A working laptop/desktop with 4 GB RAM
A working Internet connection
Knowledge of Python/R will come in handy
This is a very common question interviewers ask in data science interviews. We have conducted hundreds of these interviews for both data analyst and data scientist roles and this is quite often the jackpot question. This is especially true if you’re a fresher or a relative newcomer to data science.
Just doing courses or attaining certifications isn’t good enough. Almost everyone we know holds certifications in various aspects of data science. It adds no value to your resume if you don’t combine it with practical experience.
And that’s where open-source data science projects play such a key role!
A collection of open source data science projects, ranging from fields like Computer Vision, NLP, Machine Learning and even Data Engineering Projectas
From beginners to advanced data science folks, there are data science projects for professionals of all levels here
Interviewers love applicants who come up with Projects and their solutions which shows curiosity, passion, and enthusiasm for the field
Adding data science projects to your resume will prop up your chances of getting hired.
- About the Data Science Projects Course
- Machine Learning Visuals – A Brilliant Way to Communicate
- PandaPy – Your New Favorite Python Library
- Real-Time Audio Analysis using PyAudio
- OpenAI’s Jukebox: A Generative Model for Music
- Graph Neural Networks in TensorFlow 2.0
- Facebook AI's Detectron
- Caire - Image Resizing
- Facebook AI’s DEtection TRansformer (DETR)
- Real-Time Image Animation
- Convert Any Image into a 3D Photo
- Transform an Image into a Cartoon Illustration
- One-Shot Multi-Object Tracking
- GAN Compression
- StyleGAN2 – A New State-of-the-Art GAN!
- Real-Time Person Removal using TensorFlow.js
- Computer Vision Basics in Microsoft Excel
- Open AI's GPT-3
- NLP Paper Summaries
- Google’s ELECTRA
- Reformer – The Efficient Transformer in PyTorch
- The Goodreads Machine Learning Pipeline
- Awesome Software Engineering for Machine Learning
- ShyNet – Privacy-Friendly and Cookie-Free Web Analytics
- Coronavirus Time Series Data
- Google Brain AutoML
- ggbump – Data Visualization in R!
- Google Earth Engine – 300+ Jupyter Notebooks to Analyze Geospatial Data
- AVA – Automated Visual Analytics
I have decent programming experience but no background in data science or machine learning. Is this course right for me?
It would be helpful if you have a working knowledge of at least the basic machine learning algorithms. While this course caters to both beginners and advanced users in data science, your existing knowledge will help you approach these projects with a clearer understanding of how to solve the problems.
We suggest taking the ‘Introduction to Data Science’ or ‘Applied Machine Learning’ courses first before tackling these projects if you don’t have any prior ML knowledge.
What is the fee for the course?
This course is free of cost.
How much effort do I need to put in for this course?
The idea behind this course is to expand and hone your existing data science skills. The more effort and time you put into these projects, the more you’ll learn. Take at least an hour or two every day and work on the projects that you are interested in.
I’ve completed this course and have a good grasp on Top Data Science Projects for Analytics and Data Scientists. What should I learn next?
Practice! Think of this as a never-ending journey - if you complete one project, take up another that caters to your domain or interests. The more your practice, the better you’ll become as a data science professional.
We also recommend writing about these projects. Once you finish one project, write down your approach, your learning, etc. and put that in a blog article. This will help you solidify what you learned and also help the community when they work on the same project. It’s a win-win for everyone!
Can I download the projects in this course?
Yes, all the projects are open source and we have provided the links to download them in the course itself. So help yourself and happy learning!