Is Statistics Hard for Data Science Students? Tips for Success

Statistics is often cited as one of the most challenging subjects by students in many disciplines, including mathematics. In one study, 70% of students undertaking postgraduate research found it difficult to understand statistical information in research papers. Is statistics also hard for data science students, or do they excel in this subject with little effort?

This article discusses whether statistics is challenging for data science students. Additionally, we explain why this is the case and the approaches you can take to more easily acquire and understand the statistical knowledge you need as a data scientist.

Unfortunately, there are no statistics on the percentage of data science students who consider statistics hard. However, there is a lot of anecdotal evidence that this is a challenging subject for many students, including those taking STEM courses like engineering and mathematics. It’s, therefore, a safe bet that a good number of data science students also find statistics hard.

However, don’t let the fact that statistics is challenging dissuade you from pursuing a career in data science. Instead, consider different perspectives on the issue, such as:

• You’re Not Alone: Many other data science students face this same difficulty when learning statistical concepts, and many eventually succeed. Just because statistics is challenging in the beginning doesn’t mean you won’t eventually figure it out.
• Statistics Is Not Monolithic: Statistics is fairly broad, and while some specific concepts are harder to understand, others are relatively simple.
• There Are New Approaches to Learning: The challenge of learning statistics is not new. Experts have spent many years developing alternative teaching approaches to improve learning. We explore this further later in the article.
• It May Be Easier for You: Just because others find statistics hard doesn’t mean you will, too. Despite its mathematical foundations, the mindset needed to solve statistical problems is different, and some students are more comfortable with this shift than others.

Statistics is a field of study that deals with collecting, organizing, analyzing, interpreting, and presenting data. It’s a branch of applied mathematics, and it’s often used to understand the characteristics of and relationships in data before making estimations regarding populations using probability theory.

Many statistical concepts are used in data science, including descriptive statistics, probability, sampling, hypothesis testing, Bayesian statistics, regression analysis, correlation, bias, margins of error, etc. As a student, you’ll need to learn when to apply these concepts and their formulas.

In statistics, different situations require using a specific version of a formula. For example, the formula used to calculate the standard deviation of a sample is different from that used to calculate the standard deviation of a population. This is one of many factors that can make learning statistics difficult for some students. Other factors include:

• Perception: Studies have shown that students approach statistics with a negative attitude, sometimes stemming from negative learning experiences in high school. This leads to anxiety, which makes it harder for them to learn and get better at statistics.
• Terminology: There are many technical terms used in statistics, which may make it seem more intimidating. Additionally, some of the terms used in statistics are words that sound familiar but have different meanings, making them easy to misinterpret.
• Formulas: Data science students need to be familiar with many statistical concepts and their associated formulas. Many find these formulas hard to understand, and others rely on memorization to keep track of them without understanding the underlying concept or logic. This may help with getting good grades but not with long-term learning.
• Not Just Math: While some students struggle with the mathematical side of statistics, others struggle with the parts requiring logical reasoning. Statistics is also many students’ first exposure to the concepts of randomness and acausal relationships. These ideas can be difficult to accept since they deviate from the causal relationships common in subjects such as physics and standard mathematics.
• Tutoring Methodology: There are different approaches to teaching statistics, and many students have likely only been exposed to one. If an approach doesn’t resonate with a student, it’s likely to leave them with the impression that statistics is hard.
• No Specific Answers: Abstraction is a useful tool in statistics. A large population can be represented by a small sample, and a complex activity or entity can be represented using only a few features. This helps with calculations, but it also means exact solutions are rare in statistics, hence the use of concepts such as probability and margins of error.
• Application: The field of statistics exists to explain other phenomena. It’s supposed to help you understand events and their relationships. When it’s not taught in the context of real-world events and applications, it can be tough to understand and appreciate.

Whether you consider statistics challenging or easy, it’s a fact of life in data science. It’s a foundational subject in the field alongside mathematics, computer science, and machine learning, and understanding it is critical to correctly applying data science principles.

Luckily, whether this is a subject you’ve struggled with or an entirely new field of study for you, there are ways to make statistical concepts easier to understand.

Practice, Practice, and More Practice

A common expression amongst statisticians is that statistics is not a spectator sport. This means the way to get better at statistics is by practicing solving statistical problems. Practicing will help you become familiar with different scenarios, the concepts relevant to each scenario, and the formulas you’ll need. Regularly applying formulas also makes them easier to remember.

Additionally, you should not wait until you understand concepts fully before practicing. Whether you get things right or wrong, you’ll gain a deeper understanding as you learn by doing. Check out this list of statistics and A/B testing problems on Interview Query.

Try a Different Tutor

If you’re struggling to understand your current statistics lecturer, you may respond better to someone with a different teaching style. Try working with a different tutor either in person (e.g., a classmate) or online. Plenty of YouTube videos on basic and advanced statistics and online courses feature tutors with varying teaching styles.

Use Alternative Resources

There are books, videos, infographics, and other resources you can use to learn statistics today. Even if your lecturer is easy to understand, it’s important to learn how to use other resources because you won’t cover every important statistical concept in class. This will also expose you to more scenarios and statistical problems.

Interview Query offers a Statistics and A/B Testing learning path and a Probability learning path. These can be used to refresh your knowledge of key concepts in statistics and give you access to practice questions relevant to data science.

Understand the Base Mathematical Principles

Statistics relies on mathematical principles such as linear algebra, calculus, and sigma notation. Understanding these foundational principles will help you solve different problems. For example, knowledge of calculus is needed when working with probability distributions.

Start With the Easy

Not every statistical concept is hard. Descriptive statistics, measures of central tendency, and measures of variability, for example, are relatively easy to understand. By first learning these, you can build a stronger foundation for understanding more challenging concepts. This can also give you confidence, which helps you tackle more complex problems.

Understand Real-World Applications

Practicing statistics using relevant real-world examples can also improve your learning. Statistics can be applied in all fields, from engineering and physics to biology and geography. Learning it in a context relevant to your domain can make it easier to understand. At the very least, it will help you appreciate the role of statistics in your domain.

Keep an Open Mind

Finally, it’s vital to keep an open mind and consider that despite the challenge of learning certain concepts, you may still be able to understand them if you are motivated and have access to the right tools and learning resources.

Statistics is a critical part of data science, but some concepts have been known to challenge even students with sound mathematical reasoning. Many factors can make statistics harder to learn, including teaching styles that don’t resonate with you and having a negative perception of the subject. However, there are ways to improve your learning process, such as finding a tutor with a different teaching style and using a more comprehensive range of learning resources. It also helps to keep an open mind and not simply buy into the idea that statistics is hard before trying it out.

In addition to our learning paths, Interview Query also provides access to interview questions on statistics and probability. You can also check out our company interview guides to find out which statistics questions you may be asked when interviewing for data science positions at different companies. We also offer access to other resources you can use when interviewing for data science roles, including mock interviews, an AI interviewer, and coaching services. You can also read related articles on our blog, such as this one on whether you should get a PhD in statistics.

Whether you’ve struggled with statistics in the past or think it may be a barrier to a data science career, we hope this article will encourage you to give it a try.