at a graduate data or business analytics program? Do you need assistance in crafting a personal statement that highlights your strengths, experience, and dedication to the field? Explore our Graduate School Admissions Consulting & Editing Services and work one-on-one with an expert advisor who will help you GET ACCEPTED.

Download our free report: GET YOUR GAME ON: Preparing for Your Grad School Application

admissions expertEsmeralda Cardenal is a Former Associate Director of Admissions at Yale SOM, Director of MBA Admissions at MSU Broad, and consultant at Cardiff Business School in the UK. Since 2014, she has guided Accepted clients to acceptance in various graduate programs including MBA and master's in finance, business analytics, data science, sustainability, and public policy. Want Esmeralda to help you get Accepted? Click here to get in touch!

 

Related Resources:

NYU Stern’s New Online Master’s in Quantitative Management, a podcast episode
Data Analytics Master’s Programs: What Do They Want in Applicants?
The Rise of Master’s Programs in Data Analytics and Related Fields

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