Data Analyst Training Options | UDaily

Artwork by Cindy Dolan

Although data is easier than ever to collect and store, knowing what to do with it and how to analyze it is a challenge, especially for those without the proper training. From driving customer acquisition and retention, to managing risk and identifying the source of product performance issues, to predicting competitive bond bids, to building a list winning baseball players and countless other functions, skilled professionals possessing the unique combination of computational, analytical, and communication skills needed to uncover data-driven solutions to important business questions are invaluable to the success of any business. ‘an organization.

Tasked with examining large amounts of data to identify patterns, make projections, and use relevant information to guide business decisions, data analysts are among the most sought-after professionals in the world. With high demand and a limited supply of people to fill the job, the US Bureau of Labor Statistics predicts data analyst jobs will grow by 20% from 2018 to 2028, which is much faster than the average of all occupations.

Employed in many industries, including information technology, healthcare, finance, insurance, and professional services, a variety of educational paths can be taken to hone data analysis skills. Two of these routes are provided by the University of Delaware’s Division of Professional and Continuing Studies (UD PCS) through its Predictive Analytics and Data Mining Certificate and Foundations of R for Data Analysis Certificate programs, both of which are offered online this spring. Depending on a person’s interests and background, either course – or both – may be suitable.

Predict outputs based on inputs

Taught by two retired DuPont employees, Steven P. Bailey and Aaron J. Owens, the Predictive Analytics and Data Mining Certificate program teaches how to define project objectives, identify or collect appropriate data, analyze data to determine a solution , and effectively communicate the results to others. Although there are no formal prerequisites for predictive analytics and data mining, a college-level statistics course and/or working knowledge of statistics is required, and previous experience in data management computer aided is helpful.

“The more data you have, the more statistically significant information you’ll get that may not be of practical importance or good for predicting the future,” Bailey said. “If properly organized in a spreadsheet or spreadsheet, we can employ a number of techniques, all focused on building models that predict one or more outputs based on our inputs.”

While predictive analytics refers to the use of statistics and modeling techniques to make predictions about future outcomes and performance, data mining is a process used to transform raw data into useful information. Bailey, who worked with DuPont’s Applied Statistics group, manages the predictive analytics portion of the program before handing the class over to Owens, a former principal investigator in DuPont’s Decision Analytics group, for the exploration segment of data.

“One difference between typical predictive analytics, which is based on statistics and uses a full dataset, and the data mining segment is that in the latter, we use cross-validation all the time by dividing the data and using some to model and some to predict,” Owens said. Steve and I complement each other with his deep statistical knowledge backed by my experience in data mining.

Using the included statistical analysis program

JMP Pro is the primary analytics software used in the Predictive Analytics and Data Mining Certificate program. The commercial, menu-driven software package, which students can use for free for the remainder of the academic year in which they take the course, requires no programming skills and allows users to perform predictive modeling techniques and cross-validation and other actions. . Regardless of their industry, Bailey and Owens believe that anyone who deals with large amounts of data and wants to use historical data to predict future outcomes can benefit from their course.

“Our goal is for students to develop an appreciation for the tools we give them, understand what each of them can do, and, at least in JMP Pro, be able to perform on them,” Bailey said.

Perform data analysis with R

Rather than using a software tool such as JMP Pro, or in addition to it, some data analysts use programming languages ​​to perform their tasks. Designed to create a standard form of commands that can be interpreted into machine-understood code, programming languages ​​are regularly used in many industries, including research and academia, information technology, finance, e-commerce , social media, banking, healthcare, manufacturing. , and the government. While there are a variety of programming languages ​​to choose from, Foundations of R for Data Analysis Certificate Instructor Ryan Harrington’s vehicle of choice is R, a free and open-source statistical language that allows users to extract, clean, visualize and model data.

Previously a senior data scientist at CompassRed Data Labs and a high school math teacher, and currently Director of Strategy and Operations, Delaware Data Innovation Lab at Tech Impact where he develops data science projects, Harrington discussed the foundations of R for Data from the University of Delaware Certificate Program in Analytics last fall. The foundational level classroom welcomes everyone and no previous programming experience is required for the course designed to help students advance their technical skills, enable their statistical work, automate their analyses, produce visualizations of compelling data, to work with larger data sets, to create repeatable results. reports and break into the growing data analytics industry.

“R was developed for statisticians by statisticians and has become an extremely popular language, especially over the last 10 years or so,” Harrington said. “R was my first language for data analysis, and I fell in love with it because it simplifies difficult tasks and solves problems for me every day in my work.”

More than a programming course

By using R to support learning the basics of a budding data analyst or data scientist while focusing on programming mechanics with it and not statistical modeling techniques, Harington said that he teaches a data analysis course supported by computer programming rather than what he would call an actual programming course. Although programming is the means to the end, its goal is to train students to become competent data scientists or data analysts.

“I don’t just teach the language of R; I teach the mindset of how a data analyst would do their job,” Harrington said. “The Predictive Analytics and Data Mining course teaches the methods, and Foundations of R for Data Analysis teaches a programming language that can be used to run the methods.”

Register now for spring programs

The 15-week Predictive Analytics and Data Mining certificate program begins February 7 and the eight-week Foundations of R for Data Analysis program begins March 4. Both classes are held on Mondays, 6-9:15 p.m. Discounts are available and a payment plan is offered. Prospective students looking for advice on which course is best for them or any other information are encouraged to visit pcs.udel.edu/data and pcs.udel.edu/foundations-of-r, email at [email protected] or call 302-831-7600.

Sean N. Ayres