Real quick start to learning data science

Learn data science real fast

A super compressed course with real life demos to get students started on the data science journey. This course is meant for people who are not familiar with coding at all, and are looking for quick turnaround on learning data science and/or machine learning, artificial intelligence. Interested students should understand that the goal of this course is to do real life demo, not to learn theoretical concepts.

What you’ll learn

  • Choose and set up a development environment for starting on data science in 10 mins.
  • Run your first piece of code in under 20 mins..
  • Modify your code to make it your own in 10 mins..
  • Read and run different types of code to set you up on the path of data science in 20 mins..

Course Content

  • 67 min guide to starting with data science –> 5 lectures • 1hr 7min.

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Requirements

  • No prior experience needed. This is for very very basic beginners (you need not even have coded before)..

A super compressed course with real life demos to get students started on the data science journey. This course is meant for people who are not familiar with coding at all, and are looking for quick turnaround on learning data science and/or machine learning, artificial intelligence. Interested students should understand that the goal of this course is to do real life demo, not to learn theoretical concepts.

We start off by setting up the environment in which a student will start running notebooks with data science code. Setting up the environment is the most crucial base on which we will build our framework for doing data work.

We move on to reading and comparing multiple python notebooks written on the same data, and running both of them. The trick to learning anything in code is to realise that all code is just English written in a structural fashion, and reading and running code is super important to start in data science.

Last, but not the least, we pick up 1 random topic for exploration and go about implementing that topic in our running notebook, as well as modify the topic to integrate with the data that we are currently working on.

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