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Do I Have to Be Good at Math to Study Data Science?

math and data science

A common concern among aspiring data scientists is if they have to be good at math to study data science. Data science is one of the top careers in America today, so candidates interested in this career are scrambling about making sure they’re adequately qualified. Although data science is all about big data, does that necessarily mean it’s all about numbers and math? Here is some valuable information on data science and its dependence on math.

When considering a career in data science, one of the most important tasks is taking a look at the average college curriculum for a data science program. Most college curriculums are similar to one another but may have subtle differences related to options like specializations and electives. One program might focus on graduating students who are ready for further study in academia, and another program might prepare students for the workplace.

One thing most programs and data science careers will have in common is that they’ll feature some level of work on mathematical topics.

What is Data Science?

Data science is an interdisciplinary field that combines computer science, statistics, and mathematics with data at the core of the discipline. Data science works with data of all kinds and sizes but mostly deals with big data. Statisticians and mathematicians analyze data to create algorithms and solve problems whereas data scientists do so much more. Data scientists not only sift through data but determine where the data originated and what can be done with it. The data use is either structured or unstructured.

Structured data involves words and numbers. An example of structured might be a company’s balance sheet or expense report. Unstructured data involves more complicated things that typically cannot be sorted numerically. An example of unstructured data might be customer reviews, social media posts or similar things that need to be sorted and analyzed in different ways. We may not realize this, but data science is used in almost all areas of our lives and in every industry.

How Important is Math in Data Science?

To be truly successful in data science, an individual should have some background in math or at least be proficient in math because data science tends to rely on numbers. However, a person interested in data science doesn’t have to be a mathematician or doesn’t have to love math because there are some areas of data science that aren’t based solely on mathematics.

Big data is usually defined as something that cannot be solved or analyzed using traditional methods, but that doesn’t necessarily mean by using math. Machine learning is the perfect example. It uses statistical techniques to help computers learn things but doesn’t rely heavily on math. Data science may be analytical, but it’s more statistical than mathematical.

Career Opportunities in Data Science

math data science careers

The field of data science offers many career opportunities. Some of these careers require a good background in math while others rely more on computer science, computer programming or data analyzing. Although data science is still a relatively new field, U.S. News & World Report states that data scientists can work in a variety of settings, including government agencies, tech startups, research institutes and various companies. It also offers employees the flexibility to work with people from various backgrounds. Some jobs available in data science include:

• Data scientist

• Data analyst

• Data engineer

• Business analyst

• Machine learning engineer

• Engineering manager

Career Outlook in Data Science

Data scientists are very much in demand mostly because there are not near enough of these professionals needed to fill the many data science jobs. The U.S. Bureau of Labor Statistics projected a job growth of 19 percent for computer and information research scientists from 2016-2026. As of May 2017, these professionals earned a median wage of $114,520.

Do Data Scientists Need Extra Math in High School?

data scientist jobs with math
Earning extra credits in math in high school isn’t just a good idea for future data scientists. Pursuing extra math classes while in high school is an excellent way to increase the likelihood of getting accepted to a great college, particularly if the student is interested in a science, math, or technology major. Many high schools only require three years of math, but a fourth year of math can prove very helpful.

Not only can additional math classes provide an excellent foundation for future college classes, but they can also help the student make an important decision on a major or future career. Mathematics is a subject that has many applications outside one’s major, which means it can prove exceptionally valuable in the skills it offers in day-to-day life.

Important yet routine tasks one might need to accomplish each day that require some math knowledge include creating a budget for the home, figuring out whether a salary offer is sufficient, and completing physical tasks like car and home maintenance. Further, math can help improve problem-solving ability and critical thinking skills.

Future data scientists will definitely benefit from taking extra math classes in high school, but the decision is one that can lead to benefits in many other areas of one’s life. If a student’s high school requires three years of mathematics, it may benefit the student to take an additional year of an advanced math topic like trigonometry or statistics.

What Math Do Future Data Scientists Study in College?

Math in data science programs will often appear as part of the curriculum in every year of a student’s degree program. In a liberal arts program, students will take classes in a wide variety of subjects that range from arts to science to math and will also take several classes that are directly related to their data science major.

One of the most common basic math classes that a student will encounter in a data science program is statistics, which may be paired with lessons on probability and machine learning. Many high schools offer statistics, and it’s a great class to take in high school so that the college-level version of the class is a little familiar once the student has entered an institution of higher learning.

Data science program curriculums will often also include additional lessons on math, but the classes required are often an overview or brief exploration of other mathematical concepts rather than deep dives into those types of mathematics. Common math topics that may be examined include calculus, linear algebra, and probability.

