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Think Like Data Scientist: Processes and Tools

Think Like Data Scientist: Processes and Tools

The process of data science, involves software as a necessary set of tools, just as a traditional scientist might use test tubes, flasks,

What you'll learn

  • Structure, manage, and deliver small to medium data science project.
  • Boost workflows with literate programming and reproducibility research standards.
  • Enhance, accelerate, and optimize your workflow with Jupyter notebook ecosystem; markdown, tips, and trics.
  • Examine and identify key aspects of the project to focus on.
  • Get hands-on experience in all those concepts on your skin by working on a case study!

Requirements

  • Basics of Python, Jupyter notebook, and computer literacy (install software, python packages, …).
  • Basics of statistics (distribution, statistical indices, ...) and machine learning (k-means).
  • Curiosity and open mind!
  • Python - Data mining and Machine learning

Description

How would you manage a data science project?

What standards of data science do you know and use?

How to manage the code?

What tools are best for data science work?

How does your delivery to a client look like?

Those are some of the questions I am opening in this course. The course is about thinking. During the course, you will be given a series of similar questions so that you can challenge your own approach and experience. Then, I will share my own practice and knowledge so that you can compare it. All of that is given to you together with a data science case study. You can work on it on your own and then see how I solved it.

The main goal of this course is to help you discover and find your own way of doing a data science project and, at the same time, adopt approaches, which data science professionals widely use.

You can find the following topics in the course:

  • Project management phases, cyclic nature of data science project.
  • Data science standards.
  • How to organise your project's code; code ethics, files and folder organisation.
  • The course contains a case study, which uses the Python ecosystem.

Who this course is for:

  • Junior/mid senior data scientists, data analysts, industry students, and enthusiasts desiring for deeper insight into doing data science smoother, simple, more effective, and enjoyable.
  • Programmers/IT people curious about how data science discipline works.
  • Managers with some technical background trying to understand what data science project is about.

Code Redeem ML

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