Data Scientist vs Business Analyst: Which Career Path Is Right for You?

Data science and business analytics are two of the hottest and highest-paying career paths in the tech industry today. Both roles leverage data to drive business value, but they differ in their focus areas, skill requirements, and typical career trajectories. If you‘re analytically-minded and considering a career in data, you may be wondering: Should I become a data scientist or business analyst?

In this comprehensive guide, we‘ll do a deep dive comparison of data scientist and business analyst careers. We‘ll clarify the key differences between the two roles, the skills you need for success in each, and the important factors to consider when choosing which path to pursue. By the end, you‘ll have a clear idea of which role best aligns with your interests, abilities, and professional goals.

Data Scientist vs Business Analyst: The Basics

Before we examine the specific responsibilities, skills, and career paths for data scientists and business analysts, let‘s define what each role entails.

A data scientist is an advanced analytics professional who utilizes their expertise in statistics, machine learning, computer science, and business to extract actionable insights from large datasets. Data scientists use programming languages like Python and R to wrangle big data, build predictive models and algorithms, and provide data-driven answers to complex business questions.

A business analyst is a professional who leverages data analytics to help organizations improve processes, products/services, and software through data-driven insights. Business analysts act as the link between IT and the business, analyzing data to spot trends and areas for improvement, defining requirements and KPIs, and communicating their findings to both technical and non-technical stakeholders.

While both roles work with data to solve business problems, the key difference is that data scientists are much more focused on the technical side – statistics, machine learning, and coding – while business analysts emphasize analytics tools, business domain knowledge, and "soft skills" like communication and relationship-building.

Demand and Job Growth

The demand for both data scientists and business analysts has skyrocketed in recent years as companies seek to leverage their growing datasets for competitive advantage. However, data science has seen particularly explosive growth due to the rise of big data and AI.

According to LinkedIn‘s 2020 Emerging Jobs Report, data science roles have grown more than 37% annually since 2015, and machine learning engineer jobs have surged 74% yearly. It‘s now the #3 most in-demand role in the U.S.

Source: LinkedIn 2020 Emerging Jobs Report

Business analyst roles aren‘t growing at quite the same blistering pace, but they‘re still in very high demand. The U.S. Bureau of Labor Statistics projects 11% growth for management analyst jobs through 2029, much faster than average for all occupations.

Salaries

Both data science and business analyst jobs command high salaries, but data scientists typically earn more due to the advanced technical skills required. According to Glassdoor‘s 2020 data:

• Data Scientist median base salary: $113,736
• Business Analyst median base salary: $73,226

At the high end, senior and principal data scientists earn $150-200K+, while business analysts max out around $110-130K. Of course, salaries vary widely based on factors like job location, industry, company size, education, and years of experience.

The highest-paying industries for data scientists are tech, management consulting, finance, and healthcare. Top companies like Facebook, Google, Airbnb, and McKinsey pay well into the $200Ks for senior data science talent.

Educational Background

Data scientists typically have more extensive educational requirements than business analysts due to the technical nature of the role.

Over 90% of data scientists have at least a Master‘s degree and roughly half hold a Ph.D., mostly commonly in quantitative fields like:

• Statistics and Mathematics
• Computer Science
• Physics and Engineering
• Economics and Social Sciences

The majority of data scientists have an educational background in STEM (science, technology, engineering and math), but some come from other quantitative disciplines and pick up the necessary coding and statistics skills on their own or through bootcamps.

In contrast, business analysts usually need at least a Bachelor‘s degree, but it can be in a variety of fields like business administration, finance, economics, statistics, information management, and social science. An MBA or Master‘s degree can help you command a higher salary and move into senior roles faster.

Since deep programming knowledge isn‘t required, it‘s more feasible to transition into business analytics from different educational backgrounds. Many business analysts learn SQL, data modeling, data viz, and other key skills through online courses, bootcamps, certifications, and on-the-job training.

Skills

Let‘s dive deeper into the core skills needed to be successful as a data scientist vs business analyst.

Data Scientist Required Skills:

• Advanced statistics and mathematics (linear algebra, calculus, probability)
• Machine learning algorithms and libraries (scikit-learn, TensorFlow, PyTorch)
• Programming languages (Python, R, SQL)
• Big data technologies (Hadoop, Spark, Hive)
• Data mining and cleaning
• Data visualization (Tableau, PowerBI, Plotly)
• Software engineering and system design
• Business acumen and domain knowledge
• Communication and presentation skills

Business Analyst Required Skills:

• Data analysis and modeling (SQL, Excel, R)
• Statistics and quantitative methods
• Business intelligence tools (Tableau, Looker, PowerBI)
• Domain expertise and industry knowledge
• Requirements gathering and documentation
• Project management methodologies (Agile, Scrum, Lean)
• Stakeholder management and collaboration
• Written and verbal communication abilities

As you can see, data science necessitates much more extensive expertise in mathematics, statistics, and programming compared to business analysis. Most business analysts aren‘t expected to be able to code machine learning models from scratch or work with huge datasets.

Responsibilities

What does the day-to-day of a data scientist vs business analyst actually look like? While each role varies depending on the specific company and position, here are some of the core responsibilities:

Typical Data Scientist Tasks:

• Identifying valuable data sources and automating data collection
• Processing, cleaning, and validating structured and unstructured data
• Building machine learning models and algorithms to uncover insights
• Conducting data studies and experiments to solve business challenges
• Visualizing and communicating findings to both technical and business audiences
• Collaborating with engineering teams to deploy models into production

Typical Business Analyst Tasks:

• Analyzing business data and processes to identify trends and issues
• Creating reports and dashboards to monitor KPIs and performance
• Defining and documenting business and functional requirements
• Conducting user interviews and facilitating workshops to gather needs
• Serving as the link between business, technology, and analytics teams
• Performing financial and variance analyses to support business cases

While both roles are highly collaborative and involve a fair amount of business interaction, data scientists tend to focus more on the hands-on technical work of building machine learning models and deriving insights, while business analysts spend more time on stakeholder management, documentation, and communication.

