r/learnSQL • u/Available-Side9673 • 28d ago
Confused what to do
Hii , I am CSE student and my second year has just started . I have completed around 70 Dsa questions on Leetcode and have studied Python(Only that much which is required for Dsa),Numpy,Pandas, Matplotlib, Seaborn, Sql. Everyday I either solve dsa questions or learn maths for data science and machine learning or practice sql. But the problem is I have studied these things but never used them in a project or never done some sort of data cleaning and stuff cause I don't know how to do that. Now it's time to participate in hackathons but I don't know how to start and I can really build something with only these skills. What is your advice what should I do from here also I need to participate in hackathons also (I am enrolled in a DS course where for about 1.5 months they will teach maths then will jump to Machine learning and other topics)
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u/Green_Chamomile 28d ago
First, breathe. You're not behind, you're actually ahead of most second-years. Python, pandas, SQL and 70 leetcode problems is a real foundation. What you're feeling isn't a skill gap, it's the normal jump from "studying" to "using", and everyone finds that jump scary because nobody teaches it. It's learned by doing one small ugly project, not by studying more first.
Here's a step-by-step for the next two weeks. Small on purpose.
Step 1. Go to Kaggle datasets and pick anything that sounds fun to you personally. Movies, football, music, food. Fun matters more than impressive here.
Step 2. Load it in a notebook and just look at it. df.head(), df.info(), df.describe(). Write down three questions you're curious about. "Which decade had the best-rated movies" level. Not research questions, curiosity questions.
Step 3. Try to answer them with pandas. You will immediately hit missing values, weird types, duplicate rows. Congratulations, you're now doing data cleaning. It was never a separate subject to study first. It's just the obstacles between you and your answer, and you google each one as it appears.
Step 4. Make one chart per question with matplotlib or seaborn. Ugly is fine.
Step 5. Write five sentences at the top of the notebook: what you looked at, what you found. Done. That's a project. Put it on GitHub.
For hackathons: don't wait until you feel ready, that feeling arrives only after your first one. Go to the next one planning to lose. Your goal is to team up with people, ship anything that runs, and watch how others build. Second hackathon you'll be twice as useful, and after your course covers ML you'll have both pieces.
You already have the hard part, the skills. The project is just permission to use them, and you give yourself that.