I'm new to data analysis and excited to kick off my first real project. I've started using basic functions for analyzing and cleaning data, especially with a focus on a spreadsheet for my stock market investments. I'm aiming to eventually work as a financial analyst. I'm currently using VS Code with Python for my analysis, and I have some basic knowledge of SQL. Here's what I've done so far:
1. Load the data (obviously)
2. Check the size of the dataset to understand what I'm working with
3. Get the column headers and clean them up (I see this as a straightforward fix)
4. Perform basic operations like .head(), .tail(), .info(), and .describe().
I'm curious if there's a more effective approach. For those with experience, what steps do you take first? How did you get started? Any tips would be appreciated!
2 Answers
Great start! EDA is definitely a cornerstone of data analysis. I’d say it’s standard practice to perform shaping and describing right off the bat, as it gives you a fundamental understanding of what the dataset contains. While identifying duplicates is straightforward, there isn't a one-size-fits-all rule for inspecting datasets. It's often helpful to consider the context of the data. Always check for missing values and think about the specific insights or analysis you aim to present. It can differ based on the dataset's content and purpose, so adapt as needed!
Hey there! It sounds like you're on the right track with your exploratory data analysis (EDA). After the steps you mentioned, I'd recommend checking for null values or duplicates to ensure your dataset is clean before you dive deeper. Once you've got that sorted, I usually start visualizing the basic features of the data. It’s a great way to spot trends or anomalies you might need to address. If you're looking for an example of this process, check out this project I did on GitHub: [EDA_Project](https://github.com/gonkikong0/Minor-Projects/blob/main/Restaurant%20Orders%20Analysis/Restaurant_Orders_EDA.ipynb). I think it could help clarify things for you.
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