Today’s business leaders have greater access to meaningful data than ever before. When correctly stored, analyzed, and interpreted, this data fuels important business decision-making. Businesses are motivated to keep data private, accurate, and powerful. How do they achieve that goal? By recruiting business analysts or data scientists.
Defining Business Analytics vs. Data Science
The fields of business analytics and data science have key distinctions, and each field uses essential tools. Further, business analysts and data scientists play significant roles in developing data-driven business strategies. The roles offer value in different ways.
Business Analyst Role
Business analysts work with large datasets, extracting information to support decision-makers. This work helps executives understand past business performance and current market position with an eye to making predictions about the future.
Business analysts must carefully evaluate data to draw business-relevant conclusions. They also need to present their findings clearly and persuasively to business leaders. This often involves data visualization (presenting information via graphs, charts, or other visual means).
Data Scientist Role
Data scientists work at the front end of data analysis; typically, their job is to build algorithms or other mathematical structures that can aid in data collection. This highly technical role is essential for providing companies with the means to gather and store information, which analysts can later examine to form meaningful business insights.
Essential for data analysts are foundational skills in mathematics and machine learning.
Role of IT
Information technology (IT) connects data science with business analytics. For example, the IT team:
- Works alongside data scientists to create databases where information can be stored
- Ensures business analysts have easy access to databases and can extrapolate the information they need
- Provides security measures to protect sensitive data and ward off cyberattacks
- Promotes data collection standards that protect the privacy of individual users
Data scientists and business analysts rely on IT professionals to help them solve crucial data challenges. For example, IT creates data privacy and security parameters. IT also maintains software and cloud computing applications that ensure accurate data collection.
Business Analytics vs. Data Science: Additional Resources
Find additional insight into the fields of business analytics and data science in the following resources:
- CIO, “What Is a Business Analyst? A Key Role for Business-IT Efficiency.” Learn more about the job description and core responsibilities associated with the business analyst role.
- CIO, “What Is a Data Scientist? A Key Data Analytics Role and a Lucrative Career.” Gain further understanding of the data scientist role and its value to a business.
- Entrepreneur, “5 Ways Big Data Can Help Your Business Succeed.” Discover practical implications of big data use.
Business Analytics Tools at a Glance
Several tools are available to help business analysts analyze and interpret data and provide useful visualizations to key decision-makers.
Tableau
A core responsibility of the business analyst is taking raw data and turning it into an analysis that businesspeople understand. Tableau helps with this; it’s primarily a data visualization tool that converts raw or unstructured data into an easily understandable visual format. These formats include worksheets and dashboards from which executives and other business leaders can easily spot statistical trends.
To learn more about Tableau, consider these resources:
- Tableau. Learn more about the product and its various features.
- Tableau, “7 Tips and Tricks from the Dashboard Experts.” Get insider tips on using Tableau effectively.
Oracle BI
Using both machine learning and artificial intelligence (AI), Oracle BI is a software suite that performs data analytics throughout an organization. Specifically, Oracle BI lets data analysts build role-specific data collection tools, so each individual and department can consolidate and visualize data. In other words, the marketing team can develop a database, while the finance team can devise their own spreadsheets. The data analyst can access, compare, and analyze data from throughout the organization.
For more information about Oracle BI, take a look at these resources:
- Oracle Business Intelligence. Take a closer look at the product, its specs, and its list of features.
- Rackspace, “Tips for Using Oracle Business Intelligence Enterprise Edition.” Learn how to use this software to its full potential.
Sisense
Sisense is a “big data” processing program that allows businesses to input large or unruly sets of data, then generate relevant trends and statistics from that data. (Big data simply refers to datasets that are too large or too complicated for an individual human to effectively process.) Sisense offers a number of options for visualization and reporting, so relevant findings can be easily shared throughout an organization.
To further explore Sisense, use these resources:
- Sisense. Take a look at the Sisense software suite and consider its unique features.
- Sisense, “Tips and Tricks for Sisense Masters.” View video tutorials and learn advanced features.
Wrike
Wrike is primarily a project management tool that allows team members to share files, collaborate on projects, and keep track of due dates. However, the program also has an “Analyze” function, which allows team members to create visualizations for each project and assess their progress.
For more about Wrike, consider:
- Wrike. Get more information about the full Wrike software suite.
- Wrike, “12 Ways to Use Wrike You Never Considered.” Explore some unique ways to leverage Wrike.
