Note of data science training EP 1: Intro – unboxing
It's a series about my learning session of Data Science.
Hi~ Hi~ all guys.
Happy new year 2020 belatedly. Here is the first time for me after missing blogging so long.
Right now, it’s a series about my learning session of Data Science.
Let’s begin –
What is data science for?
Imagine that, we are in a restaurant. I, as a data engineer, is like a storage keeper who taking care of fresh and quality ingredients. When a cook want something for a dish, data engineers have to validate and afford them.
So who are cooks? Of course, data scientists are. They are mastered how to make a dish more delicious, more nutrients, and more appetizing.
Collaboration of data engineers and data scientists benefits our customers and allow them to gain insights from our data products.
Tools for data scientists
Python
Python is one of popular data manipulating languages. We can find ALL of needed libraries of the works. One thing is to make sure we are using Python 3 because Python 2 will be the end of support at the writing time.
Besides, we can write R Language as well.
Free datasets
There are lots of public dataset and it’s free to play around. One of mine is Kaggle.com/datasets and Github.
For Git, it was my old blog about it here.
IDE and Text Editor
Many IDEs (integrated development environment) and Text editors are for Python users as a list here. Pick what you favor.
For me, I would prefer Jupyter.
Command line
It’s optional if we are using Git on Terminal. Here is my old blog about Command line.
Next time will be preliminary data exploration.
See ya~