Business analytics and data science are technology-driven, evidence-based fields of study that can lead to rewarding careers in finance, healthcare, energy and many other essential sectors. These terms are sometimes used interchangeably, but they’re actually two distinct disciplines.
Business analytics transforms disparate data points into actionable insights that help organizational leaders make better decisions. Business analysts—who can sometimes be called Management Analysts or BI (Business Intelligence) Analysts—use tools like Microsoft Excel and SQL queries to conduct data analysis and generate insights to improve growth, operations and more.
Data science uses advanced computer science tools like programming languages and machine learning algorithms to discover meaningful patterns in many categories of information. Data scientists—who might also share responsibilities with Data Engineers or Machine Learning Engineers—deploy a broad range of experimental methods to investigate the forces that drive key trends and create robust models for predicting outcomes.
In Rice University’s top-ranked, accredited Online Master of Data Science program (MDS@Rice), you’ll gain the cutting-edge technical skills and holistic business knowledge you’ll need to make an impact in either of these rapidly growing fields. In this article, we’ll explore the differences between business analytics and data science regarding skill sets, educational requirements and career opportunities.
Differences in Data Scientist and Business Analyst Roles and Responsibilities
In comparing job descriptions across these two fields, you’ll find many areas of overlap, as well as a few distinctive functions. Both disciplines center around quantitative reasoning and the strategic use of computing technology, with business analysts often fulfilling the additional roles of communicator, facilitator and mediator within and between organizations.
The day-to-day responsibilities of a Business analyst or Data scientist vary considerably by industry. Here are some of the key functions you’re likely to encounter in leading industries like energy, medicine and fintech:
Business Analyst Responsibilities
- Apply knowledge of specific industry and business functions
- Create presentations and lead meetings on metrics and milestones
- Liaise between business lines and external parties
- Monitor data to aid in forecasting and prioritization
- Provide advice on the viability of new technologies
- Seek out opportunities to meet customer needs
- Use integrated project management tools
Data Scientist Responsibilities
- Apply spatial and network analysis to complex datasets
- Develop predictive models and machine learning algorithms
- Formulate new hypotheses for improved organizational performance
- Identify and analyze data shortfalls and anomalies
- Lead teams through technical phases of projects
- Preprocess data, including data cleaning and data transformation
- Script computer programs for running statistical tests
MDS@Rice’s flexible curriculum will prepare you to take on any and all of these challenges, lead positive change and compellingly communicate your discoveries to stakeholders and the broader community.
Differences in Education Requirements
While a bachelor’s degree in a related field like business administration or information technology may qualify you for an entry-level position in either data science or business analytics, many employers prefer candidates with a master’s degree. If you’re already working in one of these disciplines, pursuing a master’s can help you to expand your skill sets, advance in your career and maximize your earning potential.
In MDS@Rice’s business analytics specialization, you’ll gain access to real-world data sets, learn in-demand programming tools like Python and SQL and immerse yourself in an experiential Capstone Project.
Differences in Data Science and Business Analytics Job Titles & Salaries
While a bachelor’s degree in a related field like business administration or information technology may qualify you for an entry-level position in either data science or business analytics, most top employers prefer candidates with a master’s degree. If you’re already working in one of these disciplines, pursuing a master’s can help you to expand your skillsets, advance in your career and maximize your earning potential.
According to Glassdoor, Senior or Principal Business Analysts can earn between $109,222 - $131,222 average annual salary per year, while Data scientists can earn between $125,870 - $164,478 average annual salary per year. O*NET OnLine, a government-sponsored database, provides higher figures for both fields—$88,550 and $122,840, respectively. In many cases, a master’s degree can mean a significant difference in salary, with employers commonly offering $10,000 more to MDS graduates, even for entry-level positions.
Traditionally, most business analysts haven’t required the advanced computer science and data skills that MDS@Rice offers. With the rapid emergence of machine learning and AI in the business world, Rice graduates are well-positioned to extend into these new areas and expand their options in the job market.
There are numerous different job titles, levels and pay scales that fall within the realms of business analytics and data science. Here are a few examples in each category:
|BUSINESS ANALYTICS JOB TITLES||APPROX. SALARY*|
|Senior Business Analyst||$109,222 - $131,222|
|Business Intelligence (BI) Analyst||$87,021 - $109,982|
|Financial Analyst||$73,472 - $107,126|
|Operations Research Analyst||$89,647|
|DATA SCIENTIST JOB TITLES||APPROX. SALARY*|
|Data Scientist||$125,870 - $164,478|
|Data Engineer||$113,489 - $156,713|
|*Source: U.S. Bureau of Labor Statistics, Glassdoor, ZipRecruiter|
Differences in Data Scientist and Business Analyst Skill Sets & Tools
Data scientists and business analysts share many of the same core skills and tools, but there are also differences. As a professional in either field, it’s essential to have a solid command of mathematics, statistics and basic business computing. Additionally, each discipline has a different specialized toolkit. Here are some examples:
Data Science Skills
- Big data tools
- Likelihood estimators
- Linear algebra
- Multivariable calculus
- Python or R
- Machine learning
Business Analytics Skills
- Business forecasting tools
- Data cleaning
- Data visualization—like Tableau or Power BI
- Descriptive/prescriptive analytics
- Presentation tools
- Statistical inference
- Project management
When you pursue the business analytics specialization at MDS@Rice, you’ll acquire a broad foundation of technical skills that will serve you well in either field. Plus, you’ll be learning those skills in a flexible online environment where you can complete modules at your own pace, brainstorm with peers and engage directly with world-class faculty.
Combining Data Science and Business Analytics at Rice University
As you can see, data science and business analytics are distinct but related fields that work together to turn vast datasets into real-world insights and solutions. If you’re looking forward to a new career or advancement opportunities in either of these fields, MDS@Rice’s business analytics specialization can prepare you for success. Learn more about the program and view admissions requirements today.