Trello
Like Wrike, Trello is primarily a project management platform. It, too, offers analytic options, allowing users to input datasets and then create and share charts, reports, dashboards, and other visualizations. Trello allows team members to collaborate using foundational, easily digestible data points.
Learn more about Trello at these links:
- Trello. Find out more about Trello’s broad spectrum of collaborative features.
- PC Magazine, “10 Trello Tips Guaranteed to Make You More Productive.” Learn more about how Trello can increase your efficiency and productivity.
More About Business Analytics Tools
To learn more about recommended business analytics tools, consider the following resources:
- TechRepublic, “How to Choose the Right Data Analytics Tools: 5 Steps.” Get advice on selecting the right data analytics tools for your business or team.
- Guru99, “24 Best Business Intelligence (BI) Tools List in 2020.” Get recommendations for additional business analytics tools.
A Look at Data Science Tools
In addition to business analytics tools, businesses benefit from data science tools. Data scientists use such programs to gather data and turn it into usable information.
Apache Hadoop
Apache Hadoop is a framework that allows data scientists to efficiently store and process big datasets. Rather than using a single computer to store and analyze data, data scientists can “cluster” multiple computers with Apache Hadoop, resulting in their ability to quickly process enormous datasets.
To learn more, consider these resources:
- Apache Hadoop. Explore what this software solution can offer.
- IBM, “10 Expert Tips to Boost Agility with Hadoop as a Service.” Learn more about using Apache Hadoop and Spark.
Apache Spark
Apache Spark shares many similarities with Hadoop, but it offers a key difference. While Hadoop processes huge data batches with relative speed, Spark processes data in real-time. In other words, Spark is the preferred option for data scientists who need to interact with the data as it’s processing.
For more, check out these links:
- Apache Spark. Find out more about what Apache Spark is capable of.
- Towards Data Science, “Apache Spark Optimization Toolkits.” Discover some resources for using Apache Spark more effectively.
Microsoft Excel
Microsoft Excel is a familiar spreadsheet program that also works well for data science and business analytics. Excel allows data scientists not only to compile large volumes of information but also to run formulas that automatically synthesize data or extrapolate key conclusions. Another benefit of Excel is that, because it is a commonplace program, it allows data scientists to present information in a way that will be understandable to non-data scientists.
Learn more about Microsoft Excel:
- Microsoft Excel. See the full list of software specs and features.
- Excel with Business, “15 Excel Data Analysis Functions You Need to Know.” Explore the ways in which Excel can help with business analysis.
Matplotlib
Many data scientists rely on the Python coding language to efficiently interpret data. Matplotlib goes hand in hand with Python, allowing the data scientist to take Python data and convert it into animations and other visualizations.
Find out more:
- Matplotlib. Take a look at Matplotlib’s features and options.
- Real Python, “Python Plotting With Matplotlib (Guide).” Get a full tutorial on using Matplotlib for Python plotting.
BigML
Machine learning is an important concept in data science. BigML is a good example. This program allows data scientists to import large datasets. BigML then uses machine learning capabilities to examine that data and offer a set of statistical conclusions.
For more about BigML, consider these resources:
- BigML. Take a closer look at BigML’s features and tools.
- BigML, “Tutorials.” Find out more about how to use BigML effectively.
More About Data Science Tools
For additional insight into data science tools, consider these resources.
- Towards Data Science, “Best Data Science Tools for Data Scientists.” Consult this list for additional data science processing recommendations.
- GeekFlare, “18 Essential Software Every Data Scientist Should Know About.” Check out this article for additional software tips.
Business Analytics and Data Science Languages
When considering business analytics vs. data science, be aware of the importance of programming languages. Through familiarity with these complex languages, business analysts and data scientists can build data-driven strategies tailored to business needs. Here’s an outline of four of the most common languages used in these fields.
Python
Python is a favorite language among data scientists, as it provides a library of existing codes and formulas that can efficiently manage large sets of data. Compared to other coding languages, Python is considered fairly easy to learn and straightforward to use, making it an accessible option for novices in the field.
To learn more about Python, consider:Python for Beginners. This tutorial will walk you through the process of downloading Python and getting started.
R
While Python is a generalized data science language, R is specifically geared toward statistical analysis. In other words, it’s a language built by statisticians, and it encapsulates their particular discipline. Many data scientists learn both Python and R, believing they work best when used in tandem.
For more insight into R, consider:Free Code Camp, “R Programming Tutorial.” View an in-depth video tutorial about learning and using R.
SQL
SQL, or Structured Query Language, is used to access, manipulate, and revise large datasets. Specifically, SQL helps data scientists communicate with relational databases (which store different data points that are all related to one another), allowing for the effective use of these large, interconnected datasets.
