Beginner’s Guide to Data Science: What I Wish I Knew Before Starting

Hey there! When I first heard “Data Science,” I pictured brainy PhDs at Google crunching numbers in a lab. Sound familiar? But here’s the truth I’ve learned: data science isn’t just for geniuses—it’s for curious people like you and me. I’m still a beginner, stumbling through Python and stats, but I’m writing this to share what I wish I knew when I started. Whether you’re totally new or just curious, this guide’s for you—no overwhelm, just honest tips from one newbie to another.

1. What Is Data Science, Really?

Forget the fancy definitions. Data science is like being a detective, turning messy data into answers that solve real problems. It’s why:

• Netflix nails your next binge-watch.

• Amazon knows you need new headphones.

• Your fitness app spots trends in your steps.

Data science is everywhere, and that’s what makes it so exciting. It’s not just tech—it’s business, healthcare, sports, you name it.

2. Is Data Science for Me?

I asked myself this a lot. I’m not a coder by trade, and math wasn’t my thing growing up. But here’s what I figured out:

• You don’t need to be a math wizard.

• You don’t need to know it all on day one.

• You do need curiosity, patience, and a willingness to keep going.

If you love solving puzzles, digging into numbers, or telling stories with data, this could be your thing. I’m proof you don’t need a fancy degree to start.

3. What to Learn First (Don’t Skip This!)

I almost jumped straight to “machine learning” because it sounded cool. Big mistake. Start with the basics—trust me, they’re the real game-changers. Here’s what you need early on:

• Python: Learn the basics (variables, loops, lists), then dive into libraries like:

  • NumPy for number crunching.

  • Pandas for handling data like a pro.

  • Matplotlib and Seaborn for cool charts.

• Statistics: Get comfy with mean, median, standard deviation, and probability basics. No PhD needed!

• SQL: Query databases to grab data—it’s a must for real-world jobs.

• Excel: Still handy for quick data cleaning or pivot tables.

• Git & GitHub: Save and share your work like a team player.

• Jupyter Notebooks: Your go-to for coding, visuals, and notes in one place.

Pro Tip: Communication matters. Can you explain your findings clearly? That’s gold in data science. And consistency wins—1 hour a day beats cramming all weekend.

4. Don’t Fall for the Buzzwords

AI, deep learning, LLMs—they’re shiny, but don’t chase them yet. Focus on the core:

• Can you clean a messy dataset?

• Can you spot patterns and tell a story?

• Can you explain it simply to your friend?

That’s data science at its heart. The fancy stuff comes later.

5. The Stuff Nobody Talks About

Let’s keep it real:

• You’ll feel lost sometimes. That’s normal.

• You’ll think everyone’s ahead of you. They’re not—just on their own path.

• You’ll doubt yourself. Push through anyway.

Here’s the big one: you don’t need to know everything to be useful. Start small, do it well, and you’re already adding value.

6. My Journey (and Why I’m Sharing)

I’m learning Python, stats, SQL, and Git, aiming for 2–3 hours a day (not always perfectly, but I try). My dream? Work in Japan as a data scientist. Writing this blog helps me learn better and connect with others like you. I’m not an expert—I’m just a guy figuring it out, and I want you to know you can too.

7. Your Next Steps—Start Today!

You don’t need to be “ready.” Just take one step. Here’s how:

1. Pick One Thing: Try a Python tutorial or a stats exercise. Start small.

2. Grab These Free Resources:

   • Kaggle’s Python Course for hands-on coding.

   • Khan Academy’s Statistics for beginner-friendly stats.

   • SQLZoo for SQL practice.

3. Keep a Journal: Jot down what you learn daily, even if it’s just “I made a bar chart!”

Let’s Grow Together

I’m just getting started, and I’d love for you to join me. What’s one data science skill you’ll try this week? Let’s build our skills, share our wins, and make data science less scary—together.

Happy learning,
Shantanu Singh