New to data science? Master these 5 skills first

yasirazeem227
Nov 19, 2024
3 min read

Updated: Feb 23

Ever wondered what makes a great data scientist? Spoiler: It’s not just about coding.

Fast Facts:

Python helps you code, clean, and analyse data.
Statistics helps you find patterns and make sense of numbers.
SQL is how you pull the right data from massive databases.
Machine learning is what makes AI and smart predictions possible.
Problem-solving is the ultimate skill—because data science is all about answering interesting questions!

Interested in data science?

Hey everyone! If you’ve ever thought about getting into data science, you might have asked yourself: Where do I even start? Do you need to be a coding genius? A maths prodigy? Or do you just need to know how to Google really well?

You don’t need to master Python or statistics before university, but having a basic grasp of the following will make things much easier:

✅ Logical thinking & problem-solving – This is essential for coding and data analysis.

✅ Basic Python – Even just knowing how to write simple scripts and manipulate data can help.

✅ Introductory statistics – Concepts like mean, median, probability, and standard deviation.

✅ Comfort with numbers – You don’t need to be a maths genius, but being comfortable working with data is a plus.

The good news? You don’t need to be a programming expert or a stats wizard from day one. But there are some skills that will make your data science journey way easier—and way more fun. Let’s break them down.

1. Python (or Another Programming Language) – Your Superpower

Think of Python as the Swiss Army knife of data science. It’s beginner-friendly, powerful, and used by almost every data scientist out there. Whether you’re cleaning messy data, building machine learning models, or making cool graphs, Python has the tools to do it all.

Where to start? Codecademy, Coursera, or even YouTube tutorials—just pick a small project and start coding!

2. Statistics – Making Sense of the Numbers

You know how Spotify seems to magically know what music you’ll love? That’s statistics in action. In data science, you’re constantly working with numbers, so understanding probabilities, averages, and trends will help you make sense of what the data is actually saying.

Where to start? Try Khan Academy’s probability and statistics course—it’s beginner-friendly and super practical.

3. SQL – Your Key to Unlocking Databases

Data scientists deal with HUGE amounts of information, and most of it is stored in databases. SQL (Structured Query Language) is what helps you pull the right data from massive datasets. Think of it as a search engine for information—except instead of Googling cat videos, you’re searching for patterns in company data.

Where to start? Play around with SQLZoo or W3Schools—it’s way easier than it sounds!

4. Machine Learning – Teaching Computers to Think

Ever wondered how Netflix knows what show to recommend? Or how Google predicts what you’re about to type? That’s machine learning, a core part of data science that helps computers make smart predictions. It might sound complicated, but the basic idea is simple: feed computers a bunch of data, let them find patterns, and then use those patterns to make decisions.

Where to start? Google’s "Machine Learning Crash Course" is a great intro!

5. Problem-Solving – Your Secret Weapon

Here’s the truth: being good at data science isn’t just about technical skills. It’s about knowing how to think through problems, ask the right questions, and connect the dots. Whether you’re analysing customer behaviour or predicting stock trends, creative problem-solving is what sets apart great data scientists from the rest.

Where to start? Solve logic puzzles, play around with Kaggle datasets, or just start questioning everything—why did this trend happen? What would happen if we changed X?

What Can Be Learned at University?

Advanced programming (Python, R, SQL, etc.)
In-depth statistics & machine learning
Big data & cloud computing
Data ethics & real-world applications

Let’s Chat!

Which of these skills are you already working on? Or is there one that seems intimidating? Let’s talk in the comments—I’d love to hear where you’re at in your data science journey!