Data Scientist vs Business Analyst

The success of a modern business is strongly linked to how it manages data. Companies today have to carry out considerable amounts of analysis and research on the data they generate to better understand their customers and how they relate to the business’ products or services.

This task requires special skills to understand the patterns in data, determine how the data will contribute to business growth, and how changing functionalities will bring about the necessary change. This job is done by both data scientists and business analysts.

Data scientists and business analysts are sometimes used interchangeably. Both involve working with big data, but in different ways. It’s important to know the difference between data science and business analysis. This article will help you understand the major differences between these two professions.

What's The Difference Between A Data Scientist And A Business Analyst?

A Data Scientist specializes in high-level data manipulation, including writing complex algorithms and computer programming. Business Analysts are more focused on creating and interpreting reports on how the business is operating day to day, and providing recommendations based on their findings.

While business analysts normally focus on finding trends in data and coming up with tech solutions to improve an organization’s operations, data scientists are more focused on understanding what drives those trends. Having said that, both data scientists and business analysts work closely with each other to recommend solutions to stakeholders.

Both fields have tremendous growth potential, and they offer popular and rewarding career options. Graduates and early career professionals can gain rapid entry into data science, however business analytics requires experience in management and business development.

Now that you know the basic differences, we'll go deeper into the definitions of both data science and business analysis.

What Is Data Science?

Data science is an interdisciplinary field that involves the extraction and analysis of raw data to deliver structured data. Data Scientists, like Business Analysts, are involved in extracting, formatting, analyzing, and maintaining big data sets. They typically work more on the front end of the data collection and analysis process.

Since their responsibilities also include designing, developing, and deploying algorithms to collect and analyze data, they tend to develop more technical skills in these areas.

While both data science and business analysis rely on some coding knowledge and data manipulation, data science includes the more specialized fields of big data, machine learning, and artificial intelligence.

What Is Business Analytics?

Business analysis is the practice of analyzing, organizing, and enabling change in an enterprise by identifying needs and recommending solutions that deliver value to stakeholders.

A Business Analyst is an “agent of change” who is involved in defining a roadmap related to new opportunities. They analyze the data and develop actionable plans. They are also tasked with finding discrepancies within different business models, helping decision-makers to understand a company’s performance in the past and present, and forecasting future performance.

Data Scientists and Business Analysts—What do they do?

Here's a quick breakdown of the different responsibilities of Data Scientists and Business Analysts.

Data Scientists:

• Extract and organize data.
• Look for unstructured and structured data to extract meaningful insights.
• Need to have knowledge of machine learning, statistics, and mathematical skills.
• Required to know Python, Spark, TensorFlow, Hadoop and R.
• Make adjustments in machine learning models.

Business Analysts:

• Communicate with clients and look for business solutions.
• Specialize in structured data only.
• Needs to have interpersonal and management skills.
• Must have knowledge of SQL, Tableau, and Excel.
• Assist in designing and implementing tech solutions.
• Track and update business projects and growth.

Skills Used in Business Analysis and Data Science

There are distinct skills required by Data Scientists and Business Analysts. Here are the skills you'll need to develop if you want to pursue a career in either field.

Data Scientist Skills:

• Knowledge of statistics and math.
• Proficiency in tools like Python, R, Hadoop and Spark.
• Sufficient knowledge of SQL and NoSQL.
• Strong understanding of machine learning algorithms.

Business Analyst Skills:

• Strong communication.
• Data modeling and its methods.
• Ability to deal with clients.
• Knowledge of MS Excel, SWOT, PESTLE, Trello, and BEAM.
• Strong leadership.

Skill Requirements of a Data Scientist:

• Solve real world business issues by applying machine learning methods.
• Partner with fellow product teammates to create new methodologies for measurement.
• Interpret and transfer data into usable insights for business stakeholders.

While some of these responsibilities overlap with those of a Business Analyst, Data Scientists typically solve higher-level issues. Business Analysts tend to focus on the challenges that could help an organization become more profitable.

Qualifications of a Data Scientist:

• 3+ years’ experience in data analytics, data science, or a bachelor’s degree in computer science or statistics.
• A degree in math may also be useful.
• Experience in machine learning models.
• Strong analytical skills.
• An ability to structure unstructured data.
• Most importantly, you’ll need to be comfortable and patient while dealing with complex and conflicting data.

Data Scientists typically need more specialized knowledge of coding and advanced data manipulation than Business Analysts do. Business Analysts and other entry- or mid-level analysts sometimes go on to become Data Scientists.

Skill Requirements of a Business Analyst:

• Engage with users or customers and keep track of their buying patterns and needs, which will help the organization meet the business goals.
• Make sure that there is consistent growth in product usage.
• Make use of visualization to showcase products via graphs and presentations.
• Lead sessions with the IT team to understand the client’s requirements and objectives.
• Communicate latest technologies to the team and escalate problems for resolution.
• Provide user feedback to the product team.

The responsibilities of a Business Analyst could vary greatly based on their level of experience, and their industry. But the core duties of their role will be the same: every Business Analyst will have to manage large datasets and support company decisions by identifying data trends.

Qualifications of a Business Analyst:

• A technical degree, like a master’s in computer applications or a management degree such as an MBA or BBA from a reputed university or college.
• 4-5 years experience in a software or IT company.
• Interpersonal and organizational skills to influence and build effective product-user relationships.
• Strong communication and business analysis skills.
• Ability to manage customer expectations and work on projects independently.
• Ability to travel depending on the business’ requirements.

Like Data Analysts, Business Analysts are experts when it comes to cleaning, organizing, and storing data. But this role focuses on solving business challenges, and applying that knowledge to the company’s financial goals.

Top Careers in Data Science and Business Analysis

Here are some of the most popular roles that budding Data Scientists and Business Analysts aspire to.

1. Business Intelligence Analyst

A subset of business analysis, Business Intelligence Analysts turn an organization’s data into effective insights to make better decisions and maximize profits. They work on data provided to them by Data Scientists and are expected to consider it independently to find user patterns.

Business Intelligence Analysts should have a knowledge of reporting tools and databases.

The annual salary average for a business intelligence analyst is $85,635.

2. Data Scientist

Data scientists extract and design new processes for data modeling, mining, and production of structured and unstructured data. They may also develop certain algorithms and custom analysis.

The annual salary average for a data scientist is $116,654.

3. Database Manager

Database Managers are tasked with the responsibility of recognizing problems that occur within databases. These managers work very closely with database developers to provide solutions to problems and help with design.

The annual salary average for a database manager is $61,236.

4. Data Architect

These professionals are trusted with complex data sets and also help with designing the complex data frameworks or structures, as well as maintaining these databases.

The annual salary average for a Data Architect is $125,049.

Next Steps

Interested in a career as a Data Scientist or Business Analyst? You can read more about roles in this exciting sector on our data science blog, or our data analytics blog.

Find out more about career outcomes in the field of data science, or enroll in a data analytics bootcamp to gain all the foundational skills you need to crunch numbers, interpret data, and communicate your findings with data visualization.