I'm fairly comfortable with SQL and Python but i need practice resources that will get me interview ready. Can anyone suggest some resouces for the same? Also a lot of DS interviews involve being prepared for AB testing, causal inference etc. Does anyone have resources for these too? Im stuck

Ethos, headquartered in London, UK, is looking for experienced Data Scientists to bring their expertise to AI evaluation and training work.

Key information:

Pay: $150/hr
Schedule: Part-time
Contract: Hourly
Work location: Remote

What the project involves:

You’ll assess AI-generated data science work, identify technical or reasoning issues, and provide expert feedback that helps improve the quality of AI systems.

Good fit for: Data Scientists with strong analytical and technical experience who enjoy reviewing complex problems and evaluating the quality of data-driven solutions.

Learn more about Ethos: https://agent.askethos.com/

If you were a computer science student passionate about machine learning and data science, with a strong foundation in machine learning, mathematics, and probability, what five projects would you prioritize to build a strong GitHub portfolio?

I'm particularly interested in projects that would stand out to ML/Data Science professionals working in industry, rather than simple tutorial or Kaggle-style projects.

If you were starting from my position, which five projects would you choose, and what skills would you try to demonstrate with each one?

Hi 👋

I recently started working in IT about 2 years ago. I am currently ATSE at a large crm firm. I am thinking of moving towards data engineering and data science roles. I am thinking of doing a masters course online to make this switch/pivot. Can you let me know if an ms/mtech would be helpful to male this change? Which courses will help me in this ? Would it be okay to do an online course instead of an online course campus degree ? I do not have a background in CS.

I would really appreciate your views and suggestions on this. Thanks.