Big Data is a defining characteristic of our post-industrial society. According to the World Economic Forum 2020 Jobs Report, data science and analytics are now the most in-demand, future-focused occupations. What, however, differentiates a data scientist vs. a data analyst career path? 

While the trajectories are similar for data science and data analytics, they diverge in significant ways. Let’s tease apart the two roles and how they both contribute to the 21st-century economy. 

Scientist vs. Analyst: A Closer Look

Data is the feedstock of the information age. While data collection and analysis stretches back centuries, the exponential growth in computing power and the attendant rise in Big Data have opened new frontiers and possibilities. 

From this data evolution (and revolution) emerged the professions of modern data analytics and data science. 

Data Analyst

Arthur Conan Doyle’s Sherlock Holmes once said, “It is a capital mistake to theorize before one has data.” But once one does have data, what to do with it? This is where the data analyst comes in. 

Data analysts prepare, analyze, and interpret structured data to help solve problems, answer pre-defined questions, and streamline processes. 

Analysts work with industry leaders to solve tangible problems, identify information gaps, and transform data sources into actionable intelligence. Moreover, unlike a data scientist involved in original research and data collection, analysts analyze existing data to guide real-world decision-making. 

A successful data analyst has at least the following skills:  

  • Fundamental mathematics and statistics
  • Programing languages including SQL, Python, R, HTML, and JavaScript
  • Spreadsheet tools 
  • Visualization tools like Tableau 

A minimum of a Bachelor’s degree is typically required to work in data analysis. Top industries for data analysts include finance, business intelligence, sharing economy services, and healthcare.  

Glassdoor reports an average annual base salary of $69,517 for data analysts.   

Data Scientist

An article on the SAS website describes data scientists as “part mathematician, part computer scientist, and part trend-spotter.” A data scientist looks at the world, asks questions, and collects data to discern patterns and answer those questions. 

Data scientists possess advanced skills in manipulating and visualizing raw data. They create algorithms, employ machine learning techniques, and build insightful data visualizations that bring new, unexplored inquiries to life. 

The skills required of a data scientist include:

  • Advanced mathematics, statistics, and predictive analysis 
  • Machine learning modeling 
  • Programming languages such as Python, R, SAS, Matlab, SQL, Pig, Hive, or Scala
  • Storytelling through data visualization
  • Using distributed computing frameworks such as Hadoop
  • Business acumen. 

Data scientists often begin their careers as statisticians or data analysts with an undergraduate degree in mathematics or statistics. Advancing into a data scientist position usually requires a master’s degree. 

Industries of all stripes employ data scientists. Top sectors include banking and financial services (BFSI), healthcare, transportation, telecommunications, media, digital marketing, and retail. 

According to Glassdoor, the average base annual salary for a data scientist is $117,212.

Master of Science in Data Science: Lean In to the Future

The online Master of Science in Data Science degree program at Merrimack College teaches students essential data science theories. While these theories are crucial, the program also focuses on the practical skills and competencies required to launch or expand a career in the field. 

The top-rated, industry-aligned program is a collaboration between Merrimack’s School of Engineering and Girard School of Business. This unique pedagogy provides the engineering skills of a data scientist with the savvy of a business professional able to tell a cogent story with data that stakeholders can understand.    

The 32 credit hour curriculum consists of eight foundational, intermediate, and advanced study courses, ending with a capstone project. 

These courses include:


  • Foundations of Data Management
  • Foundations of Statistical Analysis
  • Data Visualization


  • Data Exploration
  • Data Governance, Laws, and Ethics


  • Predictive Modeling
  • Machine Learning


The capstone allows students an opportunity to spread their wings and apply what they’ve learned to practical challenges. Using raw data, students solve real-world industry data analysis problems. The hands-on practicum involves in-depth data processing and preparation.  

The capstone entails no formal lectures, pre-determined assignments, or exams. The focus is on the outcome of student-specific projects. The capstone provides students with a portfolio demonstrating the knowledge and practical skills required for success as data scientists and analysts. 

Meet the Challenges of the Digital Age

We live in a data-driven world full of complex challenges. Meeting those challenges takes a combination of imagination, hard science, and practical skill to sort out the signal from the noise. 

Graduates of the online Master of Science in Data Science program emerge ready to collect, process, and analyze raw data to ask questions, solve problems, find solutions, and open new possibilities in the digital age.