GitHub Beginners in Data Science: Here’s an Easy Way to Start!

If you are an aspiring data scientist, you will need to know how GitHub works. You will probably want to use it for your projects.

GitHub beginners often feel really stupid (like I did) when they first encounter the online web site at GitHub.com. Even if programmers have expertise in other software, when they are GitHub beginners, they often feel like they are starting over when they first look at the web site.

GitHub beginners in data science often benefit from starting with some basic activities. I recently held a GitHub beginners workshop as part of my Public Health to Data Science rebrand program. In the workshop, we covered the following beginning activities:

  • Setting up their GitHub account
  • Modifying their GitHub profile
  • Searching for and exploring GitHub user profiles and repositories
  • “Starring” repositories and “following” profiles
  • Creating a “test” GitHub repository
  • Creating folders and subfolders in the test repository using “.gitkeep”, and
  • Using R Markdown and Notepad to add a “sexy” README to the repository.

Let’s walk through these together.

GitHub Beginners Have to Set up an Account and Profile

First, you will have to go to GitHub.com and set up your account and your profile. Here is a screen shot of my GitHub profile below.

On GitHub, you can fill out a personal profile. This can help you connect with collaborators to share programming code.

It’s important to have the following ready to input into your profile:

  • A profile pic (of you or another image)
  • A username you’ve selected for GitHub
  • A short bio that will attract other likeminded GitHub users
  • Contact and social media information

Below is an example of us working on your GitHub profiles during the recent workshop.

Watch Monika Wahi's data science tutorials on YouTube!

Watch how to create and modify a profile on GitHub.

In our workshop, we had a lot of trouble using the search function on GitHub to find known repositories and users. Watch a clip from our GitHub beginners workshop below to see how we solved this.

Watch Monika Wahi's data science tutorials on YouTube!

Watch us search and find users and repositories on GitHub.

Starring Repositories and Following Profiles

GitHub beginners (and some not-so-beginners) may not be aware of the social media functions on GitHub.

  • You can “star” repositories. This is like bookmarking them so you can return to them. There is a “star” menu that you can choose to see all the repositories you have starred. You can remove the star if you no longer want to bookmark them.
  • You can “follow” people. But, you cannot “follow” repositories. If you want to “follow” a repository (e.g., be notified of updates to it), you actually have to follow the user who has the repository.
Watch Monika Wahi's data science tutorials on YouTube!

Learn about following users and starring repositories on GitHub.

Creating a “Test” GitHub Repository

Let’s face it – GitHub is not particularly intuitive. Therefore, it’s often beneficial for GitHub beginners to create a test repository so they can practice using the GitHub features without breaking anything important.

In our GitHub beginners workshop, one of the participants created a test repository – and this was actually more confusing than I thought it would be. We cleared up the confusion by adding a .gitkeep file, as shown in the clip below.

Watch Monika Wahi's data science tutorials on YouTube!

Watch us start a new repository and add a .gitkeep file.

Adding a README in R Markdown

This is an awesome trick I learned – which is how to use Notepad to create a “sexy” README in R Markdown, and then upload it to a GitHub repository. It really makes you appear “too cool for school” compared to your contemporaries. Watch us practicing this maneuver in the workshop in the clip below.

Updated February 28, 2023. Revised banners June 18, 2023.

Watch Monika Wahi's data science tutorials on YouTube!

Watch us create and upload a Notepad README file in R Markdown to GitHub.

Read all of our data science blog posts!

CDC Wonder for Studying Vaccine Adverse Events: The Shameful State of US Open Government Data

CDC Wonder is an online query portal that serves as a gateway to many government [...]

AI Careers: Riding the Bubble

AI careers are not easy to navigate. Read my blog post for foolproof advice for [...]

Descriptive Analysis of Black Friday Death Count Database: Creative Classification

Descriptive analysis of Black Friday Death Count Database provides an example of how creative classification [...]

Classification Crosswalks: Strategies in Data Transformation

Classification crosswalks are easy to make, and can help you reduce cardinality in categorical variables, [...]

FAERS Data: Getting Creative with an Adverse Event Surveillance Dashboard

FAERS data are like any post-market surveillance pharmacy data – notoriously messy. But if you [...]

Dataset Source Documentation: Necessary for Data Science Projects with Multiple Data Sources

Dataset source documentation is good to keep when you are doing an analysis with data [...]

Joins in Base R: Alternative to SQL-like dplyr

Joins in base R must be executed properly or you will lose data. Read my [...]

NHANES Data: Pitfalls, Pranks, Possibilities, and Practical Advice

NHANES data piqued your interest? It’s not all sunshine and roses. Read my blog post [...]

Color in Visualizations: Using it to its Full Communicative Advantage

Color in visualizations of data curation and other data science documentation can be used to [...]

Defaults in PowerPoint: Setting Them Up for Data Visualizations

Defaults in PowerPoint are set up for slides – not data visualizations. Read my blog [...]

Text and Arrows in Dataviz Can Greatly Improve Understanding

Text and arrows in dataviz, if used wisely, can help your audience understand something very [...]

Shapes and Images in Dataviz: Making Choices for Optimal Communication

Shapes and images in dataviz, if chosen wisely, can greatly enhance the communicative value of [...]

Table Editing in R is Easy! Here Are a Few Tricks…

Table editing in R is easier than in SAS, because you can refer to columns, [...]

R for Logistic Regression: Example from Epidemiology and Biostatistics

R for logistic regression in health data analytics is a reasonable choice, if you know [...]

1 Comments

Connecting SAS to Other Applications: Different Strategies

Connecting SAS to other applications is often necessary, and there are many ways to do [...]

Portfolio Project Examples for Independent Data Science Projects

Portfolio project examples are sometimes needed for newbies in data science who are looking to [...]

