This week at DataraFlow Cohort 2, I explored NumPy, Pandas, and intermediate SQL, strengthening my understanding of data analysis and database management.
Have you ever felt that special kind of silence, the one that settles in when you stare at an error and have no idea what it means? Your tea goes cold. Your confidence quietly leaves the room. And somewhere in the back of your mind, a small voice asks: Maybe I’m just not cut out…
This week, I spent time working with SQL Server and practising how to retrieve, clean, analyse, and summarise data from different datasets. The experience was more practical than simply learning SQL syntax because I had to work with existing databases, understand their…
This week I worked through a set of NumPy, pandas and SQL tasks. Most of them looked simple on paper. Test an array for missing values. Show the first ten rows of a table. Count the rainy days in London.
When I first started learning data analysis, I thought the process was straightforward. You load a dataset, write a few lines of Python or SQL, generate some output, and interpret what you see.
This week covered three things: more NumPy, deeper work with Pandas Series, and my first real SQL assessment pulling from an actual live database instead of a local file.
Choosing a research title sounded simple until I had to find a problem worth solving, investigate what was already known, and figure out how to make the research work.
For a long time, I thought research was mainly about choosing a topic, searching for information online, and putting everything together in a document.
Every data science journey starts with a foundational question. Mine began in the classroom during a heated debate: Is precision agriculture and artificial intelligence an overblown hype, or is it a genuine, scalable reality?
This week, we were asked to write a research proposal on any data science topic. At first, I thought it would be a matter of picking a topic and getting straight to writing. But I soon realized that there is a lot more to a research proposal than I expected.
There is something interesting about returning to something you once enjoyed. You may not remember everything, and the process might feel slower than you expected, but somewhere along the way, things begin to make sense again. That has been my experience this week as I continue…
Week 6 of my DataraFlow internship was a practical learning experience. I moved from practising with small datasets to retrieving real data from a cloud database and analysing it in a Jupyter notebook.
This week I worked with NumPy, pandas and intermediate SQL. I started with small array exercises and ended up asking whether a loan dataset could be trusted. Below is what I learned, what I solved, what went wrong along the way, and what I’d do differently. What I learned NumPy:…
This Week I Wrestled With Real-World SQL — And Real-World Data Won. The datasets I worked on where in Weather, Milk, and Loan Approvals which sounds random, but it was the most practical SQL week so far. If you told me last week that getting an average temperature would be hard,…
Week 3: The Week Python Finally Started Making Sense… Until My Math Quiz Broke!
Written byMustapha Anuhi
