As an integral part of many industries, Artificial Intelligence is progressing rapidly and becoming a part of our daily lives. Just by using the internet, there could be a chance that you will be part for an algorithm used for the future of technology through data. But, how far are we from true artificial intelligence? Could A.I. be among us?
Everyday we browse the Internet, it has become embedded in every aspect of our day-to-day lives. We use it for communication, education, entertainment, and now during this pandemic many of us depend on it to do shopping since it is best for us just to stay at home. It has been so convenient for us to get and cater the things we need in just a few taps or clicks in our smartphones or computer. But, was there a time you realize that it became overboard and it seems that most of the thing you see in the internet were…
Pandas: A fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.
Matplotlib: Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
In this article, we will be exploring an Insurance dataset by using Pandas for dataframes and Matplotlib to produce graphs for the analysis.
The data that we will be analyzing contains several variables such as age, sex, children, smoker, region, and charges. Through this analysis, we can determine the relationship of several factors on insurance charges.
We can check these Basic…
Well, data is everywhere, which makes it very powerful. Everyday, the world is dealing with lots and lots of different data from different fields and sectors we have. Anything under the sun we could possibly collect data from it; like from prices, weather, the internet, and even our todays enemy, Covid-19 cases. So, being able to work with data, process, analyze, and present it efficiently with advanced tools/methods of Data Science could be very beneficial macroscopically.
Before we can put up something successfully, we always start with the basics. And as for data, this is in the context of preparing…
i. Strings and Lists
ii. Variable Declaration
iv. IF and ELSE Statements
v. FOR loops
vi. Functions, Range ( )Functions
Last time in our FTW Data Science class, we had an activity where we had to create a 10-Question Python Quiz Code that counts the score in which you need to have 70% correct answers to pass the quiz.
So, here is the output of what I have created:
Throughout this article, I will discuss a step-by-step guide for using some of the top Python Integrated Development Environments/Code Editors:
I. Google Colaboratory | II. Jupyter Notebook | III. Spyder.
With a noteworthy guide for the feature of uploading Python code files to your Github account.
Here, we are going to learn the Basics of the 2 most essential tools used in Data Science:
Spyder is a powerful scientific environment written in Python, for Python, and designed by and for scientists, engineers and data analysts.
Launch the Spyder App