Project Management Terminology for Public Health Data Scientists

Project management terminology is often used around epidemiologists, biostatisticians, and health data scientists, and it’s [...]

Rapid Application Development Public Health Style

“Rapid application development” (RAD) refers to an approach to designing and developing computer applications. In [...]

Understanding Legacy Data in a Relational World

Understanding legacy data is necessary if you want to analyze datasets that are extracted from [...]

Front-end Decisions Impact Back-end Data (and Your Data Science Experience!)

Front-end decisions are made when applications are designed. They are even made when you design [...]

Reducing Query Cost (and Making Better Use of Your Time)

Reducing query cost is especially important in SAS – but do you know how to [...]

Curated Datasets: Great for Data Science Portfolio Projects!

Curated datasets are useful to know about if you want to do a data science [...]

Statistics Trivia for Data Scientists

Statistics trivia for data scientists will refresh your memory from the courses you’ve taken – [...]

Management Tips for Data Scientists

Management tips for data scientists can be used by anyone – at work and in [...]

REDCap Mess: How it Got There, and How to Clean it Up

REDCap mess happens often in research shops, and it’s an analysis showstopper! Read my blog [...]

GitHub Beginners in Data Science: Here’s an Easy Way to Start!

GitHub beginners – even in data science – often feel intimidated when starting their GitHub [...]

ETL Pipeline Documentation: Here are my Tips and Tricks!

ETL pipeline documentation is great for team communication as well as data stewardship! Read my [...]

Benchmarking Runtime is Different in SAS Compared to Other Programs

Benchmarking runtime is different in SAS compared to other programs, where you have to request [...]

End-to-End AI Pipelines: Can Academics Be Taught How to Do Them?

End-to-end AI pipelines are being created routinely in industry, and one complaint is that academics [...]

Referring to Columns in R by Name Rather than Number has Pros and Cons

Referring to columns in R can be done using both number and field name syntax. [...]

The Paste Command in R is Great for Labels on Plots and Reports

The paste command in R is used to concatenate strings. You can leverage the paste [...]

Coloring Plots in R using Hexadecimal Codes Makes Them Fabulous!

Recoloring plots in R? Want to learn how to use an image to inspire R [...]

Adding Error Bars to ggplot2 Plots Can be Made Easy Through Dataframe Structure

Adding error bars to ggplot2 in R plots is easiest if you include the width [...]

AI on the Edge: What it is, and Data Storage Challenges it Poses

“AI on the edge” was a new term for me that I learned from Marc [...]

Pie Chart ggplot Style is Surprisingly Hard! Here’s How I Did it

Pie chart ggplot style is surprisingly hard to make, mainly because ggplot2 did not give [...]

Time Series Plots in R Using ggplot2 Are Ultimately Customizable

Time series plots in R are totally customizable using the ggplot2 package, and can come [...]

Data Curation Solution to Confusing Options in R Package UpSetR

Data curation solution that I posted recently with my blog post showing how to do [...]

Making Upset Plots with R Package UpSetR Helps Visualize Patterns of Attributes

Making upset plots with R package UpSetR is an easy way to visualize patterns of [...]

4 Comments

Making Box Plots Different Ways is Easy in R!

Making box plots in R affords you many different approaches and features. My blog post [...]

Convert CSV to RDS When Using R for Easier Data Handling

Convert CSV to RDS is what you want to do if you are working with [...]

GPower Case Example Shows How to Calculate and Document Sample Size

GPower case example shows a use-case where we needed to select an outcome measure for [...]

Querying the GHDx Database: Demonstration and Review of Application

Querying the GHDx database is challenging because of its difficult user interface, but mastering it [...]

Variable Names in SAS and R Have Different Restrictions and Rules

Variable names in SAS and R are subject to different “rules and regulations”, and these [...]

Referring to Variables in Processing Data is Different in SAS Compared to R

Referring to variables in processing is different conceptually when thinking about SAS compared to R. [...]

Counting Rows in SAS and R Use Totally Different Strategies

Counting rows in SAS and R is approached differently, because the two programs process data [...]

Native Formats in SAS and R for Data Are Different: Here’s How!

Native formats in SAS and R of data objects have different qualities – and there [...]

SAS-R Integration Example: Transform in R, Analyze in SAS!

Looking for a SAS-R integration example that uses the best of both worlds? I show [...]

Dumbbell Plot for Comparison of Rated Items: Which is Rated More Highly – Harvard or the U of MN?

Want to compare multiple rankings on two competing items – like hotels, restaurants, or colleges? [...]

2 Comments

Data for Meta-analysis Need to be Prepared a Certain Way – Here’s How

Getting data for meta-analysis together can be challenging, so I walk you through the simple [...]

Sort Order, Formats, and Operators: A Tour of The SAS Documentation Page

Get to know three of my favorite SAS documentation pages: the one with sort order, [...]

Confused when Downloading BRFSS Data? Here is a Guide

I use the datasets from the Behavioral Risk Factor Surveillance Survey (BRFSS) to demonstrate in [...]

2 Comments

Doing Surveys? Try my R Likert Plot Data Hack!

I love the Likert package in R, and use it often to visualize data. The [...]

2 Comments

I Used the R Package EpiCurve to Make an Epidemiologic Curve. Here’s How It Turned Out.

With all this talk about “flattening the curve” of the coronavirus, I thought I would [...]

Which Independent Variables Belong in a Regression Equation? We Don’t All Agree, But Here’s What I Do.

During my failed attempt to get a PhD from the University of South Florida, my [...]

GitHub beginners – even in data science – often feel intimidated when starting their GitHub accounts and trying to interact with the web page. Don’t be shy! Catch the highlights from a recent GitHub beginners workshop I held!

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Verified by MonsterInsights