Learn more:Programming with Mosh, “McSQL Course for Beginners.” View a full YouTube course about SQL programming.
SAS
SAS (Statistical Analysis System) is a command-driven software platform, useful in both data analysis and data visualization. SAS is sometimes compared with R and is generally considered to be more user-friendly, as it can be learned without any preexisting coding or programming experience.
Find out more about SAS:SAS Crunch, “How to Learn SAS Fast.” Check out this full primer on using SAS.
More Information About Coding Languages
To learn more about these languages and their import for data science, take a look at some of these resources:
- Guru99, “R vs. Python: What’s the Difference?” Learn more about the distinctions between R and Python.
- Towards Data Science, “SQL for Data Science.” Find out more about the use of SQL in the field of data science.
Business Analytics vs. Data Science: Harnessing the Power of Big Data
Both business analytics and data science allow large enterprises to use their data effectively and make well-informed decisions about their business strategy. While these disciplines are not identical, both of them provide meaningful pathways for people interested in data, statistics, and business leadership.
Additional Resources:
Business News Daily, “How Businesses Are Collecting Data (and What They’re Doing with It)”
Business News Daily, “9 Big Data Solutions for Small Businesses”
Business News Daily, “10 Best Resources for Learning How to Code”
Dataquest, Data Science Resources
DreamHost, “The 67 Best Online Resources to Learn How to Code (Updated 2020)”
Learning Hub, “44 Noteworthy Big Data Statistics”
Learning Hub, “50 Best Open Data Sources Ready to Be Used Right Now”National Federation of Independent Business, Data Sources
Statista, Big Data — Statistics & Facts
Tech Republic, “Big Data: 3 Biggest Challenges for Businesses”
Tech Republic, “Python Programming Language: Best Resources for Developers and Managers”
FAQs
Business Analytics vs. Data Science | Ohio University? ›
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.
Can I become a data scientist with a Business Analytics degree? ›Although the most obvious choice for a graduate in business analytics would be to work as a data scientist or a management analyst, there is a range of careers available for a professional who is capable of dealing with big data and translating it to business solutions.
Which pays more data science or Business Analytics? ›Data Scientist Vs Business Analyst – Salary. According to Glassdoor, a Business Analyst earns an annual income of $69,163/yr. Whereas, a Data Scientist earns an annual income of $117,345/yr.
Can you be a data scientist with a masters in Business Analytics? ›The education requirements to become a data scientist vs business analyst differ slightly. Most data scientists pursue a master's degree before entering the field open_in_new, while many business analysts launch their careers with just a bachelor's degree open_in_new.
Is Masters in Business Analytics and data science same? ›In general, an MS in Data Science offers an in-depth exploration of foundational programming and math concepts, centered on quantitative theory, while a MS in Business Analytics is more focused on business outcomes and provides knowledge of analytics skills and leadership techniques.
Which degree is best for business analytics? ›One of the most common degrees for business analysts is a bachelor's in business administration. Business analysts can also hold degrees in computer science, finance, economics, or accounting.
Does business analytics involve coding? ›Technical Skills for Business Analytics
Having both a conceptual and working understanding of tools and programming languages is important to translate data sources into tangible solutions. SQL is the coding language of databases and one of the most important tools in an analytics professional's toolkit.
An MSBA is a particularly valuable addition to your skills package if you want to grow your career. In fact, if a professional doesn't pursue a master's degree at the mid-level career stage, it's almost essential to pursue one in preparation for senior leadership roles.
Should I become a business analyst or data analyst? ›Data analysts tend to work more closely with the data itself, while business analysts tend to be more involved in addressing business needs and recommending solutions. Both are highly sought-after roles that are typically well-compensated.
Can you make 6 figures as a data analyst? ›A low six-figure salary is really the limit of most data analytics roles, though. To earn more, you'll need to transition into a more senior position, such as a data scientist or finance manager.
Can I become a data scientist after MBA in business analytics? ›
Graduates of master's in business administration programs often specialise in data analytics and data science and are employable in many fields.
Is Masters in business analytics in demand? ›Since demand and growth for business analytics professionals are so high, expect to see great returns for your investment in a graduate degree.
Can you go from business intelligence to data science? ›The transition from Business Intelligence to Data Science. BI professionals have a great advantage over anyone else trying to shift into the data science domain. This is because they work with data scientists on various projects and already have the knowledge on how to handle data.
