How to Be a Data Scientist (Even When It Feels Impossible) – Part 2

(Missed Part 1? Read it here)

So, you’ve decided data science is for you, and you’re taking it one step at a time, learning Excel, SQL, Python, and statistics without getting overwhelmed. That’s great.

But honestly: knowing isn’t enough. You have to apply what you’ve learned. Let’s talk about building projects, networking (even if you’re introverted like me), and staying relevant in tech.

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Step 3: Build, Don’t Just Learn

Courses and certificates are great, but they won’t prove you can do the work. What will? Projects.

Think of it this way: If you’re learning to cook, you don’t just watch videos and collect recipes. You get into the kitchen and start experimenting. Data science is the same.

Start Small with Practical Projects:

• Excel & Power BI: Analyze your personal expenses using PivotTables and DAX formulas.

• SQL: Clean and analyze sales data using GROUP BY, JOIN, and CASE WHEN.

• Python : Load and manipulate datasets to analyze trends.

• Statistics & Machine Learning: Predict house prices using regression models.

Your projects don’t have to be groundbreaking. They just have to show your thought process.

Key Python Libraries for Projects:

• Pandas → Data manipulation (filtering, cleaning, transforming)

• NumPy → Numerical computations (arrays, statistics)

• Matplotlib & Seaborn → Data visualization (charts, histograms, correlations)

• Scikit-learn → Machine learning (regression, classification, clustering)

Even a simple project can make a difference when someone asks, “What have you built?”—because now, you have an answer.

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Step 4: Network (Even If You’re Introverted Like Me)

I used to think networking meant forcing conversations and attending awkward events. But real networking isn’t about being extroverted, it’s about showing up where opportunities exist.

Here’s how You can do it :

• LinkedIn – Engage, Don’t Just Scroll

➡️ Comment on posts related to data science.

➡️ Share insights from what you’re learning.

➡️ Connect with people in the industry.

• Online Communities – Learn & Share

➡️ Join Data Science Nigeria, Kaggle, DataCamp, and Stack Overflow.

➡️ Participate in discussions and competitions.

➡️ Share your learning journey (helping others makes you visible).

• Content Creation – Share What You Learn

➡️ Write about your learning journey on Substack, Medium, or X.

➡️ Share your projects on GitHub and explain your thought process.

➡️ Post about real-world problems you solved using data.

Most people don’t get jobs from job boards. They get them from people. You don’t need thousands of followers, you just need the right connections.

To Be Continued...

In the final part, we’ll talk about why statistics and probability are essential, how to keep growing in tech, and how to stay relevant in the data field.

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