Do You Need a Data Science Degree To Be a Successful Data Analyst?
Entering the world of data analytics, from a non-programming background, can seem like a big leap. But is that really the case? Not exactly. In fact, most data analysts do not hold data science nor computer science degrees. According to a study conducted by IBM in 2017, only 14% of these data professionals hold an advanced degree in a data-specific field. Majority of these individuals completed studies in areas outside of this realm, such as the social & natural sciences, and engineering.
What’s Required?
The main objective as a data analyst is to utilize software to analyze data and answer business questions. The most common skillsets for this role include fluency in a programming language (SQL and/or Python), familiarity with spreadsheet programs (Excel and Google Sheets), experience with a data visualization tool (Google Data Studio, PowerBI, or Tableau), and knowledge of mathematical & statistical concepts. Most entry-level roles require completion of a bachelor’s, but not in a specified area of study.
Bootcamps & Educational Platforms
In the age of the internet, there are a tremendous amount of educational platforms that offer courses, free and paid, on computer programming languages, mathematics, statistics, machine learning, and other tools used within data science & analytics. Some of the most notable platforms include Udemy, Coursera, and LinkedIn Learning.
For example, Udemy offers their courses at discounted rates for purchase several times throughout each month. If you’re on a budget, catching these sales can lower the cost to around $10 per course. There are also a wide-range of qualified instructors and topics you may choose from. Courses purchased through their platform are available for a lifetime, for easy and convenient access whenever. All courses come equipped with easy-to-download supplemental resources to assist you in retaining information you’ve learned as well.
Build a Portfolio
Utilizing a hosting service, such as GitHub, is a way to display your knowledge and experience with writing code and creating software applications. It’s also an avenue to connect with others that write code and becoming an open-source contributor. Once you’ve picked up a programming language of your choice, it is best to put those newly developed skills to the test. An active, up-to-date GitHub profile looks great to employers and gives you an outlet to showcase your dedication to your craft.
What about Technical Interviews?
Technical interviews assess your ability to think critically promptly and perform in pressure situations. Data analyst positions will oftentimes provide applicants with a set of data scenarios, that must be completed within a given time-frame. Other positions test purely your ability to write code in the language of your choice. To prepare for such events, you can sign up for a HackerRank account and practice at your own pace. Lots of coding challenges are given on this platform, so becoming familiar with their user interface and prompts will give you a strong advantage.
Your Prior Educational Training is Translatable
Lots of degree programs, such as the natural sciences and engineering, deal with data on a regular basis. Being able to collect, analyze, and interpret this data is a tedious process and requires one to execute a thorough plan. If you come from a social science background, thinking back on challenging assignments or research projects you’ve been assigned are also useful experiences to build on.
The critical thinking developed, during your previous degree program, is translatable to the field of data science & analytics, no matter the field of study. These thought-processes are used on a daily basis in a data analyst role. Mastering the technological aspects of data analytics is what might be novel to some. These deficiencies can be removed by conducting independent training in select areas, with the above resources.
Conclusion
Nonetheless, remember why you want to transition into data and have pride in your educational difference. Obtaining the hard-skills necessary for a data analyst role takes time. However, half the job is already complete with your prior education, experience, and willingness to learn. For individuals who prefer to allocate their efforts outside of academia, or simply do not have the means to complete another program at a university, this article is a great guide towards obtaining a data analyst role. If you found this article insightful make sure to like, save, and share with others!
