Organizations rely on valuable insights to drive their success in today’s data-driven world. Business Analytics and Data Science are two key disciplines at the forefront of this data revolution. Similarly, professionals in the roles of Business Analyst vs Data Scientist play crucial roles in extracting meaningful insights from data. But what sets them apart? In this article, we delve into the fascinating realms of Business Analytics vs Data Science and explore the distinctive responsibilities and skills of Business Analysts and Data Scientists. Join us as we unravel the power of data and uncover the nuances between these disciplines, shedding light on their respective contributions in leveraging data to make informed business decisions.
Business Analytics is the practice of using data analysis and statistical methods to derive meaningful insights and make data-driven decisions that optimize business performance. It involves collecting, organizing, and analyzing data from various sources to identify trends, patterns, and correlations. Business analytics focuses on solving specific business problems, improving efficiency, identifying opportunities, and guiding strategic planning by providing actionable insights and recommendations.
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Data Science is a multidisciplinary field that combines statistical analysis, machine learning, data visualization, and computer science to extract knowledge and insights from structured and unstructured data. Data scientists employ a combination of data exploration, data preprocessing, predictive modeling, and data visualization techniques to uncover patterns, make predictions, and gain valuable insights that drive decision-making and solve complex problems across various industries. Data science encompasses the entire data lifecycle, from data collection and cleaning to analysis and interpretation.
The skills and tools required to make a career in Business Analytics and Data Science differ to a great extent. Here is the list of the same for both profiles:
Business Analytics professionals must be proficient in presenting business simulations and business planning. A large part of their role would be to analyze business trends. For eg, web analytics/pricing analytics.
Some of the tools used extensively in business analytics are Excel, Tableau, SQL, Python. The most commonly used techniques are – Statistical Methods, Forecasting, Predictive Modeling and storytelling.
A data scientist must be proficient in Linear algebra, programming, computer science fundamentals. Some examples of data science projects vary from building recommendation engines to personalized E-mails.
The common tools of a data scientist are R, Python, scikit-learn, Keras, PyTorch and the most widely used techniques are Statistics, Machine Learning, Deep Learning, NLP, CV.
And for both the roles, structure thinking, and problem formulation is a key skill to do well in their respective domain.
| Data Science | Business Analytics |
|---|---|
| Data Science involves advanced statistical analysis, machine learning, and predictive modeling techniques to extract insights and solve complex problems. | Business Analytics focuses on using data analysis and statistical methods to make data-driven decisions that optimize business performance. |
| Data Science utilizes programming languages like Python or R, as well as big data technologies, to handle and process both structured and unstructured data from various sources. | Business Analytics relies on tools like Excel, SQL, and data visualization software to work primarily with structured data from internal systems and databases. |
| Strong programming, mathematical, and statistical skills are required in Data Science. | Business Analytics requires strong analytical skills, statistical knowledge, and business acumen for effective decision-making. |
| Data Science can involve data engineering and big data processing tasks. | Business Analytics primarily focuses on data analysis and interpretation rather than data engineering. |
| Data Science finds applications in industries such as finance, healthcare, retail, and technology. | Business Analytics is applied in various industries, including finance, marketing, operations, and more. |
| Data Science roles typically command higher salaries due to the advanced technical expertise required. | Salaries in Business Analytics may vary based on industry, location, and experience, but they are generally lower compared to Data Science roles. |
Let us take an example of an exciting electrical vehicle startup. This startup is now big for creating job families. And, they have decided to create three job families, one is a scientist, and the other two are an engineer and a management professional. Now I want you to take time and imagine what kind of role they play in the company.
