[A hundred words are not as good as seeing once] Basic SQL for data analysis

Hello, this is Minju from Datalian. I provide feedback on the projects of students at the SQL data analysis camp run by Datalian. As I continue to provide feedback on projects, I have noticed many mistakes that many people frequently make.

“I’m trying to write a data analytics portfolio, but I’m not sure if it’s right.”

People preparing for employment often send me these concerns. Many people studying data analysis will probably do data analysis projects, and I think they all have similar concerns. In this article, I will show you some examples and common mistakes, and tell you how to avoid them. I will share tips to make portfolio writing a little easier.

1. Unable to determine results

Sometimes, when drawing a graph, there are cases where there is no title, no axes, units are missing from the figures, or there is no legend. When this happens, the information in the graph cannot be properly conveyed.

2. Writing confusing expressions

It is important to express yourself clearly. Mixing similar terms to express the same meaning or mixing English and Korean notations can also distract the reader.

3. No business explanation when setting the criteria

When conducting analysis, there are many cases where criteria need to be set. Often, these criteria are set outside of the data.

Simply saying that the average revisit cycle is one week is not a good standard. You need to have a business context, such as, "We upload content once a week, so let's see if people revisit once a week," to set a good standard.

4. Be obsessed with cool tools

I often get asked, “What tools should I learn to start data analysis?” Just because you use a fancy tool doesn’t mean you’ll get a good visualization. Good visualizations are tool-agnostic.

If you can use a specific tool and want to show off your skills, then go ahead and use it, but if not, there's absolutely no need to learn and use a difficult tool.

5. Write about something you don't know much about.

If you want to do a project using a cool analysis technique, but end up using an analysis technique you are not familiar with, it can be a negative because it will show that you do not know it well.

In interviews, you may be asked, “Why did you analyze it this way?” so it would be good to keep this in mind when analyzing.

6. Describe the project process that the reader is not interested in.

When I was a job seeker, I felt like I had to show something, so I wrote down the project details in detail. I wanted to show, 'I worked so hard on this project!'

But from the reader's perspective, such information may feel out of place or out of context, which can hinder understanding.

If you can avoid all these common mistakes, you'll be able to make your portfolio more complete, right?

We will be talking about 4 tips to improve the completeness of your data analysis portfolio in the February seminar. If you are interested, please sign up now 😉

Monthly Datalian Seminar February 2023 [How should data analysts prepare?]

⏰ Application Deadline: 2/13 (Mon)

👉 Go to the seminar to learn more: http://bit.ly/3kmIEjK