Data Analytics vs. Data Science: What’s the Difference?

Both data analysts and data scientists work heavily with data or the statistics and facts that have been collected and grouped for the purpose of referencing and analyzing. The primary difference is how they use this data.

Data analysts are “thinkers,” taking the time to analyze data so that they can identify trends within the collections. They use the results to develop charts and presentations with the goal of more clearly defining and explaining what the data has shown them. Analysts are often the ones presenting businesses with ideas of how to increase or decrease the public attention around a certain trend. Their jobs are often to improve the overall business model.

Data scientists also work closely with data, but more often on the other side, such as determining the algorithms to identify and isolate the trends that data analysts will look for in the future. They are the data designers, so to speak.

They also construct the processes for data modeling. To do this, they generate prototypes, design new algorithms or tweak old ones, develop predictive models and run analytical tests to ensure that the end results will give them the data they need.

People who have a degree in data analytics may carry out their careers in a variety of ways depending on what industry and business they become involved in. Wherever it is, their purpose fundamentally stays the same: they use data to solve issues and gain insight into improvements to the business model or the direction of the industry.

Working in data analytics often involves getting a direct assignment toward specific sets of data. You are typically working to figure out the reason that something has or has not happened. For example, you may be looking through recorded data to understand why sales dropped during the last quarter or what specific change helped them to increase. Since every business can vary by region and industry, these data sets can change dramatically, but your overall goal typically won't.

Data analytics courses can vary, but they should all teach you a similar set of skills such as:

Data analysts can potentially hold a wide variety of career titles when they specialize in big data and analytics. Although analysts often complete similar tasks, different industries require working in different environments and at varying levels of administration. Some of the career titles you might consider pursuing can include:

Data scientists might find it helpful to develop a background with plenty of mathematical and statistical knowledge, skills in hacking and building algorithms and programs as well as substantive expertise. As a data scientist, you might also consider advancing your education with a master's degree.

Data science courses do not often differ substantially from data analytics courses since you need to be able to see and understand both sides of the story as a data scientist. You will typically focus heavily on courses in software development to be able to hone the skills needed for creating algorithms and programs that businesses can put to use. Topics and programs you might study include:

Data scientists have very similar career titles to data analysts. This grey area is where much of the confusion in the two career paths come from since the titles are vague but the jobs tend not to be. What it comes down to is clear communication with a potential employer. Ask them what the job description involves so you can figure out if they are looking for more of a data scientist or a data analyst.

Typically, job titles related to careers in data science include:

Now that you know more about data analytics vs. data science, you might have a better idea of which path sparks your interest the most. Start a conversation with us today to begin building a personalized learning plan that can help you achieve your goals.