In this article
Data Science vs Business Analytics: How I'd Choose My Path
Ekene Eze 
If you enjoy coding, working with algorithms, and solving mathematical problems, you’ll likely thrive in data science. However, if you prefer analyzing trends, making strategic decisions, and communicating insights, business analytics is a better fit.
When I was deciding between the two, I kept getting lost in job descriptions and salary comparisons. But what really mattered was how I wanted to work with data. Did I want to build models and predict future trends? Or did I want to interpret existing data to solve business problems?
If you’re facing the same dilemma, the best way to decide is by understanding what each role actually does and how it fits your strengths. In this guide, I’ll walk you through the key differences, career paths, and real-world examples, so by the end, you’ll have clarity on the right choice for you.
Before diving in, here’s a quick comparison:
| Data Science | Business Analytics | |
|---|---|---|
| Key role | Building data models and using math and programming to analyze complex data and extract insights. | Analyzing data and digging for insights to solve specific business problems and presenting them to inform strategy. |
| Skills | Machine learning, advanced mathematics, programming, business intelligence, data analytics, and data visualization. | Data analysis using Excel or Sheets, SQL, visualization, basic statistical modeling, business intelligence, communication, presentation, and problem-solving. |
| Tools | Python, R, SQL, Tableau, PyTorch, Power BI, Tensorflow. | SQL, Excel, business acumen, data visualization tools, and communication. |
| Career paths | Data scientist, AI researcher, data engineer, machine learning engineer. | Business analyst, data scientist, operation analyst, data analyst, product analyst, marketing analyst. |
Now, let’s get into the meat. If I were in your shoes, the first step I’d take is to understand the differences and similarities between business analytics and data science.
Data science vs. business analytics: What sets them apart?
The difference between data science and business analytics comes down to how you want to work with data.
When I was making this decision, I had to ask myself: Do I enjoy coding, working with algorithms, and solving mathematical problems? If so, data science was the better fit. Or do I prefer analyzing trends, making strategic decisions, and communicating insights? In that case, business analytics made more sense.
If you go the data science route, you’ll take a highly technical approach that blends computer science, mathematics, and business knowledge. You’ll work with business analysts to understand key challenges, then clean, explore, and mine unstructured and structured data to improve its quality.
Once the data is ready, you’ll build and test predictive models using machine learning algorithms to uncover hidden patterns. These models help businesses make data-driven predictions, optimize processes, and automate decision-making.
When I looked at these responsibilities, I realized data science was all about solving complex problems, discovering trends, and forecasting what’s next. If that excites you, it might be the right path.
Business analytics might be a better fit if you don’t fancy data science. You’ll sit at the intersection of business, data, and operations. You are responsible for evaluating the business’s overall health, identifying gaps, and recommending solutions to improve business operations.
You’ll also perform basic data cleaning and transformation using tools like SQL and Excel. However, your primary focus is analyzing data to uncover insights, create reports, and present findings to stakeholders.
Beyond analysis, I realized that business analysts also take action. You’ll recommend strategic next steps based on data insights and collaborate with decision-makers to execute them. This means being involved in change strategy and ensuring that insights lead to tangible business improvements.
For example, imagine you work for an energy company and notice a drop in residential electricity usage due to increased solar panel adoption. A data scientist will build predictive models to forecast energy demand, optimize distribution, and detect anomalies. A business analyst, meanwhile, will interpret these insights, develop strategies like solar buyback programs, and lead their implementation to drive impact.
What Are Their Similarities?
Although data science and business analytics have differences, after working in both fields, I realized that they share the same goal. Both disciplines focus on transforming raw data into valuable insights that drive business decisions.
Think of them as two sides of the same coin. Both fields involve data mining, statistical analysis, data visualization, SQL usage, problem-solving, and stakeholder collaboration. They work together to help businesses make sense of their data, but their approach and execution set them apart.
The next step to take is to understand what daily tasks may look like and which one you’d prefer.
What Would Your Day-to-Day Look Like?
Your day-to-day life as a data scientist or business analyst depends on the size of your company and team, industry, and project focus. I’ll break down the primary responsibilities of both fields to give you a clearer picture.
What are the key responsibilities of a data scientist? A data scientist analyzes complex data to extract insights, build predictive models, and support data-driven decision-making.
Primary tasks:
- Business understanding: Understand the why behind what you’re building. You’ll often work with stakeholders to define project and business requirements that guide data usage and model development.
- Data ingestion: Gather raw data from systems. This data is obtained from databases, APIs, IoT sensors, Excel sheets, etc. A data scientist collects all the relevant data needed to solve the current business problem.
- Data processing, migration, and storage: You’ll spend most of your time cleaning, transforming, and migrating structured and unstructured raw data. The goal is to convert data into suitable formats and structures for analysis and accessibility.
- Data analysis: Data scientists identify patterns in data behavior using visualization tools and statistical techniques such as Bayesian Inference, A/B testing, and K-means clustering.
- Building machine learning models: You’ll define the appropriate modeling approach based on business objectives, data characteristics, and analytical requirements. Then, you’ll build, train, validate, and fine-tune the model using historical data to predict future trends or automate workflows. Depending on the use case, you may also leverage pre-trained models or transfer learning for faster deployment.
- Deploying and testing predictive models: You’ll test your model against business requirements and deploy it to production.
- Reporting: You also need to present model results to stakeholders using visualizations and clear, concise explanations.
Data scientists collaborate closely with data engineers to build data extraction and transformation pipelines. You’ll also work with business analysts and other stakeholders to set business requirements and align the model with them.
