[Student DataFest 2018] Skilltest - Python for Data Science

We get numerous queries related to learning Data Science. Most of them are questions like “What is Data Science?” or “How to become a data scientist?” - which are asked to us multiple time every day specially. For this, we are doing Student DataFest 2018 this summer, where we are focusing on providing learning resources (video based training) for students, skilltest to test their progress and hackathon to compete.

This week's skilltest evaluates how much you have grasped Python in a practical manner. The questions emphasizes on the commands, functions or packages to be used to perform a specific task in data science using Python

The topics covered in the skilltest are:

• Python for Data science
• Conditional statements and Looping
• Pandas and Matplotlib

You can avail a maximum of 50 AV points.

The skilltest is based on the course 'Introduction to Data Science' in regards to Student DataFest 2018. Please go through the course before attempting the skilltests, as you will not be able to retake the skilltest once started.

Rules

• The questions are MCQ types. Each question carries equal marks.
• There is no negative marking for any wrong answer.
• There is only one option correct for each question.

FAQs

1. Who is a student?
   Any one undergoing a course from a recognised university or college with graduation date on or after15th May 2018will be considered as a student.
   
   
2. I am undergoing a part time course, will I be considered as student?
   No
   
   
3. I am a student, but I am unable to register for this hackathon?
   First, create a profile on Analytics Vidhya (SignUp) and then register yourself as a Student here.
   
   
4. What do you need in a Photo id?
   A photo id is an identification issued by your college / university acknowledging that you are a student. The id would typically have your roll number, start date and end date
   
   
5. How long will it take to be verified as a student?
   We will do it with in a couple of days. Normally, it is faster than that.