Overview
- Open source data science projects add a lot of value to your resume and help you stand out in an interview
- Here there is 7 open source data science projects you should be working on this month
Introduction
I'm going to give you some advice that I wish someone had given me when I started my career in data science.. When I was navigating the obstacle-filled journey through the backwaters of data science, I had a great struggle before I got my first role.. Had all the qualifications (or so i thought) but something seemed wrong.
That gap between what I brought to the table and what the interviewer expected was experience in data science projects..
Data science projects add a lot of value to your resume, especially if you are a beginner. Most newcomers will have certifications, but adding open source data science projects will give you a significant advantage over the competition. And believe me, there are a staggering number of open source data science projects for you.
Here, I have put together a list of the top open source data science projects that were created or launched in June. This is part of my monthly project series where I present the best open source data science projects on GitHub.
If you want to see the previous projects, I have gathered them in the form of free course. They are structured by domain (computer vision projects, NLP projects, etc.) so you can focus on the project you want. And if you are new to GitHub, make sure you are enrolled in this free introductory Git and GitHub course.
Open Source Data Science Projects to Enhance Your Resume
I have divided the projects into three categories according to their domain:
- Machine learning
- Computer vision
- Other open source data science projects, including an impressive data set
Let's look at each category individually.
Open source machine learning projects
This is where you will get the machine learning terrain.. Here we will cover three useful open source projects related to machine learning.. You can choose a project based on your interests or try them all. I tried to keep them as diverse as possible so you can see a project on machine learning documents and another on creating machine learning pipelines.
If you are looking for guidance or are new to this field, I'll direct you to some helpful learning resources:
Reading research articles on machine learning is quite an overwhelming prospect for most professionals., much less for beginners. Data scientists and machine learning researchers tend to write extremely technical articles that even experts have a hard time decoding.. In reality, this is one of the biggest weak points in our field.
Therefore, any effort to break the complexity is always welcome. This useful project is a collection of articles on data science and machine learning “with illustrations, annotations and short explanations of technical keywords, terms and previous studies that facilitate the reading of the article and the main idea”.
This project was open source on GitHub last week, so it is updated regularly. At the moment, we can already see some articles there so you can review them and get an idea of how the annotations have been made. I especially love the YOLOv1 annotation:
Very cool! Go ahead and explore this and the other documents. There is much to learn!
This is quite an interesting project for anyone with a bit of data science knowledge.
NeoML is a comprehensive machine learning framework that allows us to create, train and implement machine learning models. In summary, we can create an end-to-end machine learning pipeline without the hassle of spending a lot of money on out-of-the-box solutions.
Data scientists and data engineers can use it for computer vision and natural language processing tasks (NLP), as image preprocessing, classification, document design analysis, OCR and data extraction from structured and unstructured documents.
Here is the key NeoML feature I took from their GitHub repository:
- Neural networks compatible with more than 100 types of layers
- Traditional machine learning: more of 20 algorithms (classification, regression, clustering, etc.)
- CPU and GPU support, quick inference
- ONNX support
- Languages: C ++, Java, Objective-C
- Multi platform: the same code can be run on Windows, Linux, macOS, iOS and Android
Here's a beginner's article on how to create machine learning pipelines:
Here's another project any data scientist would love, especially if you are inclined towards research. We often struggle to move from a test environment to a full-scale deployment; it is not an easy step to take (we should really appreciate the role data engineers play).
Google, of course, has a potential solution for us in the form of Caliban. This is a tool to help you start and track your numerical experiments in an isolated and reproducible computing environment.. Caliban was developed by machine learning researchers and engineers at Google.
As they say, Caliban "makes it easy to go from a simple prototype running on a workstation to thousands of experimental jobs running in the cloud". These are the highlights to be aware of:
- Develop your experimental code locally and test it within an isolated environment (Docker)
- Easily scroll over the parametersThe "parameters" are variables or criteria that are used to define, measure or evaluate a phenomenon or system. In various fields such as statistics, Computer Science and Scientific Research, Parameters are critical to establishing norms and standards that guide data analysis and interpretation. Their proper selection and handling are crucial to obtain accurate and relevant results in any study or project.... experimental
- Submit your experiments as cloud jobs, where they will run in the same sandbox
- Control and track jobs
Open source machine vision projects
I am in awe of the progress we are seeing in computer vision (No pun intended!). It seems like every month, when I sit down to write this article, I come across more and more innovative frameworks and new approaches that improve the state of the art in this field.