Does data science require coding? ›1. Does Data Science Require Coding? Yes, data science needs coding because it uses languages like Python and R to create machine-learning models and deal with large datasets.
Is data science a master or MBA? ›An MBA in data science helps students to convert data into key business insights and incorporate in-demand tools and technologies, preparing them for the modern business landscape. On the other hand, M.Sc in data science will equip the learners with tools and techniques, making them part of the current industry trends.
Is business analytics hard? ›It's moderately hard to become a business analyst. You should have soft and technical skills and the proper education to become a successful business analyst.
Is there a demand for business analytics? ›As most business analysts directly contribute to the company's growth and vital decisions, they are in demand and also paid considerably high. A business analyst's work usually revolves around research, data mining, and visualization.
Is business analytics in high demand? ›Job outlook
The demand for business analysts has increased in recent years and is projected to continue. The US Bureau of Labor Statistics (BLS) projects job growth between 2020 and 2030 for similar roles to range from seven percent (computer systems analysts) to 25 percent (operations research analysts) [2, 3].
Is an MBA in Business Analytics worth it? Yes. From marketing managers seeking data about customers to financial analysts calculating investment decisions, data has become a fundamental part of the business.
Is Python used in business analytics? ›Business Analytics using Python is a five-day instructor-led classroom course. The course graduates from basic level to advanced topics carefully designed to make it ideal for candidates with or without prior experience in Python programming and data analytics.
Is SQL necessary for business analyst? ›
Because they often perform many roles, business analysts are among the most sought-after professionals in the business world. One of the fundamental skills every business analyst should have in their analytical toolbox is SQL.
Is business analytics math heavy? ›This is a very math-heavy role that involves complicated statistical calculations. Data analysts focus on reaching conclusions from data, so they need a strong background in mathematics to excel in this role.
Is business analytics a stem degree? ›Yes, business analytics is a STEM degree. Business analytics degrees are highly technical. A degree in business analytics can include courses like descriptive analytics, data mining, and discrete mathematics.
Is business analytics stressful? ›Yes, being a business analyst is a stressful job.
This is because their role is involved with looking at huge amounts of data, synthesizing it, and using that information to help their company make decisions.
The difference is what they do with it. Business analysts use data to make strategic business decisions. Data analysts gather data, manipulate it, identify useful information from it, and transform their findings into digestible insights. Analyzing data is their end goal.
Which is more difficult data analyst or business analyst? ›Business analysts must have at least a working knowledge of the technology involved in analytics, though the need for hard technical skills is generally lower than for data analysts.
Who earns more data analyst or data scientist? ›According to Glassdoor, the average salary of a Data Scientist in the US is $100,000 per annum. As per Glassdoor, the average salary of a data analyst in India is 6 Lac rupees per annum. In India, the average salary of a Data Scientist is 9 Lac rupees per annum.
Can a data scientist make 300k a year? ›Data scientists with significant experience, expertise, and specialized skills can potentially earn salaries exceeding $300,000 per year.
Can I make 100k as a data analyst? ›Can you make 100k as a data analyst? The numbers don't lie: You certainly can earn 100k as a data analyst—sometimes even more! While you're unlikely to earn a six-figure salary in your very first data role, it's definitely an achievable goal as you work your way through your career.
Do you need high IQ for data analyst? ›As for data science, it turns out you need to have an IQ of 150 (3 std up above the average population). The truth is that IQ is purely genetic (meaning you cannot improve your IQ and at best you can up about 2 points basis), and it is in fact a good way to measure your intelligence and success besides consciousness.
What field is business analytics under? ›
Business analytics combines the fields of management, business and computer science. The business aspect entails both a high-level understanding of the business as well as the practical limitations that exist. The analytical part involves an understanding of data, statistics and computer science.
Is MBA after data science worth it? ›As the industry is expanding, the need for management in it is also increasing and hence an MBA in Data Science is definitely worth considering.
Which MBA is best for data scientist? ›- MBA in Data Science and Machine Learning. ...
- MBA in Fintech and Data Analytics. ...
- MBA in Strategic Data-Driven Management. ...
- Master of Business Administration [Online] ...
- Professional MBA Digital Transformation & Data Science. ...
- The UCL MBA. ...
- MBA (Data & Cyber Management) ...
- MBA Data Analytics.
The average salary for MS in Business Analytics in USA ranges between 66,500 to 191,000 USD (55.01 lakhs to 1.58 lakhs INR) per annum based on skills, experience, and location.
