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data science major

Any college student who’s considering a career in tech is likely to be wondering whether data science is a hard major. In tech circles, data science is considered to be one of the hottest up-and-coming fields of specialization. Employers are in need of growing numbers of qualified data scientists. New data science degree programs, certifications, and MOOCs (massive open online courses) are becoming available at a rapidly accelerating rate.

How difficult are these classes? Is it really hard to succeed in the field of data science? Let’s dive in and take a look at the relative difficulty levels of the requirements for graduating with a degree in data science.

You may also like: 20 Best Data Science Bachelor’s Degree Programs

An Interdisciplinary Major

It’s typically necessary for data science students to take courses in multiple academic departments:

  • Computer science
  • Mathematics
  • Statistics
  • Machine learning
  • Business

Depending on how a student’s college is organized, this can sometimes make for a challenging and disjointed experience. It’s ideal to seek out a data science degree program where there is a close collaboration between the relevant departments, particularly the math, statistics, computer science, and machine learning departments. This can help to cut down on some of the inherent difficulties with a data science major.

Some students might enjoy the variety of course requirements and thrive in a major that requires such intensive learning. Others will find it too difficult to mentally “switch gears” between all these disciplines. It’s can also be problematic when students fail to grasp the intricate relationships between these disciplines; it’s necessary for the data science student to connect them all seamlessly.

Typical Course Requirements for a Data Science Major:

A data science major is typically comprised of some combination of the following types of courses:

  • Mathematics and Statistics Courses: calculus, multivariate calculus, analytic geometry, linear algebra, discrete mathematics, statistical methods, and probability
  • Computer Science and Machine Learning Courses: data structures, data analytics, data mining, algorithms, object-oriented programming, introduction to artificial intelligence, machine learning and database systems
  • Other Foundation Courses: Many data science degree programs require some other basic foundational courses like ethics, writing, and an introductory science course.
  • A Capstone Project: Many data science degree programs require students to complete a final project, known as a “capstone project”, under the supervision of an academic advisor.

Data literacy resulting from the completion of these types of courses could be helpful to an individual seeking a career in almost any industry, according to Wired.

Whether a student would find these data science major course requirements hard or not is largely a matter of individual opinion. The rigorousness of each class can also depend a great deal on the professors who are teaching.

Solving Problems Versus Writing Papers

A data science degree requires students to spend significant amounts of time troubleshooting code and solving problems. This is a major that doesn’t require the intensive volume of paper writing that many other major courses of study require. This could be an advantage for students who hate writing papers and find it difficult. Conversely, verbally gifted students who want to spend their academic lives writing papers could find data science to be a hard major.

Data science degrees also require more hands-on learning than some other majors. It’s not as simple as attending class and getting questions right. Data science students need to prove their knowledge and skills in actual business settings. Most data science schools have corporate partners for internship placements. Popular data science internship sites include Intel, LinkedIn, Cisco, McKinsey, Boeing, and Intuit. Internships put students into a data scientist’s shoes and real-life projects. Supervisors expect interns to already have hard skills like C++ and SQL. Students who lack tech prowess and hate math will struggle in data science internships.

Different Types of Data Science Degrees

How hard data science majors are usually depends on the degree level. Data science courses get increasingly difficult each year. Introductory 100-level data science courses could be a piece of cake. However, 300- and 400-level courses in the junior or senior year will be tough stuff. Graduate courses in data science are unsurprisingly the most challenging. The highest level of 900-level classes will require extensive technical abilities to mine data sets.

One way to check a data science program’s difficulty level is to review the graduation rate. The National Center for Education Statistics tracks graduation rates. Programs with high graduation rates over 90 percent are likely less trying. Low graduation rates suggest fewer students make it through because it’s harder. There’s a potential caveat though. Schools with already low acceptance rates only admit top-notch students ready to tackle the curriculum. Here’s what to expect at each data science degree level.