For many students, thoughts of calculus will bring back nightmares of terribly difficult high school classes and sleepless nights spent studying for exceptionally hard tests. However, the average data science program doesn’t require Calculus I, II, and III. It simply requires a basic understanding of the main concepts in calculus, as well as linear algebra.

Is It Possible to Become Good at Math for Data Science Careers?

According to the National Center for Education Statistics, more than 80 percent of 4th-grade students in the United States perform at or above basic mathematics achievement levels, but that percentage drops to 62 percent by the time those students are in 12th grade. Even fewer students are “proficient” or “advanced” in mathematics in high school, which means many students enter college with a lot of studying to do in the realm of mathematics.

However, these statistics don’t necessarily mean that underachieving students are doomed when it comes to getting good grades in math. There are many options that students have for improving their math skills. For students who are serious about becoming data scientists but are concerned that they don’t or won’t have the aptitude in statistics to succeed, enrolling in remedial math courses may help.

Further options include taking additional math classes online, getting a math tutor, and dedicating oneself to studying hard and often for math tests in school. Some students are blessed with an innate aptitude for math and don’t need to study that hard for tests. However, many students need to put in extra hours studying to get the best grades and to really learn the material. For many, becoming proficient in math is simply a matter of investing the right amount of time in mathematical study.

Types of Math Used by Data Scientists

statistics in data science
The two most important types of math studied by future data scientists in their degree programs are statistics and probability. Some professionals consider data science an advanced and technological form of statistics. Statistics and advanced statistics are very often some of the most important classes one might take in a data science program.

Statistics is the collection of data, the analysis of that collected information, and the interpretation of the data. Advanced statisticians must understand each of these concepts, as well as how to effectively communicate the results of their data. Some of the concepts learned in statistics classes include linear regression and correlation, data sets, confidence intervals, and probability.

Data Science as an Interdisciplinary Field

Data science is considered an interdisciplinary field that isn’t solely focused on just one aspect of mathematical learning. In a data science program, students won’t just take math classes. They’ll also learn how to take their data science knowledge and prepare their data for presentation in many different environments. The nature of data science is such that students should be familiar with far more topics than just mathematics.

Some of the domains and fields where data science is an important topic include business communications, computer science, machine learning, and human-computer interaction. Data science students should not only be prepared to learn about mathematics in data science. They should also expect to learn about data science as it impacts fields that may house the jobs that a future data scientist might hold.

At one time, the concept of data science was indistinguishable from the field of statistics. However, changes in technology in the late 1990s and early 2000s led to an expansion of data science from a theoretical field within statistics to an independent field that dealt with the emergence of the digital world and advanced technologies.

The Importance of Analytical Skills in Data Science

While one of the primary skills any student needs is math in data science careers, it’s also extremely important to gain analytic skills like critical thinking, attention to detail, problem-solving, and communication. The responsibility of a data scientist isn’t to solve complex math problems but rather to use mathematics to present information in a valuable way.

Analytical skills are just as important as math in data science when one works in an environment like a computer systems design firm, a technical consulting company, an insurance carrier, or another company in the information services sector. As an interdisciplinary field, data science is an area that is constantly growing, and distinguishing one’s self in the growing field means mastering mathematical concepts like statistics, as well as all the other areas of knowledge that are important for data scientists.

Alternatives for Students Who Are Not Great at Math

Mathematics is a required part of any data science program, and it’s a concept with which students should be familiar before they enter college or university. Students don’t need to have advanced mathematical knowledge to enter a data science program at the undergraduate level, but it’s helpful to have a firm grasp of all mathematical topics presented in the average high school math class.

An option for those who want to become data scientists but who aren’t stellar mathematics students is the route of a data science professional rather than a data science academic. For data scientists who work in college environments as researchers and professors, mathematics is usually the most important part of the profession.

However, practicing data science within a corporate environment or outside of higher education environments often requires less day-to-day focus on mathematics. There are also opportunities in related fields where the focus might not be on mathematics or statistics at all times. Rather than working as a data scientist, students who are interested in the field of data science might consider the role of a data analyst, which is more closely related to computer programming than theoretical statistics.

Conclusion

For the future data scientist who wants to become a success in his or her chosen career, the journey begins with a well-rounded college program that helps the student master all of the mathematical concepts he or she needs, as well as the technical knowledge required of modern data science professionals. Taking a math class or two during the summer before college can help students who are unsure of their math skills master all of the math in data science they’ll need during college.

One of the great things about data science is that it offers a multitude of career opportunities in areas that are not totally math-related, such as data engineer, data analyst, and machine learning engineer. While it is quite beneficial for an individual to be good at math to study data science, aspiring data scientists can find excellent career opportunities within the field without being a mathematician.

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