Data Science and Data Analysis: What Are the Differences and Which is Right for Me?

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With the proliferation of technology, data-centric jobs are more prominent than ever. However, in such a vast field, it can be difficult to select which niche you would like to pursue a career in. A few of the most exciting areas in this sector are data science and data analysis. If you’re relatively new to the field, you may be wondering: what is the difference between the two? What kind of education do I need to be successful and gain employment in these fields? Which is right for me? If you’re contemplating breaking into the data sector but are struggling to choose a specialization, the first step in facilitating this decision is knowing the differences between each role type, as well as their educational requirements.

Data Science

Data science encompasses many different skills, including hacking, mathematics, and statistics. Data scientists gather and examine big data, then conduct predictive analysis to provide businesses with accurate predictions and insights that can be used to make critical corporate decisions.

To become a data scientist, there are certain educational requirements. A bachelor’s degree in IT or a similar field is necessary as a starting point and further education is recommended, particularly if you wish to secure an advanced leadership position. This could come in the form of a graduate certificate or diploma, a master’s, or a doctorate degree. Many universities offer data science programs, including RMIT University, which boasts a 100% graduate certificate of data science online program. The 28-week course provides students with the key foundations of programming, analytics, and data wrangling that employers require now.

Data Analysis

While data science is focused on machine learning and predictive modeling, data analysis concentrates more on viewing and examining historical data in context. In other words, while data science seeks to discover new questions that drive innovation within a business, data analysis seeks to answer existing questions to produce better business decision-making. Data analysis is one of the most broad fields in the technology sector; there are dozens of types of data analysis depending on the nature of the data being examined and manipulated.

In terms of the educational requirements, these are very similar to those for data scientists. You’ll need a bachelor’s degree in IT, computer science, statistics, or a similar field. It is also advisable to minor in applied statistics or data analysis. Additionally, when it comes to advancing your career, pursuing a graduate certificate or diploma, a master’s, or a doctorate degree are great ways to enhance your skills and knowledge, while boosting your employability.

With so much opportunity and exciting development, it’s no wonder you’re choosing to dive into the world of data. Whether you wish to pursue a career in data science, data analysis, or another specialization, jobs in such fields are abundant and transformative. Although it can be tricky to choose your niche in such a vast field, educating yourself on the distinctions and requirements for each is the first step in making the best possible decision for yourself.

Image by Gerd Altmann