  • Bachelor of Science in Data Science – Earning a B.S. in Data Science isn’t an easy feat, but it’s the least specialized degree level. Bachelor’s degrees require 120-130 credits total. Of those credits, about one-third will be in data science subjects. Undergraduate schools have a general education curriculum that covers diverse basic courses from English to history. Non-technical courses are a huge slice of the B.S. in Data Science curriculum. Bachelor’s majors could even fill their electives with simple classes like PE. Ambitious B.S. in Data Science students can choose extremely hard electives, such as Financial Engineering. Bachelor’s degrees are as hard as students make them.
  • Master of Science in Data Science – Completing an M.S. in Data Science is inherently harder than the undergrad major. Master’s degrees are meant to push students forward with more technical, specialized courses. For unrelated majors like art or philosophy, the Master of Science will be an extreme change. For B.S. in Data Science graduates, the transition won’t be as dreadful. Data science majors already have prerequisite courses for a shorter study time too. M.S. in Data Science curricula generally entails 30 to 48 credits beyond the 400 level. Complex concentrations, such as Artificial Intelligence, Data Engineering, and Health Analytics, may be available. Whether online or on-campus, M.S. in Data Science courses lead to a thesis project. Taking on a graduate assistantship would add 10-20 hours more work weekly.
  • Doctor of Philosophy in Data Science – Finishing a Ph.D. in Data Science is a tremendous accomplishment. On a difficulty scale of 1-10, doctorates are an 11. The Doctor of Philosophy is an intensive, research-based program reserved for scholars in the upper echelon of the field. Most successful Ph.D. candidates have superior GPAs and Graduate Record Exam scores. After all, doctoral degrees usually only admit 5-10 people each year. Above-average understanding of statistics and coding is crucial to compete. Once admitted, candidates are paired 1:1 with doctoral advisors to get through complicated seminars. Doctoral curricula leads up to an in-depth dissertation project. Students need to complete original research, write a paper of 100-200 pages, and give a dissertation defense.

Is the Data Science Job Search Hard Too?

Strong STEM students could buzz right through a data science major. Next comes the job hunt. According to Money Magazine, the average job search lasts 43 days. The timeline is likely shorter for data science majors though. Demand for data professionals is through the roof. After-graduation job searches might be the easiest part of a data science major. The Big Data market is currently valued at $229.4 billion. IBM reported that U.S. data science jobs jumped to 2.72 million last decade. Certain specialties, such as artificial intelligence, have posted double-digital growth over 40 percent. Four of LinkedIn’s 10 fastest-growing careers are in data science or analytics. There are 6.5 times more data scientists in 2020 than in 2015.

Data science graduates are also handsomely paid for their tough tasks. The U.S. Department of Labor calculated median annual pay of $100,560 for data scientists. Information research scientists report a $122,840 average salary. Information assurance analysts receive a mean $98,900 wage. Database administrators have a median compensation of $96,110. Data analytics managers reap average profits of $108,000 yearly. Statisticians bring home $95,680 on average. Biostatisticians are rewarded with $91,800 mean income. Financial managers have median earnings of $129,890. Data engineers are granted an average paycheck of $102,864. Computer systems analysts make a mean $96,160 salary. Hard data science majors are worth the investment with six-figure income projections.

Handy Tools to Make Data Science Majors Easier

Data mining doesn’t have to be done by hand with a pen and paper anymore. Cutting-edge computer software is every data science major’s best friend. The software automatically takes on tedious statistical analysis tasks. Software is capable of extracting usable data and finding correlations or patterns between data points. Data science professors teach students how to correctly use this software in hands-on computer labs. Online data science boot camps can also demonstrate how different software is applied. Let’s look at a few tools that help undergrads and post-grads simplify data science.

  • DataRobot – Founded in 2012 in Boston, DataRobot was the globe’s first automated machine learning solution. This top-notch enterprise AI platform makes it effortless to create correct predictive data models. Owned by Paxata, DataRobot helps data scientists build nearly 2.5 million models daily. There’s even DataRobot University to learn the software features.
  • MATLAB – MathWorks has more than 4 million users from 100,000 organizations who use MATLAB to develop data algorithms. The interactive, scalable platform is popular on college campuses to skip the $99 student price. MATLAB features the Live Editor to automatically generate C/C++, CUDA, or HDL codes. It takes the programming work out of data analysis.
  • RapidMiner – Since 2006, RapidMiner has been a leading cross-platform option used by over 625,000 data scientists and 4,000 universities. Data science majors can let their colleges cover the $15,000 server cost and benefit from complete life-cycle products. RapidMiner prepares, analyzes, and visualizes data. It has 1,500 algorithms to generate the best models.

So now you’re updated on the typical course requirements for earning a degree in data science. How hard do they sound to you? You’re the only one who can decide whether data science would be a hard major to pursue.


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