We can infer their role from the general level of understanding:
Now, let’s take these roles and convert it to data-based profiles.
| Business Analyst | Data Scientist |
|---|---|
| Focuses on understanding business needs and requirements and translating them into actionable insights and recommendations. | Focuses on extracting insights and knowledge from data to solve complex problems and drive decision-making. |
| Works closely with stakeholders to gather requirements, define metrics, and identify business opportunities. | Collaborates with stakeholders to understand their data needs and formulate data-driven strategies. |
| Utilizes business analysis techniques such as requirements gathering, process mapping, and data visualization to support decision-making. | Utilizes advanced statistical analysis, machine learning, and predictive modeling techniques to uncover patterns and make predictions. |
| Develops reports, dashboards, and presentations to communicate insights and recommendations to stakeholders. | Develops and deploys sophisticated models and algorithms to analyze large volumes of data and derive actionable insights. |
| Proficient in tools like Excel, SQL, and data visualization software to analyze and present data effectively. | Proficient in programming languages like Python or R, as well as big data technologies, for data manipulation and analysis. |
| Possesses strong business acumen, domain knowledge, and communication skills to bridge the gap between technical analysis and business objectives. | Possesses strong analytical, mathematical, and statistical skills to tackle complex data problems and generate insights. |
| Works across various business domains, such as finance, marketing, operations, and supply chain management. | Works across industries like finance, healthcare, technology, and retail, applying data science techniques to solve domain-specific challenges. |
| Collaborates with technical teams, such as data engineers and developers, to ensure data availability and quality. | Collaborates with cross-functional teams, including domain experts, data engineers, and business leaders, to understand data requirements and align with organizational goals. |
| Salary ranges vary based on industry, experience, and location, with mid-level business analysts earning around $70,000 to $90,000 per year. | Data scientists typically command higher salaries, with mid-level professionals earning around $90,000 to $120,000 per year, depending on factors like experience and expertise. |
This is a very basic analogy that you need to keep in mind to differentiate the role of Data Scientist, Business Analyst, and Data Engineer.
Caution: These terms are losely used in the industry. The exact role can depend on the maturity of your organization in data initiatives.
Now that we have our basic analogy clear, let us see the kinds of problem solved by data scientists and business analysts.
To understand the difference between a business analyst and a data scientist, it is imperative to understand the problems or projects they work on. Let us take up an interesting example. Imagine that you are a manager of a bank and you decide to implement two important projects. You have a team of a data scientist and a business analyst. How will you do the project mapping job? Below are two problem statements:
Take your time to understand the problems. What do you think, which problem is best suited for which profile?
The first problem statement requires making several business assumptions and incorporating macro changes into the strategy. This will require more business expertise and decision making, this will be the job of a business analyst.
The second problem statement requires processing vast behavioral data from customers and understanding hidden patterns. For this, the professional should have a very good understanding of problem formulation and algorithms. A data scientist will be a suitable person to tackle this kind of specific and complex problem.
A Data scientist’s strengths lie in coding, mathematics, and research abilities and require continuous learning along the career journey whereas a business analyst needs to be more of a strategic thinker and have a strong ability in project management.
Business Analyst tends to take business roles, strategic roles, and entrepreneurship roles as they progress through career while we notice that data scientist are more of tech entrepreneur roles as they have a strong technical background.
You can refer to the following career path to see a more in-depth route from the start of data science and business analytics journey:
As we conclude our exploration of Business Analytics vs. Data Science and the roles of Business Analysts vs. Data Scientists, one thing becomes clear: the world of data holds immense potential for driving business growth and innovation. Whether you choose the strategic application of data in business operations or the advanced data analysis and modeling techniques, there is a path for you to excel.
To further enhance your skills and gain a competitive edge in Business Analytics, consider enrolling in our Certified Business Analytics Professional (CBAP) program. This comprehensive program equips you with the necessary knowledge and practical expertise to become a proficient Business Analyst adept at translating data insights into actionable strategies.
For those aspiring to master the intricacies of Data Science and take on the role of a Data Scientist, our Blackbelt program offers an immersive learning experience. With a focus on advanced statistical analysis, machine learning algorithms, and real-world projects, this program helps you become a seasoned data expert.