What is the salary after Masters in business analytics in USA? ›According to data by PayScale, MS in business analytics in USA salary on average is 75,000 USD. It is quite high as compared to other programs in USA. In this blog, we will further look at the and the average salary for MS in business analytics in USA, along with tips to find jobs after MS in business analytics in USA.
How much does MIT business analytics pay? ›The average base salary increased 3.7% to $132,413, and the average signing bonus increased 28% to $26,189. The Class of 2022 found opportunities to drive transformation through data at over 30 companies.
Can a business analyst switch to data scientist? ›Business Analysts can successfully transition to Data Scientists with the right training, education, and experience.
Is business intelligence easier than data science? ›In general, business intelligence focuses on analyzing past events, while data science aims to predict future trends. Data science requires a more technical skill set compared to business intelligence.
What is the difference between a business intelligence analyst and a data scientist? ›While BI focuses on generating reports based on the internal structured data, Data Science focuses on generating insights out of the data. These insights are generated as a result of complex predictive analytics and the output presented is not a report but a data model.
Is Python must for data science? ›The demand for both data scientists and data analysis will increase by over 1000% over the next few years; it's time for you to make your move. Whether you want to become a data analyst or make the big leap to data scientist, learning and mastering Python is an absolute must!
What kind of degree do you need to be a data scientist? ›
Pursue an undergraduate degree in data science or a closely related field. You will generally need at least a bachelor's degree in data science or a computer-related field to get your foot in the door as an entry-level data scientist.
Is only Python enough for data science? ›It's possible to work as a data scientist using either Python or R. Each language has its strengths and weaknesses. Both are widely used in the industry. Python is more popular overall, but R dominates in some industries (particularly in academia and research).
Who earns more data scientist or MBA graduate? ›...
MBA vs Data Science: Which is a Better Career Move?
| Parameters | Postgraduate Program in Data Science | General MBA |
|---|---|---|
| Highest Package | INR 32.21 lakhs per annum | INR 32.29 lakhs per annum |
| Average Salary | INR 10.71 lakhs per annum | INR 10.50 lakhs per annum |
| Students Placed | 100% | 100% |
While earning a master's degree in data science comes with certain costs—in terms of both tuition and time—it can be a worthwhile investment when you're interested in furthering your abilities to work with and parse data.
Why data science is better than MBA? ›Technical skills: The main difference between MBA in Analytics and Data Science and other domains is that the other domains are solely focused on managerial aspects. But data science clubs technical skills and managerial skills. A person will learn technical skills like SQL, Python and data visualisation.
Is a business analytics minor worth it? ›A minor in business analytics can give you a competitive advantage in almost any career path. The increasing availability of data in today's world means people who are trained to understand and apply that data to business decisions can find opportunities in almost any industry.
Why study business analytics in USA? ›Business analytics is a STEM program. Studying in the US offers international exposure to students as they get to work with global brands. The salary range in the US for a business analytics graduate is₹40 lakh to 67 lakh. A master's in business analytics from the US opens lucrative career opportunities.
Can you become a data engineer with a business analytics degree? ›For example, business analytics graduate degrees can provide data engineers with the skills to examine and interpret large data sets. Knowledge of business analytics also enables them to use advanced analytical tools and techniques and create unique data models and databases to support business solutions.
Is MBA in business analytics tough? ›An MBA in Business Analytics is not tough. This course doesn't check your hardcore technical aptitude like the data science course. Business analytics is the field concerned with data therefore, an MBA in Business Analytics is more about generating real-world insights from large bodies of data.
Is an MBA in business analytics worth it? ›A master's degree in business analytics can boost your salary. New job opportunities may mean a pay bump, too, Harter says. “An MSBA allows you to jump into a high growth area with high potential salaries that might more than justify the investment in a program like this,” he says.
Is business analytics easier than data science? ›
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.
Is a BS in data analytics worth it? ›#1: Lucrative Compensation
While the types of jobs you'll be qualified for vary slightly, all of them are well-paid. For example, according to the Bureau of Labor Statistics (BLS), the median salary of a data scientist in May 2021 was $100,910. This salary alone makes a Data Analytics degree worthwhile.
This is the main difference between the two fields: data analytics looks backward and focuses on past data, aiming to identify trends (by describing the past and diagnosing why certain events happened). Data science looks forward and focuses on the future (by predicting it or prescribing what should happen).
Can a data scientist be a business intelligence analyst? ›There are a variety of great business intelligence jobs open to data scientists. What is business intelligence? Business intelligence involves using data to garner insights that can help businesses make strategic decisions and drive up business value.