Data Science in Business Guide: Benefits, Uses, and More

Written by Coursera Staff • Updated on

This article explores the benefits of using data science in business. Learn about certifications that you can achieve in data science and how to apply them to your business.

[Featured Image] Two data scientists work together on a desktop computer.

Businesses employ data scientists, who collect, clean, organize, and analyze large data sets, in order to solve a business problem and develop actionable insights. In their daily work, consequently, data scientists plunder big data to determine trends and predictions and form conjectures that companies use to make decisions about their operations, target audiences, or products.  Data is all around us. But, how are businesses actually using data in their daily operations?

In this article, you'll learn more about how businesses use data science, common benefits of data science in business, and encounter the skills you'll need to become a data scientist yourself. At the end, you'll also explore some cost-effective, online course to help you gain job-relevant skills today.

Impact of data science in business

Adding data science to your business practices can make a marked difference in productivity, decision-making, and product development. It can help you minimize or eradicate the risk of fraud and error, increase efficiency, and provide better customer service.

Data scientists can also help automate time-consuming functions in your business to leave more critical tasks to human hands and minds. Consider the following key benefits data science brings to companies.

Making better business decisions

Companies can use data and risk analysis practices to make informed business decisions. The collection and analysis of data collected within the company can assist higher-ups by providing objective evidence to direct difficult business choices. 

Measuring performance

Data science allows businesses to measure performance through data collection to make more educated decisions across the organization by using trends and empirical evidence to help them come up with solutions. 

Providing information to internal finances

Your company can also use data science to make predictions, generate financial reports, and analyze economic trends so you can make informed decisions on budget, finances, and expenses. This will allow for a fully optimized revenue generation with an accurate picture of what is going on with internal finances. 

Developing better products

Data analysis can use a data-driven approach to provide verifiable and evidence-based numbers that allow a company to reach its target audiences, find what its audiences enjoy, and then cater its products to that audience.

Increasing efficiency

With data collection in the workplace, a business can allow for testing and measuring different methods and feedback from workplace operations. Data can enable the company to grow and take on more load by increasing the efficiency of daily operations and work volume.

Collecting manufacturing data can allow companies to iron out inefficiencies and optimize production. A high amount of volume collected from manufacturing machines can provide critical data to increase production efficiency and maximize output. 

Mitigating risk and fraud

Data science can allow your business to increase security and protect information that may be sensitive. Detecting fraud based on typical behavior from a user can be done using machine learning algorithms. Generating large clumps of data from these instances can allow machine learning to capture these occurrences with high accuracy. 

By tracking workplace operations and keeping a log of workplace activities, the company can take note of any employees not complying with policy or any fraudulent practices. 

Predicting outcomes and trends

Using statistics and big data collection within the company, statisticians and data scientists can develop projections and predictions to allow executives to adjust operations accordingly based on these predictions. 

Collecting data and analytics can also give your company predictions on consumer feedback, market trends, and general trends in the public so you can tailor your practices to target a specific group or make adjustments based on what’s going on with competitors in the market. 

Improving customer experiences

Data collection on customers can be valuable in attracting a target market and tailoring the customer experience and need toward the data collected.  By demonstrating their likes and dislikes, the results can increase sales and allow companies to build a brand on which their customer base relies. 

Customer data can show their habits, characteristics, preferences, likes, and dislikes, among other meaningful data. A company can gather customer data in various ways. Still, data scientists, statisticians, and analysts need to digest it and present it in a way that is valuable to the organization. Understanding who the customers are is integral to placing your product in front of the right eyes and building a brand image. 

Should you apply data science in your organization?

Data science can add value to any business by using its data to develop solutions and optimize day-to-day operations. Data science can be used for statistics, hiring candidates, and making more informed business decisions based on a data-oriented approach. 


Data science skills for businesses

The internet offers no shortage of resources to get you started learning data science concepts such as Python programming, SQL, statistics, machine learning, and data modeling. From free videos and open source platforms to structured courses and degree programs, you can gain the data science skills you need to make a difference in your business or hire a professional.

Here are just a few of the data science skills applicable to business:

Computer/coding programs

Computer programming skills are highly valuable and integral to performing data analysis. Proficiency in one or more high-level programming languages can come in handy when analyzing large data clusters. 