Remember, the power of data lies in your hands. Choose your path and embark on an exciting adventure with endless possibilities.
A. The choice between business analytics and data science depends on your interests, career goals, and the specific job market you are targeting. Here are some insights to consider:
Business Analytics: Business analytics focuses on analyzing data to gain insights and make data-driven decisions to optimize business operations, improve efficiency, and drive strategic planning.
Data Science: Data science involves extracting actionable insights and building predictive models using advanced statistical analysis and machine learning techniques to solve complex problems and support decision-making.
A. Salaries can vary based on factors such as location, industry, experience, and skill set. In general, data science roles tend to command higher salaries compared to business analytics positions due to the higher demand for advanced technical skills and expertise.
A. Business analytics may involve coding, but the extent of coding required can vary depending on the specific role and organization. Basic coding skills, such as working with data manipulation and visualization in tools like Python or R, are often beneficial for business analysts.
A. The choice between an MBA and business analytics depends on your career goals. If you aim to develop a comprehensive business skill set and pursue leadership roles across various industries, an MBA may be a better fit. However, if you have a specific interest in data analysis and want to focus on leveraging data for decision-making, business analytics can offer more specialized training in that area.
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While I see a lot of influencers and mentors who post and guide about Data Science on LinkedIn, I rarely see anyone for Business Analytics! Can you please help me with a list of such people to follow?
Thanks for the information. It was detailed and easy to understand
Thanks Tarul - Glad you found it useful!
Thanks for such a great article. Cleared my doubt
Thanks Rajan. Really glad that it helped you!
Thanks for the explanation. I currently work for a tech company creating automated reports on a daily basis, using sql, google sheets and scripts, so I'd like to think I'm a Business analyst, however, I think I'm far from making business decisions as I'm just doing what I'm asked, so I'm not sure that I'm a formal business analyst, I guess everyone starts somewhere.
Thank you so much. I'm currently going through a journey of deciding what my focus should be on. It's a bit difficult for me because I'm supporting a tech team and don't have strong programming/computer science skills.
Great piece Thanks alot
Thanks Charles!
Excellent explanation makes the sometime confusing terminologies clear. What I see a linear relationship between Data Science > Data Engineering > Business Analysis. Outcomes of data science are the tools data engineers can use to develop analyic applications which eventually will be used by the business analyst and managers for strategic decision making.
Thank You for this simple explanation..Now, I have a better understanding of the three. However, I need your utmost advice. I have put in 14years into my career as an accountant /Internal Auditor. I also have second degree in marketing. I will like to develop my self in other field to be able to fit into the emerging economy. I decided to dive into Business Analytics. Now my question, Do you think this course will be relevant for me looking at my career background? Presently, I am in Africa but intending to relocate to a more developed country. Thank You.
Thanks for this detailed post on the differentiation between these two terms in the industry.
Great article. It really helps me.
Thanks for the information
Thanks for the Detail Info. I am In transition now. As a Senior QA with 10 years experience was confused between data Scientist Vs Data engineer Vs Business Analytic course. Now I know which one is suitable and progress of journey in Big Data is in detail
The terms are deeply intertwined with each other and hence the confusion is bound to be there. Glad this article helped you!
Interesting! I have understood a lot with this summary you made. Now i can easily differentiate between the two. Thank you.
Thank you! Glad you found it useful!
Thank you, for the article. I am an undergraduate in Economics, and currently looking for jobs. These two career paths were confusing to me also.
The confusion is inevitable given the fact that these terms are used loosely in the industry! Glad you liked it!
Thank you.Really a nice article.
Thank you! Glad you liked it!
Interesting and very useful piece of information. Worth sharing ...
Thanks it was useful
That is explained very well!! But what i am trying to understand is which one should i choose if i want to excel more in digital marketting? Advice will be helpful
Precise even to the feeble minded . Loved it!!
Thank you really details and help yiu choose wisely