COVID-19 Updates

Read the latest regarding our Fall Term opening.

Data Science Careers in Action: Putting a Master’s to Work

UNLIMITED DATA | BY JAMES KULICH | 5 MIN READ

Data science careers in action: Elmhurst University program graduates are getting hired, applying their knowledge and advancing in their careers, as represented by this futuristic illustration.

When I recently began canvassing our graduates from Elmhurst University’s Masters in Data Science program, I expected to see good results.

What I found went well beyond what I imagined.

I fully expected that most of our alumni are making use of analytics in their work, as they are. The range and depth to which they are doing so amazed me.

What Our Data Science Graduates are Doing

Elmhurst’s MDS graduates are employed by Fortune 500 companies, by innovative startups, and by many small- to mid-size organizations. Sectors represented include health care, insurance, financial services, retail, manufacturing, logistics, media, agriculture, education, human resources, government, sports, consulting and more.

Some job titles, such as senior analyst, data scientist, and senior manager of enterprise data and analytics, directly reflect responsibilities in the data science realm.

Others, such as principal–strategic planning and analytics, supply chain advanced analytics and process improvement manager, and collateral risk innovation manager, reflect the depth to which data science is becoming strategically embedded in the fabric of organizations.

Getting Hired and Advancing

Our alumni report some common themes in the paths to their current positions. One step involves demonstrating technical skills to put data science in action. Graduates describe being asked to prepare a dataset for further analysis, using tools like Python to handle issues such as missing data and outliers. The emphasis is on getting the basics right.

This can be a difficult hurdle to clear, but once this challenge is met, the focus turns to a candidate’s ability to translate technical work into organizational value. Managers evaluate a candidate’s ability to work well in larger teams, to interface with other parts of the organization, to explain why a technical result solves the problem at hand, and to paint a pathway for achieving the promise the output of a model might offer.

I was gratified to hear repeatedly from alumni that our holistic approach gave them distinct advantages in the hiring process. Their ability to handle both technical and organizational perspectives was key to being chosen, especially for more senior positions. Graduates cited our inclusion of project management concepts in the curriculum as being especially important.

Even in this very challenging business environment, I have received several notes in recent weeks from program alumni who have landed good new jobs or who have moved into new roles.

What’s Next?

The experiences of our alumni support the general sense that opportunities remain strong in the world of data science. But there are some caveats.

First, addressing basic short-term business needs is more important than ever. Most businesses are looking to contain costs. One graduate with whom I spoke described how his work on AI projects has been temporarily diverted toward more basic efforts to automate routine organizational tasks. This is an area known as Robotic Process Automation, or RPA.

Another basic need at this time is effective forecasting of key business metrics. Standard forecasts simply don’t work in the face of the pandemic. Organizations need new ways to get a sense of what to expect, both now and when the pandemic recedes.

Many organizations have less room than usual to experiment. They need tangible results. One graduate reports that his consulting duties have been refocused on organizational data governance, as these governance issues are perceived to be a primary roadblock to effective implementation of AI projects.

As these illustrations show, keeping the focus on producing organizational value remains essential. While this is not new, there is a now a sense of urgency.

Plenty of room remains for advanced work, such as natural language processing. Indeed, several of our alumni are active in fields such as this. But, applying data science techniques to everyday problems is the core work to be done. The path to value must now be clearer than ever.

A Larger View

The potential for data science to address some of the larger issues on our collective tables continues to grow.

One of our graduates focused her work on equitable models for public school funding. A current student is beginning work on modeling the connections between budgets and the impact of investments in government programs.

I am beginning a project with a local school district that is achieving strong results with students from lower-income families to determine the drivers behind their success and the extent to which some of their approaches could be used by others.

And, one of our earliest graduates just took a new position in the area of People Analytics with the title Diversity, Equity, and Inclusion Data Strategist.

I don’t have much more to report now on these early efforts, but I look forward to telling you more about them in future posts. I’m incredibly proud of what our Elmhurst students and alumni have achieved.

I welcome your thoughts as we look to prepare even more people to make a difference via effective use of data.

Request More Information Today

Want to know more about Elmhurst’s M.S. in Data Science program and how it helps professionals excel in business? Complete the form below.

Fill out my online form.
About the Author

Jim KulichJim Kulich is a professor in the Department of Computer Science and Information Systems at Elmhurst University. Jim directs Elmhurst’s master’s program in data science and teaches courses to graduate students who come to the program from a wide range of professional backgrounds.

Illustration by Tanner Wayment
Posted Sept. 1, 2020

Can machines think? The answer might surprise you, given the great strides that have been made in machine learning.

Can Machines Really Think?

November 5, 2019 | 5 Minute Read

18th century statistician and philosopher Thomas Bayes is still having an amazing impact on the field of data science.

The Amazing Thomas Bayes

September 24, 2019 | 4 Minute Read

What is the value of data? It is a hard question to answer, but there is undoubtedly some worth in your information.

What is the True Value of Data?

May 7, 2019 | 4 Minute Read

An illustration showing data scientists applying the human side of data to their work.

The Human Side of Data

April 4, 2019 | 4 Minute Read

Leave a Reply

Your email address will not be published. Required fields are marked *

Connect with #elmhurstu