Tools

The specific tools that data scientists and business analysts use depend on their company and industry, but here are some of the most common:

Data Scientist Toolbox:

• Languages: Python, R, SQL, Scala, Java
• Data processing: Pandas, NumPy, SciPy, dplyr
• Machine learning: scikit-learn, TensorFlow, PyTorch, Keras, Spark MLlib
• Big data: Hadoop, Spark, Hive, Presto, AWS (EMR, Kinesis, Redshift)
• Data viz: Matplotlib, Seaborn, ggplot2, Tableau, PowerBI

Business Analyst Toolbox:

• Spreadsheets: Excel, Google Sheets
• Databases: SQL, Oracle, DB2, Access
• Analytics: R, SAS, Python (Pandas)
• Data viz: Tableau, Looker, PowerBI, QlikView
• Process mapping: Visio, Lucidchart
• Project management: JIRA, Trello, Asana

The main difference is that data scientists rely heavily on more advanced programming languages and big data tools, while business analysts tend to work with analytics tools that have a graphical interface and require less coding. But there is certainly overlap in tools like SQL and Tableau.

Career Outlook

Both data science and business analyst jobs have a very positive long-term outlook as data becomes increasingly critical for business success. However, data science is seeing more explosive growth and is largely shielded from the threat of automation due to the highly complex nature of the work.

According to IBM, the number of data science jobs will surge by 28% through 2026, with particularly high demand in industries like finance, insurance, professional services, and IT. Data science is also a lucrative field, with a median salary of $126K and scope for moving into high-paying specializations like AI/ML and quantitative analysis.

Business analysts face more risk of certain tasks being automated, but there will always be a need for professionals to act as the "translator" between raw data and actionable business insights. LinkedIn ranks business analysis as one of the top 10 most in-demand skills in 2020. And there is ample opportunity for career progression into in-demand and well-paid roles like analytics manager, data scientist, and management consultant.

Choosing the Right Path

Now that you understand the nitty-gritty differences between data scientists and business analysts, how do you decide which path to pursue? Here are the key factors to consider:

1. Interests: Are you more passionate about statistics, modeling, and building algorithms or drawing insights to inform business strategy? Do you prefer coding and independent analysis or lots of interaction with stakeholders?

2. Skills: How strong are your math, statistics, and programming abilities? Do you enjoy the challenge of mastering new technical concepts? Be honest about your current aptitude and bandwidth to pick up new skills.

3. Education: How much time and resources are you willing to invest in your education? Data science increasingly necessitates a Master‘s or even Ph.D., while business analysis is easier to break into with just a Bachelor‘s and self-study.

4. Career goals: Consider your long-term career aspirations. If you want to be on the cutting-edge of AI research or do pure data analysis, then data science is the clear choice. But if you‘re more interested in the business applications of data and moving into management, then business analysis could be the better bet.

5. Personality: Data science tends to attract introverts who enjoy independent problem-solving, while business analysis is typically better suited for extroverted "people persons." Of course, both roles require a mix of solo work and collaboration.

If you‘re quantitatively gifted, relish the challenge of building sophisticated models, and want to focus on pure data analysis, then the data scientist path may be an excellent fit. But if you‘re more passionate about applying data to real-world business problems, influencing strategy, and developing leadership skills, then business analyst could be your ideal role.

And if you‘re still unsure, then the good news is that you can always start in one role and transfer to the other as you gain experience and hone your interests. It‘s very common for business analysts to upskill into data scientists and vice versa. The two roles actually make for very complementary skill sets.

Getting Started

No matter which path you choose – data scientist or business analyst – there has never been a better time to build a successful and high-impact career in analytics. Here are some resources and next steps to kickstart your journey:

• Education: Look for data science and business analytics degrees, certifications, bootcamps, and MOOCs from providers like Coursera, edX, Udacity, DataCamp, and General Assembly. A Master‘s degree in a quantitative field can be very valuable for data science.

• Skill-building: Practice your data skills through hands-on projects, Kaggle competitions, open-source contributions, and pro bono work. Build an online portfolio to showcase your abilities.

• Networking: Join data science and business analytics online communities, attend industry events and conferences, and participate in local meetups to make connections and learn about job opportunities. Don‘t be shy about reaching out to people for informational interviews.

• Job hunting: In addition to traditional job boards like Indeed and LinkedIn, check out data-specific platforms like DataJobs and Kaggle Jobs. Consider internships and contractual work to get your foot in the door. And tailor your resume and cover letters to emphasize relevant data projects.

The initial hurdles to breaking into data science and business analytics can seem daunting, but determination and hard work can take you far. If you‘re truly passionate about using data to drive value, then you‘ll find a way to make your mark, no matter your starting point.

Conclusion

Data scientists and business analysts are two sides of the same data coin. Both roles leverage data to generate insights, inform decisions, and build better businesses. But data scientists lean more heavily on advanced statistics and machine learning, while business analysts focus more on telling stories through data and influencing strategy.

When choosing between the two paths, aspiring data professionals should carefully evaluate their interests, skills, goals, and values to determine the best fit. Factors like educational requirements, day-to-day tasks, and interaction levels with stakeholders can all sway your decision.

But regardless of which role you choose, one thing is clear: Companies of all sizes and sectors are scrambling to hire talented data scientists and business analysts. By building the right mix of technical and business skills, you‘ll be well-positioned to build a successful and future-proof career in our data-driven world.