Organizations are scouring the world for computer vision talent right now, so it is a good time to work on these projects and get into the field. If you haven't started reading about machine vision yet, here are some useful resources:
What if I gave you a target image and asked you to write a computer vision program that would create the image from scratch? Yes, that's the power of computer vision!
This really cool open source project allows us to mimic a drawing process when a target image is provided to us. Here is a little demo of what the process looks like:
I can't wait to get my hands on this and start drawing all kinds of things.. You will need the following Python libraries to run this:
- OpenCV 3.4.1
- NumPy 1.16.2
- matplotlib 3.0.3
The developer has also given us an example so you can run it and see the magic of computer vision unfold.. I also suggest that you check out the following OpenCV articles if you haven't worked with them before:
This open source project is aimed at slightly more advanced data scientists. To understand what this project is about, we must understand the concept of single image super resolution. In simple terms, The goal here is to build a high image of resolutionThe "resolution" refers to the ability to make firm decisions and meet set goals. In personal and professional contexts, It involves defining clear goals and developing an action plan to achieve them. Resolution is critical to personal growth and success in various areas of life, as it allows you to overcome obstacles and keep your focus on what really matters.... from a corresponding low-resolution input.
Sounds like a classic computer vision project!!
PULSE is a novel solution to this problem statement. PULSE, abbreviation for Photo Upsampling via Latent Space Exploration, generates ultra-realistic, high-resolution images at incredibly high resolutions. And this is achieved in a fully self-monitored manner and is not limited to a specific degradation operator used during the trainingTraining is a systematic process designed to improve skills, physical knowledge or abilities. It is applied in various areas, like sport, Education and professional development. An effective training program includes goal planning, regular practice and evaluation of progress. Adaptation to individual needs and motivation are key factors in achieving successful and sustainable results in any discipline.....
Then, an example of how PULSE works is shown:
I encourage you to first read the research work before looking at the code. This will give you a better idea of how PULSE below works so that you can approach the code much more clearly.
Other open source data science projects
Here are a couple of open source data science projects that don't fit into the two categories above. In reality, these are two contrasting projects: one is aimed at data science beginners, while the other deals with the world of reinforcement learningReinforcement learning is an artificial intelligence technique that allows an agent to learn to make decisions by interacting with an environment. Through feedback in the form of rewards or punishments, The agent optimizes their behavior to maximize the accumulated rewards. This approach is used in a variety of applications, from video games to robotics and recommendation systems, standing out for his ability to learn complex strategies.....
Pick the one that works best for you and start exploring it.
I'm sure most of you have worked with the Iris dataset. In fact, it might even have been the first dataset you used to understand the concept of classification in machine learning. I love how easy it is to understand and explore the dataset.
But working with the same dataset can get a bit boring, especially when you're learning the ins and outs of machine learning.
This is where the PalmerPenguins dataset comes in.. This data set, open source last month, is positioned as an alternative to Iris and aims to provide a large data set for data exploration and visualization, especially for beginners. Here's a sample of the visualizations you can come up with:
The link I mentioned above contains examples of how to start exploring this data. They have even provided details on the different variables, but wouldn't you like to explore that yourself? 🙂
You can get PalmerPenguins on your machine using the following code:
# install.packages("remotes") remotes::install_github("allisonhorst/palmerpenguins")
I also recommend checking out the popular articles below on exploring and visualizing data.:
Ah, here is an open source project for all of you, reinforcement learning friends. SlimeVolleyGym is a simple gym environment for testing single and multiple agent reinforcement learning algorithms. This has been created and open source by hardmaru, a legend in the machine learning space.
This is how the game works according to him (he created the game himself in JavaScript):
The game is very simple: the agent's goal is to make the ball fall to the ground on his opponent's side, causing his opponent to lose a life. Each agent starts with five lives. The episode ends when either agent loses all five lives., or after they have passed 3000 Steps. An agent receives a reward of +1 when your opponent loses or -1 when you lose a life.
You can install limovolleygym straight from pip:
pip install slimevolleygym
Here are a couple of great tutorials from our resident reinforcement learning expert Ankit Choudhary:
Final notes
Uf, there are many projects. My objective, as always, was to keep the projects as diverse as possible so you can choose the ones that fit your data science journey. If you are a beginner, I suggest you start with the PalmerPenguins dataset, since most people don't even know right now. A great opportunity to start with an advantage.
I'd love to hear your thoughts on which open source project was most useful to you.. Or let me know if you'd like me to feature other data science projects here or in next month's issue..