Examples: SPSS, SAS, MATLAB, R, Python, Hadoop Platform, C/C++, Java, SQL/NoSQL databases

Technical skills in t-testing

Having a solid understanding of mathematical concepts used to dissect data and analyze trends is crucial to data science and being able to make projections and find meaningful information from large clusters of data.

Examples: Linear algebra, calculus, probability

Unstructured data techniques

Organizing data in a comprehensive way is another critical skill in bringing data science to your business. As a result, you should be comfortable working with both relational and non-relational databases, and know which is best suited to the task at hand.


A large part of analyzing data is performing statistics and creating models to show data trends to key stakeholders within a company. As a result, data scientists must be comfortable working with established statistical methods and techniques to ensure their findings are accurate.

Data visualization and reporting techniques

Presenting your findings and trends in an easy-to-digest way is essential when presenting your results to your team or higher-ups. Visualizing data trends and turning your findings into plain English can be a valuable asset for your team. 

Machine learning tools and techniques

Understanding machine learning software and how to use it will also be valuable to your team or company. You can use highly advanced techniques with automation and machine learning that may not be available to you using traditional analysis practices. 

Data cleaning and munging

Cleaning data and making it usable in an intuitive form is another process of data science. As a data scientist, you’ll often receive large clusters of data in an indecipherable form, and by using tools to construct the data in a more organized way carefully, you can then perform your analysis. 

Read more: How to Choose a Data Science Bootcamp (+ 5 to Consider)

Data science certifications for business

Data science certifications help you to demonstrate your mastery of critical job-relevant skills to potential employers. Earning a certification, consequently, can be a great way to hone skills that are valuable to both employers and the businesses they operate. If you're interested in pursuing greater professional knowledge of data science for business, here are some certifications you should consider:

  • Certified Analytics Professional (CAP): This certification allows you to learn big data but doesn’t focus on a specific platform. You must pass the Associate Certified Analytics Professional exam before pursuing this certification. 

  • Cloudera Certified Professional (CCP) Data Engineer: This certification requires passing the Cloudera Associate Data Analyst exam. Cloudera CCP Data Engineering certification is a rigorous and performance-based certification that requires mastery of deep data engineering. 

  • Dell Data Scientist Associate v2 (DCA-DS): This certification equips you with data science fundamentals so that you can participate immediately in projects. 

  • Google Professional Data Engineer Certification: This exam is designed to certify your knowledge of testing the Google Cloud Platform (GCP) and machine learning models and designing, operating, and security. 

  • Open Certified Data Scientist (Open CDS): Open CDS is completed through an application and a board of review, and this certification is earned by progressing through Certified Data Scientist, Master certified Data Scientist, and Distinguished Certified Data Scientist certifications. 

  • Microsoft Certified: Azure Data Scientist Associate: This program is for data scientists to use machine learning to create models that solve business problems. This exam covers AI solutions, predictive analytics, and natural language processing. 

  • SAS Certified Big Data Professional: Participants in this exam should have experience in programming and SAS. There are nine courses with this certification that cover statistics and data analysis. 

  • SAS Certified Data Scientist: This course combined with other SAS certifications, gives you additional distinction, and does not expire. 

  • DASCA Senior Data Scientist (SDS): This certification breaks into five tracks that require different levels of experience. You need a bachelor's degree and five years of experience to start the first track. Some of the other tracks may require a master's degree. 

Read more: 5 SQL Certifications for Your Data Career in 2022

Learn more about data science in business

Data and data professionals are incredibly valuable to businesses. If you're interested in pursuing a data science career and helping businesses make important decisions, then you might consider taking a cost-effective, online course through Coursera to gain job-relevant skills.

In CertNexus' Certified Data Science Practitioner Professional Certificate, you'll learn how to more effectively extract insights from data and leverage them to address business issues, thereby bringing greater value to your business.

In the University of Colorado Boulder's Introduction to Data Analytics for Business course, meanwhile, you'll explore the analytical process, how data is created, stored, and accessed, and how organizations work with data and create environments in which analytics can flourish.

Keep reading

Updated on
Written by:

Editorial Team

Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact...

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.