Main image of article Data Scientist Degree: Which Courses Should You Take?

Are you interested in getting a degree in data science? As a rapidly growing profession with a lot of earning potential, data science is attracting a lot of attention right now, and a degree can definitely help you stand out in a crowded hiring environment. Let’s explore what a data scientist does, and then what getting a degree involves.

On the most basic level, data scientists analyze enormous amounts of data for strategic insight. They often present those findings to executives, who use it to make critical decisions. On a day-to-day basis, data scientists work with raw data from multiple sources (much of it complex and messy), using (or even building) the tools to pull that data together. A lot of the job also involves creating reports and visualizations that a wide range of stakeholders can understand.

If you’re interested in data science as a career, it might help to explore some online courses that break down the profession, its challenges, and its tools. These courses include: 

Here are options that cost money (but you earn a certificate at the end):

However, many organizations also want their data scientists to possess a degree. Let’s break down that issue.

It’s Usually a Master’s Degree

Although there are a few schools that offer a bachelor’s degree in data science, far more schools have only a master’s degree program in data science. In theory, you would obtain your BS in computer science and mathematics before going for your Master’s in data science.

During your bachelor’s degree program in math or computer Science, you’ll need at least two semesters of calculus (with a bachelor’s in mathematics, you will take far more than just two semesters of calculus.) You’ll also need statistics and programming courses, as these are vital data science skills.

If you enroll in a bachelor’s degree in data science, you’ll take the above coursework alongside courses in data science. However, your time will be limited and you won’t take nearly as many data science courses as you would in a Master’s program. And that can put you at a disadvantage when applying for jobs.

The Courses You’ll Take for a Master’s in Data Science

Different graduate schools have different sets of required courses. Remember, this field is still very new and there’s some disagreement over what coursework will prepare a student for such work.

Here are some courses you might see in a program (not all programs will have all of these):

Mathematics for Data Scientists: Courses such as this will focus on linear algebra, which is a branch of mathematics that handles matrices (which play a big role in data science).

R programming and Python programming: If you didn’t study these languages in your bachelor’s degree program, you’ll need to take courses in them, as these are the two main languages used by data scientists.

SQL and Database architecture: This will likely be more than one course. You’ll learn how databases work, how they store their data, and how to manage them so the data can be read and written efficiently. You’ll learn how to use a language called SQL (usually pronounced “sequel,” and stands for Structured Query Language). Data is typically stored in tables, which are similar to spreadsheets in design, and SQL is ideal for looking up such data. Tables are related to each other and are therefore called “relational data.” You might also take a course on non-relational databases such as MongoDB. Non-relational databases don’t use SQL and are usually referred to as “NoSQL” (pronounced “no sequel”).

Data Modeling: Storing data in a database is one thing; understanding how to store it correctly is another. This is where a course in data modeling comes in, where you’ll learn how to “model” the data into different pieces that makes the data easy to save and retrieve. Additionally, you’ll see how to read such data into your software applications using various programming languages.

Big Data Architecture: This is where you’ll learn to use tools with names such as Hadoop and Spark to manage what’s come to be known as “big data”; that is, billions of records of data commonly found in huge corporations and tech companies such as Google and Amazon. Processing this type of data gets complicated, because you often have to spread the processing out among multiple computers. Tools such as Hadoop are ideal for this kind of work.

Data Visualization: You’ll likely have to take more than one course in this. You’ll use different tools for analyzing and ultimately presenting the data. Python is often present in this course, along with tools with names such as matplotlib, numpy, and pandas.

Data Mining: Here you’ll learn how to extract data from diverse sources. The data will typically not initially be in a usable format; your job will be to extract usable data from a much larger set of messy or chaotic data.

Cloud Computing: Big data needs to be processed by big networks of computers. Cloud providers such as Amazon Web Services include services that run Hadoop and other “big data” tools. These services also allocate multiple computers that come with the necessary software installed.

You’ll likely find some other courses that might be considered electives. For example:

Data inference: This course will teach you how to infer data from existing data, often with a focus on causal effects. There are rules in coming to such conclusions, and you’ll learn that in a course like this.

Deep Learning and Machine Learning: Considering the burgeoning popularity of machine and deep learning, you might want to take a few courses in this. Amazon was one of the first companies to really make use of deep and machine learning, as their website analyzes your searches and purchases to come up with suggestions for products you might want to buy.

Natural Language Processing: This is a big one lately with AI tools such as Chat GPT. While Chat GPT is often considered an artificial intelligence tool, its primary function was originally to process language in a form that humans might speak.

Conclusion

A degree in data science will certainly prepare you for a great career. Most people prefer to take the longer road and first get a bachelor’s degree in either Math or Computer Science, and then a master’s in data science, as these programs are more complete and add additional credentials to your resume.

Like any technology career, it’s a lot of work to get going, and you’ll need to plan on continually learning throughout your career. Data science will certainly evolve in the decades to come. But once you get into the field, you’ll continue learning new technologies as they come out. Perhaps you’ll create the next version of Chat GPT!

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