You’d perform these responsibilities using tools like TensorFlow, Pandas, Jupyter Notebooks, Scikit-learn, Apache Spark, Hadoop, Docker, GitHub/Git, SQL, Tableau, and cloud platforms ([AWS],(https://roadmap.sh/aws) Google Cloud, Azure)
What Are the Key Responsibilities of a Business Analyst?
As a business analyst, you’ll bridge the gap between business needs and technical solutions by analyzing data and processes and using these insights to drive strategic decisions. Your primary tasks involve:
- Data manipulation and analysis: While business analysts don’t need to be expert programmers, you need SQL and Excel for querying databases and lightly analyzing unstructured and structured data. Some business analysts also pick up Python for deeper analysis.
- Business acumen: You’d need to know how to perform holistic business analysis. This involves asking the right questions: How can I solve business problems with data? What processes benefit from data-driven insights? Do I have the right data? How can I start collecting this data? Understanding how different departments (marketing, sales, finance) operate is necessary to align data insights with business goals.
- Data visualization and reporting: You’ll create interactive dashboards and reports using visualization tools like Tableau, Power BI, and Google Data Studio.
- Communication and stakeholder management: You’ll be involved in clearly communicating complex data insights and providing simple and actionable business recommendations. You’ll often present findings to executives who may have little or no technical skills.
As a business analyst, you’ll perform these responsibilities using Tableau, Power BI, Salesforce, Excel, Google Analytics, SQL, Microsoft Power BI, Google Sheets, and Looker.
Career Prospects: What Paths Are Available to You?
When choosing a path, I found it helpful to learn about the diverse and promising career options in each field.
Data Science Career Paths
If data science is your focus, you have several options, but I’ll focus on the three leading fields: data science, machine learning engineering, and artificial intelligence.
Data Scientist
This role involves developing machine learning algorithms, analyzing large datasets, and extracting actionable insights to support business decision-making. You’ll work with structured and unstructured data, applying statistical methods and algorithms to uncover patterns, predict trends, and solve complex problems.
According to Indeed, the average annual salary for a data scientist in the U.S. is $125,639, with a range between $79,587 and $198,339.
Machine Learning Engineer
Machine learning engineers design, optimize, and deploy machine learning algorithms in production environments. Unlike data scientists, you’ll specialize in software engineering, ensuring models are scalable, efficient, and seamlessly integrated into real-world applications.
According to Indeed, Machine Learning Engineers earn an average salary of $163,390 per year in the U.S.
AI Specialist
AI specialists develop AI-driven solutions, lead artificial intelligence research, and manage business initiatives.
According to Glassdoor, the average salary for an AI Specialist in the U.S. is $134,500 per year.
A computer science or mathematics or data science/AI master’s degree or PhD is often desired for data science career paths.
Business Analytics Career Paths
If you choose business analytics instead, several career paths are available, but I’ll discuss three leading ones: business analyst, business intelligence analyst, and operations analyst.
Business Analysts
In this role, you’ll get to interpret data, identify business trends, and recommend strategies to optimize performance. You’ll also work closely with stakeholders to assess business challenges and use data to drive process improvements and cost-saving measures.
According to Indeed, the average salary for a business analyst in the U.S. is $85,000 per year, with potential earnings exceeding $100,000 for senior-level professionals.
Business Intelligence Analysts
This role focuses on data visualization, reporting, and trend analysis. Working in this role involves developing dashboards, creating reports, and helping organizations monitor performance metrics in real-time.
According to Indeed, Business intelligence analysts in the U.S. earn an average salary of $97,872 per year, with top earners making over $130,000.
Operations Analysts
Operations analysts focus on optimizing business workflows, improving efficiency, and reducing operational costs through data-driven analysis. This role is common in industries like logistics, finance, and retail, ensuring processes run smoothly and profitably.
According to Indeed, the average salary for an operations analyst in the U.S. is $74,648 per year, with potential earnings exceeding $100,000 in industries like finance and technology.
Business analytics admission requirements for those interested in this field often include a bachelor’s degree in business, economics, mathematics, or a related field, along with proficiency in statistics, data interpretation, and business intelligence tools.
Why Choose One Over the Other?
I chose data science because I have loved mathematics since my early school years, and anything AI gives me life.
As I mentioned earlier, if you enjoy coding, solving technical and mathematical problems, and developing data-driven solutions, data science might be the right path for you—especially if AI, machine learning, and big data interest you. This field focuses on building models, algorithms, and predictive systems to extract meaningful insights from data.
On the other hand, if you prefer interpreting data, identifying trends, and using insights to drive strategic business decisions, business analytics is a better fit. This path is ideal for those who enjoy working closely with stakeholders to solve real-world business problems through data-driven strategies.
If you’re still unsure, experimenting with small projects in both fields can help you determine which excites you more. Sometimes, hands-on experience is the best way to find the right path. Also, some business analytics programs focus on ML, allowing you to explore both fields simultaneously.
Next Steps?
Once you’ve chosen between data science and business analytics, the best thing you can do is stop second-guessing and start learning. Follow our Data Scientist Roadmap as your step-by-step guide from beginner to expert, tracking your progress along the way.
You can also start with Python, explore data analysis, and learn machine learning basics. But, if your preference is business analytics, master Excel, learn visualization tools like Tableau or Looker, and practice creating dashboards.
The roadmap also helps you schedule learning time and block study time on your calendar to stay consistent. For a detailed overview of any specific role, join the Discord community and stay informed!
