5 Fascinating Data Science Courses You Can Look Forward To

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You’ve obviously got an appreciation for the power of data and the many ways it can transform a business—or even society! But as you dig into data-related graduate programs, it can be tricky to tell what exactly your time in school will cover. Are you going to be bogged down in interesting but hard-to-apply theory? Does the program actually teach you how to do the things you’re learning about? One way to get a sense of this is to take a closer look at the courses offered within a program.

We asked Rasmussen College Data Science instructor Don Wedding to highlight some of the courses you’ll find within the program and explain why they’ll matter in your career. Wedding says he’s passionate about providing data science students the practical skills they’re likely to use in future data science positions. That’s a big part of why this fully online program was developed in partnership with industry leaders, so students learn industry-standard tools and languages. “Our goal was to always be practical,” Wedding says.

5 Intriguing data science courses to expect

So what’s on the agenda for Master of Science in Data Science students at Rasmussen College? Let’s take a look.

1. Foundation of Data Science

What you’ll learn: This class will set the stage for the rest of your data science courses. Students will learn about data storage processes, statistical analysis techniques and the typical life cycle of a data science project. Along the way, students will get plenty of experience with the most prominent tools and languages including SAS®, R and Python®. This course will ensure you’ve got the fundamentals down and know how to best approach a project.

Why it matters: Simply put, this course provides students with a versatile toolkit for navigating data science projects. Wedding says throughout the Data Science program, students are asked to solve the same problem multiple times using different languages in order to develop their understanding of the benefits and drawbacks of each approach. According to Wedding, companies’ data science groups tend to stick to either SAS, R or Python. If students aren’t comfortable working with these languages, their employment options may be limited—so an emphasis on versatility is needed.

“That’s the goal here—get a job and be good at it,” Wedding says.

2. Data Visualization and Communication

What you’ll learn: Students in this course will learn how to best use data visualization tools like Tableau® and Google® Charts—interfaces that enable data professionals to share data from predictive models to machine learning techniques in simple and sophisticated ways.

Why it matters: Once a data analyst builds a model, develops a statistical or machine learning technique, they’ll likely have to communicate the meaning of it to an executive or manager in a way they can easily understand. Learning to visually display and explain data is an essential part of data science—the best analytical model in the world isn’t worth much if the decision-makers viewing it can’t make sense of what’s presented.

“Executives are not going to make a decision where millions of dollars move around if they don’t understand it,” Wedding says. “If they don’t understand it, they don’t trust it. That’s one of the reasons I think communication is key in this field.”

3. Advanced Statistical Techniques

What you’ll learn: Students will practice and deploy various advanced statistical techniques. This includes Monte Carlo simulations, which are used to model a variety of potential outcomes and visualize potential risk; Markov modeling, used for modeling potential outcomes from randomly changing systems; and Bayesian networking, used for determining probabilities based on a set of conditions, among others. Along the way, students will learn the strengths and weaknesses of each in order to justify their reasoning for choosing a certain technique over another.

Why it matters: Though these techniques are different from one another, they can all be applied to analytics. Wedding sees these techniques as tools akin to a screwdriver, hammer and monkey wrench—all necessary for building a house though they all perform different functions. These complex techniques enable data scientists to analyze data with gold-standard statistical techniques.

4. Text Mining

What you’ll learn: You’ll get to know text processing principles and learn what it takes to build a text mining system including speech-tagging, string processing, filtering techniques and how to use several lexical resources.

Why it matters: Text processing involves a type of machine learning using language theory. This process tags certain words and categorizes them in a way that suggests a certain course of action. So what does that look like in practice?

For a simplified example, imagine you’ve been tasked with finding ways to improve customer satisfaction at a data-savvy company that receives thousands of emails per day. Text mining techniques could be used to filter every email from customers who use words like “defective” or “broken” in combination with words categorized as having an angry sentiment. This batch of emails could then be routed to an expedited customer service queue in order to quickly address serious customer issues. And that’s just one practical application—text mining can also be used for powering automated chat bots that can help answer frequently asked questions and more.

5. Risk Assessment and Modeling Methods

What you’ll learn: This course is designed for students to learn the fundamental concepts of risk and exposure—with a focus on the techniques used in insurance, health management and finance to assess potential risk. Additionally this course touches on laws and regulations pertaining to data and how they can potentially affect data models.

Why it matters: Predictive analytics is incredibly valuable in risk-focused industries like finance and insurance. Knowing how to create legally sound data models for determining answers to key questions like “Who is most likely to default on this loan?” or “Which drivers are the most expensive to insure?” makes you a valuable asset. This course provides a practical opportunity to use data modeling and analysis skills in a scenario where they’re commonly applied.

Is a Master of Science in Data Science in the cards for you?

This is a complex field, but as you can see from these data science courses, that doesn’t mean your education will be focused strictly on learning theory from a 10,000-foot view. Each course of the Rasmussen College Master of Science in Data Science program was created with practical application in mind—students will complete projects with real-world applications, master the tools at your disposal and demonstrate to employers that you have what it takes to hit the ground running as a member of their data science team.

Ready to get started? Visit the Rasmussen College Master of Science in Data Science program page to learn more.

Python is a registered trademark of The Python Software Foundation.
SAS is a registered trademark of SAS Institute, Inc.
Tableau is a registered trademark of Tableau Software.
Google is a registered trademark of Google, LLC.

Kirsten Slyter

Kirsten is a Content Writer at Collegis Education where she enjoys researching and writing on behalf of Rasmussen College. She understands the difference that education can make and hopes to inspire readers at every stage of their education journey.


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