From Zero to Hero : What I’ve learned so far in my Data Science class
I had a conversation before with a friend who aspires to become a data scientist and we merely talked about data science. And me as a good friend, I was just patiently listened and supported him. But when he started talking about coding, computing, machine learning and artificial intelligence models, I was like, “What is he talking about? Am I still on earth or am I talking to an Alien? Why it is too intimidating?” But kidding aside, I really cannot imagine going through the same aspiration as his because I knew to myself that it will be out of what I’ve used to.
One day while scrolling on my Facebook account, something came across to my timeline and saw this scholarship program in FTW foundation. When I went down on their Facebook page, something pops in my head and said to myself, “I want to feel challenged like these girls on FTW and step up my game”. So I bravely gave it a try and went through some rigorous process for a span of 2 months. To fast track the story, luckily I made it to the cut and I was so surprised!
Now, what’s next for me?
Now, here I am writing my own blog relating to what I’ve learned so far in my 4th day (out of 14 days) of data science class while listening to my favorite k-pop songs and taking a sip of my 3-in-1 coffee. What a great way to start!
So let me share to you my first programming lesson during class, the Colab.
What is Colab?
is a free Jupyter notebook environment that runs entirely in the cloud. Most importantly, it does not require a setup and the notebooks that you create can be simultaneously edited by your team members — just the way you edit documents in Google Docs. Colab supports many popular machine learning libraries which can be easily loaded in your notebook. — Tutorialspoint
Getting started
First, go to this Colab link to create your own account. Once done, open your account and it will take you to this section.
Create your own file notebook.
This is the Colab environment looks like.
Hover over the cell. Code and text will appear. This is where you can execute your code
1st Exercise: Loading data using Pandas
Now based from my first exercise, you need to add your code by hovering +Code, then click to add. Once your code is in-place, used shift+enter to run.
Below is the example when I imported my DataSeerGrabPrizeData.csv file from my computer.
This means, my file was successfully uploaded to my colab.
2nd Exercise: Editing and removing missing values
After uploading your data, you can view your data in tabular form. Below is the example.
Example on how I remove my missing values.
3rd Exercise: Downloading Edited file from Colab
Below is the example on how I did my editing from Colab. I renamed my previous file into “Grab_new.csv” and “Grab_with_new_row_numbers.csv” file. Press shift+enter to execute. Once you have executed, your new file will be downloaded automatically to your computer.
For sample exercise files, you may click on this link.
I’ll just end my sharing here for now and will be back for more learning and improvements. Be better than you were yesterday.
Have a great day ahead!
