master’s of data science and analytics in ct

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

Information is the currency of contemporary businesses. With modern technology, these businesses rely on IT professionals to collect, store, and analyze this information in a meaningful way, in order to improve business models and revenues.

Data analysts and data scientists work within businesses to effectively analyze data. However, the main difference lies in what they do with it.

If you are interested in a career that is centered around data analysis, strategy and statistics, then pursuing a Master’s in Data Science or a Master’s in Data Analytics could be for you. The question is, what is the difference between these two degree paths? And which one should you choose to study?

Let’s explore the differences together. We’ll first uncover the responsibilities of data analysts and data scientists, and further the benefits of pursuing each master’s degree path.

What Does a Data Analyst Do?

Data analysts examine and organize large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions.

Data analysts spend a large amount of time searching data for useful insights and trends in order to communicate what they’ve learned to company stakeholders and their recommendations for improvement.

These recommendations aid businesses to address problems in their industry, identify missed opportunities for growth, and develop data-driven business plans.

Data analysts utilize data to draw meaningful insights and solve problems like:

  • Why sales dropped in a certain quarter
  • Why a marketing campaign fared better in certain regions
  • How internal attrition affects revenue

Data Analyst Characteristics’ and Skills

Data analysts typically have backgrounds in mathematics and statistics. They often choose to pursue an advanced degree, such as a Data Analytics master’s degree in order to advance their careers.

The best data analysts have both technical expertise and the ability to communicate quantitative findings to non-technical co-workers or clients.

A data analyst also has the following skills:

  • Data mining/data warehouse
  • Data modeling
  • Attention to detail
  • Proficiency in R or SAS
  • Knowledge of Structured Language Query (SQL), Excel, and Power BI
  • Problem solving
  • Statistical analysis
  • Database management and reporting
  • Data analysis
  • Business acuity

What Does a Data Scientist Do?

In contrast, data scientists design and construct new processes for data modeling and production using prototypes, algorithms, predictive models and custom analysis. A data scientist’s primary job is to collect data. They are often more involved in programming machines and optimizing systems.

Additionally, data scientists spend their time improving the effectiveness and efficiency of programs that collect and analyze data. They estimate the unknown by:

  • Asking questions
  • Writing algorithms
  • Building statistical models

Unlike data analysts, data scientists are heavy coders and can arrange undefined sets of data using multiple tools at the same time while also building their own automation systems and frameworks.

Data Scientists’ Characteristics and Skills

Data scientists have strong backgrounds in computer programming, machine learning, data mining, and deep learning. Individuals who pursue a master’s in data science are often responsible for the machines that store and interpret information.

A data scientist has mathematical and statistical knowledge, hacking skills, and substantive expertise. Like data analysts, many data scientists pursue a master’s degree in Data Science.

They also have knowledge and skills in:

  • Programming language
  • Problem-solving
  • Attention to details
  • Software development
  • Proficiency in big data tools: Hadoop and Spark
  • Programming abilities: Python, R, Scala
  • Expertise in SQL, Cassandra, MongoDB
  • Knowledge of visualization tools: QlikView, Tableau

Now that we’ve explored the difference between a data analyst and a data scientist, we can discuss the thriving career paths for each profession.

Data Analyst Career Paths

Data analysts work in a variety of fields and can have many different titles, including:

  • Database analyst
  • Business analyst
  • Market research analyst
  • Sales Analyst
  • Financial analyst
  • Marketing analyst
  • Advertising analyst
  • Customer success analyst
  • Operations analyst
  • Pricing analyst
  • International strategy analyst
  • Quantitative Analyst
  • Data Consultant

Additionally, the U.S. Bureau of Labor Statistics (BLS) predicts that companies will create over 30,000 new data analyst jobs by the year 2026. Individuals interested in a Master’s in Data Analytics can also leverage their value and opportunity to achieve the median pay of $81,390 per year.

Data Scientist Career Paths

Like data analysts, data scientists also work in many different fields and titles, such as:

  • Data Scientist
  • Data Architect
  • Data Engineer
  • Machine Learning Specialist
  • Statistician

According to the BLS, computer and information research scientists enjoy a median salary of $114,520 and the data scientists career path is in the midst of a growth trend. The BLS anticipates a 19% increase in data science jobs from 2016 to 2026.

Data Analyst vs. Data Scientist

No matter which career path you choose, at some point you will likely consider one of two advanced degrees: A Master of Science (MS) in Data Science or a Master’s in Data Analytics.

While both degree programs offer potential paths to working with data in powerful ways, each program presents its own focus and opportunities.

If you want to learn how to take complex datasets and use them to identify trends, share information, make decisions and recommendations, and predict outcomes, then enrolling in a Data Analytics master’s program would be the right choice.

On the other hand, if you want your advanced degree to have strong emphasis on computer programming, coding, cloud computing, and machine learning, then a Master’s in Data Science online, or in person, would be more beneficial.

However, be sure to interview prospective programs for further information. Different institutions offer varying program names, and it’s possible that a Data Analytics degree could effectively position you for a career in Data Science (or vice versa). Talk with your school to understand potential career options with either degree in hand.

Choosing the Right Career for You

No matter which master’s program you choose, make sure—along with educational requirements—you also consider your…

  • Personal and academic background
  • Interests and passion for computer science and business
  • Desired salary and future job

Data analyst and data scientist are both highly coveted titles in today’s job market, especially with businesses’ desire to make sense of – and capitalize on – their data.

Now that you know the difference between the two, you can identify whether pursuing a Master’s in Data Analytics or a Master’s program in Data Science is the best choice for you based on your interests and goals.

Both the STEM-designated Analytics MS degree and our online Analytics MS degree program at UB were designed by faculty who are industry professionals and experts in the field. Click here to learn more!