Image by Author | Canva
Whenever we hear about data science, the first thing that comes to mind is that it’s something related to data and mathematics. Beginners in the field often think they need to be very skilled in statistics and mathematics to be good data scientists. But the point is that both mathematics and statistics are very broad fields, and becoming skilled in either of them could easily require a lifetime.
So, if you want to pursue a career in data science, how would you go about learning math and statistics? I’ll provide a learning path, and a comprehensive course to build your foundation to get started in Data Science. But before that, I will clear up some common myths about mathematics for data science, one by one.
5 Common Myths About Mathematics in Data Science
Myth 1: Without a Mathematics-related degree, You Can’t Succeed
Many students and aspiring data scientists think they need to have a mathematical background or a related degree to pursue data science. But the truth is that data science is applied to a wide range of tasks from different fields, and data scientists come from diverse backgrounds, often with minimal focus or background in mathematics. The key to their success is learning what they need, specific to the projects they might be working on, rather than mastering all areas of mathematics. So, this is what you should know if you thought the same:
What You Need:
- Applied Focus: Concentrate on learning mathematics that directly applies to your work in data science. For example, focus on the mathematics behind the algorithms you are using rather than on unrelated advanced topics.
- Continuous Learning: As you progress, you can deepen your mathematical knowledge as needed. Start with the basics and build up your understanding as you encounter more complex challenges.
Myth 2: Advanced Mathematics is required even for Entry Level Data Science Positions
When I first started doing data science projects, I always thought that I couldn’t be a good data scientist unless I knew advanced mathematical concepts deeply and completely. Many people, like me, believed that they needed to be very strong in advanced mathematical topics to excel in data science projects. However, it turned out that this wasn’t really the case. Like in any other field, a data scientist must engage in continuous learning. Nevertheless, the entry requirements to start working on data science projects are not very high.
You might be wondering what exactly you need to understand as a beginner-level data scientist. Here are three main points:
- Statistics: Understanding descriptive statistics (mean, median, mode, variance), probability distributions, and hypothesis testing is important. These concepts are the backbone of data analysis and help in making informed decisions from data.
- Linear Algebra: Familiarity with vectors, matrices, and matrix operations is important, particularly for working with datasets and understanding how algorithms like Principal Component Analysis (PCA) work.
- Calculus: A basic understanding of derivatives and integrals can be helpful, especially when dealing with optimization problems in machine learning models, such as gradient descent.
That’s all you need initially. You don’t have to master every aspect of statistics, calculus, and linear algebra all at once before diving into data science. Start with these basic concepts and build your knowledge base over time as you work on more projects and encounter advanced techniques. You can continue learning and applying concepts on the fly.
Myth 3: You must understand Calculus and Differential Equations
A lot of students think that to understand the math behind different statistical and machine learning algorithms, they need to have a very strong understanding of differential calculus. They often spend most of their time focusing on theoretical aspects rather than diving into practical projects. But what do you really need to know about calculus? Here’s what you need:
What You Need:
- Optimization Basics: Learn the basics of gradient descent, which is often used to minimize error in models. You don’t need to solve complex differential equations, but understanding how gradients work is beneficial. Once you grasp the basic idea of gradient descent and its conceptual significance, that will be sufficient for developing your intuition for various aspects of your data science projects.
- Derivatives: Knowing how derivatives relate to slopes and rates of change can help you understand how models are trained, particularly in deep learning.
Myth 4: You Need to Understand Every Detail of Every Algorithm
A lot of data science projects involve applying various statistical tests, algorithms, and machine learning models to different datasets. Additionally, there are numerous algorithms and techniques for selecting the best feature engineering methods needed for these models and tests. With so many options available, the sheer number of them can be overwhelming for beginners, and even their names can seem daunting. The idea of mastering all of these methods can make the field of data science appear very difficult to enter, even before starting.
However, the reality is that data science is more of an applied field than a theoretical one. If you understand the logic behind using different algorithms and techniques, and why one technique might be preferred over another, that is often sufficient for you as a data scientist. This practical understanding is really all you need to get started on data science projects.
What You Need:
- Conceptual Understanding: Focus on understanding the intuition behind algorithms. For example, grasp how a decision tree splits data or how a neural network learns patterns.
- Use of Libraries: Learn how to use tools like Scipy, Scikit-learn, TensorFlow, or PyTorch, which provide well-tested implementations of algorithms. These libraries allow you to apply complex algorithms without needing to understand every underlying mathematical equation.
Myth 5: Data Science is All About Fancy Statistical Tests and Complex Equations
While some people think that to understand data science they need to have a strong mathematical background, most of them believe this because they think that data science is solely about solving complex mathematical equations. This is not at all the case. Data scientists use well-researched techniques and methods for their projects, and they do not need to understand the underlying mathematical details of the equations related to those concepts. All they really need is an understanding of which methods should be applied to which types of problems. The underlying details of methods or algorithms are not required at all.
While mathematics is undeniably crucial, data science includes a broader spectrum of skills, such as data preprocessing, data cleaning, domain expertise, and effective communication of results.
Learning Path for Mathematics in Data Science
Recommended Course
The Mathematics for Machine Learning and Data Science Specialization is one of the most credible courses, offered by DeepLearning.ai on Coursera. Here, the instructors cover the basics of linear algebra, calculus and probability in three modules of the course. This is really what’s required at the start for data scientists to get started on their initial projects.
The resources mentioned above, along with this course, are actually what you may really need to get started as a data scientist.
Takeaway
The myth that you need to be a mathematics expert to succeed in data science is just that—a myth. While some mathematical knowledge is definitely important to get started, it’s more important to focus on practical, applicable math rather than trying to master everything at once.
Don’t be intimidated by mathematics in data science. Start with the basics, focus on practical application, and remember that many successful data scientists have built their math skills over time, learning what they need as they progress.
Kanwal Mehreen Kanwal is a machine learning engineer and a technical writer with a profound passion for data science and the intersection of AI with medicine. She co-authored the ebook “Maximizing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, she champions diversity and academic excellence. She’s also recognized as a Teradata Diversity in Tech Scholar, Mitacs Globalink Research Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having founded FEMCodes to empower women in STEM fields.
This post was originally published on here
