Data Science on Github: How to Get Started Quickly

In summary, post code examples that are meaningful to a large project, and don't focus too much on your own repositories.
  • #1
Avatrin
245
6
Hi

I have the educational background to do a few jobs I have seen posted as "data scientist" or "data engineer". However, I keep getting told that to get a job like that with my background, in mathematics, I need to post code examples on Github. However, I always feel like my ideas are either too basic or too advanced (so, either it won't look good enough for recruiters or it'll take me forever to actually write the code). I thus never get started.

My question is, how good should my code be? Where should I start?
 
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  • #2
Just put it out there. If you include the link in your resume and job application, they'll look at it and decide if it is what they are looking for in a programmer.
 
  • #3
Avatrin said:
Hi

I have the educational background to do a few jobs I have seen posted as "data scientist" or "data engineer". However, I keep getting told that to get a job like that with my background, in mathematics, I need to post code examples on Github. However, I always feel like my ideas are either too basic or too advanced (so, either it won't look good enough for recruiters or it'll take me forever to actually write the code). I thus never get started.

My question is, how good should my code be? Where should I start?

I don't think you need to post code examples on GitHub to appeal to your employer. I've gone through thousands of applications and actually, on some 90% of the applications where I've seen it included, it has actually hurt the candidate instead.

If you do go the route of including your GitHub profile, I personally feel that a better signal than your own repositories is to have meaningful contributions to large projects. This also gives you an easy way to generate ideas that are relatable to your recruiters - because surely, if it's an important issue to a large project, it's probably accessible to a wide audience. A way to find these is to look for issues tagged with "good first issue" or "contributor-friendly". Here's a few examples for Python: 1, 2, 3.
 
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1. What is data science?

Data science is a multidisciplinary field that combines statistics, programming, and domain knowledge to extract insights from data. It involves collecting, cleaning, analyzing, and interpreting large datasets to inform decision-making and solve complex problems.

2. Why is GitHub important in data science?

GitHub is a platform that allows for collaborative and version-controlled code sharing. It is particularly useful in data science as it provides a way to manage and track changes to code, collaborate with team members, and showcase projects to potential employers.

3. How do I get started with data science on GitHub?

The first step is to create a GitHub account and familiarize yourself with the platform. Then, you can start by learning the basics of coding and data analysis using popular languages like Python or R. You can also explore existing data science projects on GitHub and contribute to them to gain experience and build your portfolio.

4. What are some essential tools for data science on GitHub?

Some essential tools for data science on GitHub include Git, a version control system, and GitHub Desktop, a graphical user interface for managing GitHub repositories. Other useful tools include Jupyter Notebooks for data analysis and visualization, and Docker for creating reproducible environments.

5. Are there any resources available to help me learn data science on GitHub?

Yes, there are various resources available to help you learn data science on GitHub. You can find online courses, tutorials, and documentation on GitHub's official website. You can also join data science communities on GitHub and participate in discussions and code reviews to improve your skills.

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