fbpx
until LLM Bootcamp: In-Person (Seattle) and Online Learn more

GitHub Repositories

This blog lists down-trending data science, analytics, and engineering GitHub repositories that can help you with learning data science to build your own portfolio.  

What is GitHub?

GitHub is a powerful platform for data scientists, data analysts, data engineers, Python and R developers, and more. It is an excellent resource for beginners who are just starting with data science, analytics, and engineering. There are thousands of open-source repositories available on GitHub that provide code examples, datasets, and tutorials to help you get started with your projects.  

This blog lists some useful GitHub repositories that will not only help you learn new concepts but also save you time by providing pre-built code and tools that you can customize to fit your needs. 

Want to get started with data science? Do check out ourData Science Bootcamp as it can navigate your way!  

Best GitHub repositories to stay ahead of the tech Curve

With GitHub, you can easily collaborate with others, share your code, and build a portfolio of projects that showcase your skills.  

Trending GitHub Repositories
Trending GitHub Repositories
  1. Scikit-learn: A Python library for machine learning built on top of NumPy, SciPy, and matplotlib. It provides a range of algorithms for classification, regression, clustering, and more.  

Link to the repository: https://github.com/scikit-learn/scikit-learn 

  1. TensorFlow: An open-source machine learning library developed by Google Brain Team. TensorFlow is used for numerical computation using data flow graphs.  

Link to the repository: https://github.com/tensorflow/tensorflow 

  1. Keras: A deep learning library for Python that provides a user-friendly interface for building neural networks. It can run on top of TensorFlow, Theano, or CNTK.  

Link to the repository: https://github.com/keras-team/keras 

  1. Pandas: A Python library for data manipulation and analysis. It provides a range of data structures for efficient data handling and analysis.  

Link to the repository: https://github.com/pandas-dev/pandas 

Add value to your skillset with our instructor-led live Python for Data Sciencetraining.  

  1. PyTorch: An open-source machine learning library developed by Facebook’s AI research group. PyTorch provides tensor computation and deep neural networks on a GPU.  

Link to the repository: https://github.com/pytorch/pytorch 

  1. Apache Spark: An open-source distributed computing system used for big data processing. It can be used with a range of programming languages such as Python, R, and Java.  

Link to the repository: https://github.com/apache/spark 

  1. FastAPI: A modern web framework for building APIs with Python. It is designed for high performance, asynchronous programming, and easy integration with other libraries.  

Link to the repository: https://github.com/tiangolo/fastapi 

  1. Dask: A flexible parallel computing library for analytic computing in Python. It provides dynamic task scheduling and efficient memory management.  

Link to the repository: https://github.com/dask/dask 

  1. Matplotlib: A Python plotting library that provides a range of 2D plotting features. It can be used for creating interactive visualizations, animations, and more.  

Link to the repository: https://github.com/matplotlib/matplotlib

 


Looking to begin exploring, analyzing, and visualizing data with Power BI Desktop? Our
Introduction to Power BItraining course is designed to assist you in getting started!

  1. Seaborn: A Python data visualization library based on matplotlib. It provides a range of statistical graphics and visualization tools.  

Link to the repository: https://github.com/mwaskom/seaborn

  1. NumPy: A Python library for numerical computing that provides a range of array and matrix operations. It is used extensively in scientific computing and data analysis.  

Link to the repository: https://github.com/numpy/numpy 

  1. Tidyverse: A collection of R packages for data manipulation, visualization, and analysis. It includes popular packages such as ggplot2, dplyr, and tidyr. 

Link to the repository: https://github.com/tidyverse/tidyverse 

In a nutshell

In conclusion, GitHub is a valuable resource for developers, data scientists, and engineers who are looking to stay ahead of the technology curve. With the vast number of repositories available, it can be overwhelming to find the ones that are most useful and relevant to your interests. The repositories we have highlighted in this blog cover a range of topics, from machine learning and deep learning to data visualization and programming languages. By exploring these repositories, you can gain new skills, learn best practices, and stay up-to-date with the latest developments in the field.

Do you happen to have any others in mind? Please feel free to share them in the comments section below!  

