Wendy C. KurniawanFundamental Concepts for Learning Data Science -Hi There! Learning something new can feel overwhelming—especially at university, where fields of knowledge are so vast that it’s hard to know where to even place your first step.
When I began my Data Science journey, I constantly heard that mathematics is the 'backbone' of the field or even the 'language of the universe.' This only added to my confusion: Where do I start? Do I need to be a professional mathematician to succeed?
The answer is both yes and no, depending on how deep your curiosity goes. My advice? Just start. I’ve found that you don’t need to master all of pure mathematics to be effective. Instead, we can focus on a few key pillars to build a strong foundation. Here are the essentials to get you started.
🧠 The Four Pillars of Data Science Math
| Subject | Why it matters |
|---|---|
| Linear Aljebra | This is the "language" of data. It helps to understand how computers handle large datasets (matrices) and how algorithms like Principal Component Analysis (PCA) or Neural Networks function. |
| Calculus | Calculus is about "change." In data science, we use it for Optimization. Specifically, derivatives are used in Gradient Descent to teach models how to minimize errors and improve accuracy. |
| Statistics | This is how we draw conclusions from data. It helps you understand whether a result is "significant" or just a random fluke (Hypothesis Testing) and how to summarize large groups of information. |
| Probability | Data is often messy and uncertain. Probability helps us model that uncertainty, which is the secret sauce behind things like Weather Forecasting, Insurance Pricing, and Bayesian Classifiers. |
Don’t worry, friends! Some of these terms might be new to you, but they are simply the names of common methods we use in Data Science.
If you're curious to learn more, you can explore the Level 1 Reading List, where the books explain these fundamentals in a relaxed, concise way. And if you’d like to continue this journey with me, feel free to dive into these related posts:
- Mathematical Notation
- Introduction to Linear Aljebra
- Introduction to Calculus
- Introduction to Statistics and Probability
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