Being a leader in business requires excellent data interpretation to inform all necessary functions.
Gaining highly sought-after data analytics skills can help you position yourself for a wide range of careers in business and beyond.
What are data analytics skills?
Data analytics provides a framework to analyze data and translate it into actionable insights. These techniques are designed to address challenges and help businesses navigate issues across virtually all operation processes.
How can a degree in data analytics help you develop this expertise?
Creating actionable solutions begins with organizing information, gaining a clear understanding of business histories, and drawing on those findings when formulating plans for the future.
Four Categories of Data Analytics
Data analytics falls into these four main categories which are based on the specific issue one seeks to solve.
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Descriptive Analytics
Helps discover and describe what happened in the past through several data analytics skills and tools, including data mining and aggregation.
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Diagnostic Analytics
Partially rooted in probability and drawing on myriad data analytics skills and techniques, this helps professionals determine why an event happened.
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Predictive Analytics
Utilizing probability and various data analytics skills and analysis techniques (including predictive modeling), this helps professionals anticipate potential outcomes.
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Prescriptive Analytics
Helps organizations and businesses determine the next steps for any given circumstance by drawing on many analysis techniques and data analytics tools, such as simulation analysis, and may draw on artificial intelligence.
What Are Data Analytics Skills?
Successful professionals working with data must employ a wide range of techniques on the job.
Experts generally agree that the most common data analytics skills include:
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Communication and interpersonal skills
Professionals in any role who are dealing with data analytics must be able to properly convey their work to audiences of all knowledge and skill levels.
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Computer programming languages
Writing code, improving algorithms, and employing data visualization tools are necessary for professionals working with data. Well-developed computer skills include a solid background in computer programming languages such as Structured Query Language (SQL), Python, R, C and others.
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Critical thinking and problem-solving
Professionals who work with data must use critical thinking in order to understand the issues at hand and devise frameworks for effective solutions.
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Mathematics
Nearly all functions related to data analytics require mathematics, particularly:
- Statistics
- Probability
- Applied Statistics
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Research and analytical skills
Professionals must be skilled researchers who can properly investigate, examine, and interpret their findings.
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Technical skills
Data professionals need experience working with a variety of data analytics tools, such as data mining and predictive modeling, to properly carry out their tasks.
“As the volume of data collected by organizations continues to expand, there is a shortage of competent graduates who understand the needs of business and are well-versed in a wide array of data analysis tools.”
– Pang-Ning Tan, Ph.D., Associate Professor, Computer Science
Experiential Learning Courses
There are a myriad of undergraduate and graduate courses available at Michigan State University designed to teach the data leaders of the modern world.
Our M.S. in Business Data Science and Analytics program offers courses that explore applied statistics, data mining, contemporary tools, and more.
In addition to the courses listed below, you’ll also study:
- Business strategy
- Project management
- Marketing technologies
- Communications
Our programs are specially designed to round out your skill set and expand your career trajectory, no matter your aspirations.
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Business Analytics Problem Solving (Statistics)
Learn to apply statistical concepts including random variables, analysis of variance and others while developing an understanding of when to each should be applied.
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Computational Techniques for Large-Scale Data
Examine emerging issues in big data, such as:
- Collection and querying
- Mining
- Association analytics
- and more.
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Data Management and Visualization
Learn about the role of analytics in:
- shaping competitive strategy
- improving operational design
- implementing business processes
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Data Mining
Examine techniques and algorithms for knowledge discovery in databases, from data pre-processing and transformation to model validation and post-processing.
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Machine Learning and Optimization
Delve into these concepts and their applications:
- Supervised learning (such as penalized regression, classification and survival analysis)
- Deep neural networks for classification
- Advanced topics in machine learning (such as generative models and meta learning)
- and more.
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Web Analytics
Gain a better understanding of the collection and analysis of information from the web, including predicting future behavior, mobile marketing and analytics, and more.