• 4.6/5

  • Advanced

A Comprehensive Collection of Open Source Data Science Projects!

“How many data science projects have you completed so far?”

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!

Course curriculum

  • 1
    Welcome to the course!
    • About the Data Science Projects Course
    • AI&ML Blackbelt Plus Program (Sponsored)
  • 2
    Machine Learning Projects
    • Machine Learning Visuals – A Brilliant Way to Communicate
    • PandaPy – Your New Favorite Python Library
  • 3
    Deep Learning Projects
    • VisualDL
    • Real-Time Audio Analysis using PyAudio
    • OpenAI’s Jukebox: A Generative Model for Music
    • Graph Neural Networks in TensorFlow 2.0
  • 4
    Computer Vision Projects
    • Facebook AI's Detectron
    • Caire - Image Resizing
    • AlphaPose
    • FastPhotoStyle
    • 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
  • 5
    Natural Language Processing (NLP) Projects
    • Open AI's GPT-3
    • NLP Paper Summaries
    • Google’s ELECTRA
    • Reformer – The Efficient Transformer in PyTorch
  • 6
    Reinforcement Learning Projects
    • DeepReinforcementLearning
    • Minigo
  • 7
    Data Engineering Projects
    • The Goodreads Machine Learning Pipeline
    • Awesome Software Engineering for Machine Learning
  • 8
    Other Data Science Projects
    • TextShot
    • 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

Who is the Top Data Science Projects Course for?

This course is for anyone who:

  • Wants to become a data scientist, data analyst, business analyst, or any other role in the data science space

  • Wants to practice and work on their existing data science skills

  • Is curious about the latest state-of-the-art projects in data science

  • Wants to enhance and improve their data science resume

FAQ

Common questions related to the Top Data Science Projects for Analytics and Data Scientists Course

  • 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!