 

April 27, 2023

Data Science Dojo has launched Jupyter Hub for Data Visualization using Python offering to the Azure Marketplace with pre-installed data visualization libraries and pre-cloned GitHub repositories of famous books, courses, and workshops which enable the learner to run the example codes provided.

What is data visualization?

It is a technique that is utilized in all areas of science and research. We need a mechanism to visualize the data so we can analyze it because the business sector now collects so much information through data analysis. By providing it with a visual context through maps or graphs, it helps us understand what the information means. As a result, it is simpler to see trends, patterns, and outliers within huge data sets because the data is easier for the human mind to understand and pull insights from the data.

Data visualization using Python

It may assist by conveying data in the most effective manner, regardless of the industry or profession you have chosen. It is one of the crucial processes in the business intelligence process, takes the raw data, models it, and then presents the data so that conclusions may be drawn. Data scientists are developing machine learning algorithms in advanced analytics to better combine crucial data into representations that are simpler to comprehend and interpret.

Given its simplicity and ease of use, Python has grown to be one of the most popular languages in the field of data science over the years. Python has several excellent visualization packages with a wide range of functionality for you whether you want to make interactive or fully customized plots.

PRO TIP: Join our 5-day instructor-led Python for Data Science training to enhance your visualization skills.

Data visualization using Python
Using Python to visualize Data

Challenges for individuals

Individuals who want to visualize their data and want to start visualizing data using some programming language usually lack the resources to gain hands-on experience with it. A beginner in visualization with programming language also faces compatibility issues while installing libraries.

What we provide

Our Offer, Jupyter Hub for Visualization using Python solves all the challenges by providing you with an effortless coding environment in the cloud with pre-installed Data Visualization python libraries which reduces the burden of installation and maintenance of tasks hence solving the compatibility issues for an individual.

Additionally, our offer gives the user access to repositories of well-known books, courses, and workshops on data visualization that include useful notebooks which is a helpful resource for the users to get practical experience with data visualization using Python. The heavy computations required for applications to visualize data are not performed on the user’s local machine. Instead, they are performed in the Azure cloud, which increases responsiveness and processing speed.   

Listed below are the pre-installed data visualization using python libraries and the sources of repositories of a book to visualize data, a course, and a workshop provided by this offer:

Python libraries:

  • NumPy
  • Matplotlib
  • Pandas
  • Seaborn
  • Plotly
  • Bokeh
  • Plotnine
  • Pygal
  • Ggplot
  • Missingno
  • Leather
  • Holoviews
  • Chartify
  • Cufflinks

Repositories:

  • GitHub repository of the book Interactive Data Visualization with Python, by author Sharath Chandra Guntuku, AbhaBelorkar, Shubhangi Hora, Anshu Kumar.
  • GitHub repository of Data Visualization Recipes in Python, by Theodore Petrou.
  • GitHub repository of Python data visualization workshop, by Stefanie Molin (Author of “Hands-On Data Analysis with Pandas”).
  • GitHub repository Data Visualization using Matplotlib, by Udacity.

Conclusion:

Because the human brain is not designed to process such a large amount of unstructured, raw data and turn it into something usable and understandable form, we require techniques to visualize data. We need graphs and charts to communicate data findings so that we can identify patterns and trends to gain insight and make better decisions faster. Jupyter Hub for Data Visualization using Python provides an in-browser coding environment with just a single click, hence providing ease of installation. Through our offer, a user can explore various application domains of data visualizations without worrying about the configuration and computations.

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. We are therefore adding a free Jupyter Notebook Environment dedicated specifically for Data Visualization using Python. The offering leverages the power of Microsoft Azure services to run effortlessly with outstanding responsiveness. Make your complex data understandable and insightful with us and Install the Jupyter Hub offer now from the Azure Marketplace by Data Science Dojo, your ideal companion in your journey to learn data science!

Try Now!

August 18, 2022

Related Topics

Statistics
Resources
Programming
Machine Learning
LLM
Generative AI
Data Visualization
Data Security
Data Science
Data Engineering
Data Analytics
Computer Vision
Career
AI