Contents
When I was narrowing down the courses for my MS in Business Analytics degree, admittedly, I was confused. The confusion was about two specific things: What will I learn about? And what jobs will I be able to pursue? It became important to learn the distinction between two similar degrees: business analytics and data science.
To begin my discovery path, I jumped to the jobs section in my search. One of the most attractive qualities of these degrees was the salaries. According to ONET OnLine, a government-sponsored database, a business analyst earns an average of $88,550 per year while a data scientist earns $122,840 per year. The figures looked attractive to me, but why were they so different? I had to go back to the basics and find out.
Are data science and business analytics relevant degrees?
Finding the differences between data science and data analytics might not be an isolated query just for professionals. Internet use has increased by 70% since this past spring — making the appropriate use of data essential. The sectors of business, healthcare, entertainment, manufacturing, transportation, banking, and others, precisely monitor data to make business decisions. According to my findings, there is nothing but growing opportunities in these fields. Even the general public will soon use some of the technologies and strategies to use data more effectively and efficiently. That’s why learning the difference between business analytics and data science is relevant to many.
| Business Analytics | Data Science |
|---|---|
| Business Analytics is the statistical study of business data to gain insights. | Data science is the study of data using statistics, algorithms and technology. |
| Uses mostly structured data. | Uses both structured and unstructured data. |
| Does not involve much coding. It is more statistics oriented. | Coding is widely used. This field is a combination of traditional analytics practice with good computer science knowledge. |
| The whole analysis is based on statistical concepts. | Statistics is used at the end of analysis following coding. |
| Studies trends and patterns specific to business. | Studies almost every trend and pattern. |
| Top industries where business analytics is used: finance, healthcare, marketing, retail, supply chain, telecommunications. | Top industries/applications where data science is used: e-commerce, finance, machine learning, manufacturing. |
What is business analytics?
Business analytics bridges the gap between information technology and business by using analytics to provide data-driven recommendations. The business part requires deep business understanding, while the analytics part requires an understanding of data, statistics and computer science.
What does a business analyst do?
According to LinkedIn Talent Solutions, a business analyst acts as a communicator, facilitator and mediator, and seeks the best ways to improve processes and increase effectiveness through technology, strategy, analytic solutions, and more.
What are the skills of a business analyst?
Here is the Naveen Jindal School of Management description of the marketable skills of those who earn an MS in Business Analytics:
- Interpretation: Businesses manage a vast amount of data. As a business analyst, you should have the ability to clean data and make it useful for interpretation.
- Data Visualization and storytelling: Data visualization is an evolving discipline, and Tableau defines data visualization as a graphical representation of data and information. A business analyst uses such visual elements as charts, graphs and maps, and provides an accessible way to see and understand trends, outliers and patterns in data.
- Analytical reasoning ability: Consists of logical reasoning, critical thinking, communication, research and data analysis. A business analyst requires these to apply descriptive, predictive and prescriptive analytics in business situations to solve business problems.
- Mathematical and statistical skills: The ability to collect, organize and interpret numerical data is used for modeling, inference, estimation and forecasting in business analytics.
- Written and communication skills: If you have better communication skills, it becomes easy to influence the management team to recommend improvements and increase business opportunities.
What is data science?
Data science is the study of data using statistics, algorithms and technology. It is the process of using data to find solutions and predict outcomes for a problem statement.
What does a data scientist do?
Data scientists apply machine-learning algorithms to numbers, text, images, videos and audio, and draw various understanding from them. According to Hugo Bowne-Anderson writing in the Harward Business Review, “Data scientists lay a solid data foundation in order to perform robust analytics. Then they use online experiments, among other methods, to achieve sustainable growth.”
Finally, they build machine learning pipelines and personalized data products to better understand their business and customers and to make better decisions. In others, in tech, data science is about infrastructure, testing, machine learning, decision-making, and data products.”
What are the skills of a data scientist?
The core skills required in data science are as follows:
- Statistical analysis: You should be familiar with statistical tests, likelihood estimators for a keen sense of pattern and anomaly detection.
- Computer science and programming: Data scientists encounter massive datasets. To uncover answers to problems, you will have to write computer programs and should be proficient in computer programming languages such as Python, R and SQL.
- Machine learning: As a data scientist, you should be familiar with algorithms and statistical models that automatically enable a computer to learn from data.
- Multivariable calculus and linear algebra: This significant mathematical knowledge is needed for building a machine learning model.
- Data visualization and storytelling: After you have the data, you have to present your findings. Data scientists use data visualization tools to communicate and describe actionable insights to technical and non-technical audiences.
Data science is a vast and technical topic. To understand more about it, read the blog posts “What Data Scientists Really Do” in the Harvard Business Review, “Top 10 Skills for a Data Scientist” in Towards Data Science, and “What is Data Science” on Thinkful.
So, what is better for me?
Business analysts take a hands-on approach to their work by having to interact and manage the data while data scientists tend to focus more on data’s development. As I see it, a business analyst can transition into a data science role with more training hours and experience. I decided to jump into business analytics because of this particular flexibility.
