AIQ EP1: Is math a barrier to data science?
Moderator: How important is math in learning data science?
Guest: Hello everyone. The core of machine learning, which is essential for data science, is based on mathematics. So, mathematics is indeed very important. However, there are many online resources, both free and paid, that can help you learn these concepts. It’s not rocket science, and if you’re a beginner or intermediate, you don’t need advanced mathematics. It’s important, but it’s achievable.
Host: What if someone is unfamiliar with basic math concepts like probability and statistics? How can someone get started learning the math required for data science?
Guest: I recommend starting with books. There are many good books on probability and statistics that can help you. You don’t need to understand everything in one read. The key is to get the basics without diving too deep at first. There are also many blogs, articles, and YouTube tutorials. When learning data science algorithms, learn the math involved as you go. For example, if you are learning about logistic regression, learn the math behind it on the fly. Don’t try to learn all the math up front. Learn it step by step.
Moderator: That seems manageable. But what about people who have a hard time understanding the algorithms and their calculations? How can we make this process easier?
Guest: There are many illustrations and tutorials available that make these concepts easy to understand. Once you learn an algorithm like logistic regression, you only need to learn it once as the math behind it remains the same. There are 10-12 core algorithms in the industry that solve most problems. Learning the math of these core algorithms is enough.
Moderator: What are some key math topics that math enthusiasts should tackle first to gain a better understanding?
Guest: Basic math like addition, subtraction, multiplication, and division is a must. Linear algebra, basic differential and integral calculus, probability, and statistics are also important. Most tasks do not require advanced concepts. Beginners should focus on linear algebra, probability, and statistics, which are similar to high school and early college level topics.
Moderator: Are there any roles for data that don’t require a lot of math?
Guest: Yes, roles like Data Analyst or MLOps may require less math knowledge compared to core data science roles, however, I can’t comment in detail on those roles as my experience is mainly in data science.
Moderator: How much math knowledge does a data scientist working at a startup need on a day-to-day basis?
Guest: In reality, implementing algorithms is only a small part of your job. You spend a lot of your time meeting with stakeholders, creating dashboards, presenting results, and solving business problems. Python and R have built-in functions to handle the math for you. You don’t need to know the math in detail, just have an intuitive understanding of how the algorithm works.
Moderator: So an intuitive understanding of mathematics is enough for your day-to-day work?
Guest: Yes, of course. Even if you don’t know the math, it’s very helpful to have an intuitive understanding of how it works. Typically, stakeholders are not interested in the equations, but in the results and how they solve their business problems.
Moderator: In business meetings, do stakeholders ever ask for detailed mathematical explanations of algorithms?
Guest: This is a misconception. You never present equations in business meetings. There is a field called Explainable AI, where you explain why a model produces a certain result and which variables are important. Both business and technical stakeholders are more interested in the business perspective than mathematical equations. No one has ever asked you to write an equation in an interview. It is more important to understand how to solve the problem from a business perspective.
Host: Let’s dive deeper into some core math topics that math enthusiasts should start learning to strengthen their data science foundations.
Guest: Focus on probability, statistics, and linear algebra. These are foundational areas. There are a lot of resources available and studying them will give you a strong foundation for understanding data science algorithms.
WALLACE: What happens to someone who studied biology in high school and didn’t put much emphasis on math?
Guest: You should never stop learning. Many people in this field come from a variety of backgrounds, such as geology or chemical engineering. The subjects you studied at school or university don’t matter much. What matters is your willingness to learn and adapt.
Moderator: Thank you for your valuable feedback. It’s very helpful for anyone considering entering the data science field.
Guest: You’re welcome. Math is important, but it’s not insurmountable. With the right resources and approach, anyone can master math.
Moderator: That’s it for today’s episode. I hope this discussion has helped clarify the importance of mathematics in data science. In the next episode, we will cover more interesting topics.
