Today, I began solving the SQL challenges on Data In Motion. It is important to keep your skills sharp, like a knife. Remember, those who don't practice, tend to forget. This is the first challenge, thanks to @LLCdatainmotion . My solution is on my GitHub https://t.co/l9nZJvuy5K
Continuing the learning, here is my #Python code for @LLCdatainmotion Python Coding Challenge. The following code is to capitalize the first letter of the string input by the user. I particularly liked the intuitive method of string slicing!
Decided to try out my SQL skills with Tinyshop sales @LLCdatainmotion
I don't say what I can do, I show you what I have done and can do.
"I am a doer"
https://t.co/YDiY3SRFNF
This dashboard is looking at different data science jobs and their salaries rankings and the employees' demographics. Thanks to @LLCdatainmotion, they have a good data analysis challenge platform for free.
This is my entry for Week 5 of @LLCdatainmotion Python Challenge. The question was to create a function that takes in the current mood and returns a sentence in the following format: “Today, I am feeling {mood}”. If no argument is passed, return “Today, I am feeling neutral”.
This is my entry for Week 7 of @LLCdatainmotion Python Challenge. I have been really learning a lot through this challenge and I can't wait to solve some more!
Another week, another @LLCdatainmotion data analysis challenge. For Week 19, I worked on some roller coaster data. The great thing about these challenges is seeing how much faster I'm getting at answering these questions: https://t.co/G0JOUMH5Wq
Q #1
I knew I had to use lag and lead to compare records from before and after to find consecutive visits, but I wasn't of the most efficient method. I went with the use of "Case" for this one to cover the 3 possibilities.
Q#2
Pretty straight forward.
@LLCdatainmotion
Here's the results of my Week 18 Data Analysis Challenge for @LLCdatainmotion: https://t.co/V2p2zG9vHk. This week was a lot of visualizations, and I found myself flexing between the built in pandas plotting and Seaborn, mostly depending on what I knew how to do easily.