Wanna become a professional data scientist?
Build the perfect portfolio and rock that job interview!
To land your first data scientist job, you don’t need yet another online course.
Employers don’t want to know about the languages you are familiar with and the courses you’ve done. They want to hear that you KNOW what you’re talking about.
How do you achieve that?
By understanding the problem and convincing your future employers you can SOLVE it.
And you won’t be able to truly understand and solve any problem until you go through the ups and downs of a real-world project.
I help you choose, define, and build a project you will be proud of.
So when the time comes, you STAND OUT from the crowd.
Why Choose Me?
Because I’ve already walked the path. I build projects for a living.
- Six years ago, I felt lost and didn’t know where to start.
- I depended on feedback and was lucky to have great senior colleagues.
- Thanks to their help and my persistence, I entered Toptal and landed my first freelance job.
- Since then, I have worked for promising startups from Dubai, the USA, Spain, and France.
- I started earning a lot more and working fewer hours.
- I know HOW to turn your confusion into exceptionally well-paid projects.
What do others say?
What Do I Offer?
Based on your background and interests, I help you choose the right project, guide you through everything you need to know to make a good start, and then give you feedback and support all along the way.
Let’s say you want to join Uber to improve their forecasting models. Here are the steps we go through:
- We define the problem.
- We agree on the final solution.
- We find the relevant data for the problem.
- We discuss the architecture and the technologies to build the solution.
- I support you all along the way because you WILL have a lot of questions.
- You put in the hard work and DON’T GIVE UP.
- We make the solution publicly available and fully documented.
- We prepare for the interview.
- I write you a public recommendation.
- We celebrate the win.
These are some of the topics we cover along the way:
- SQL: There is no data scientist without data. And to query and extract the data for your projects you need to master SQL. Without it, you will be slow and dependent on data engineers.
- Python: The main programming language in data science and ML, thanks to its vast ecosystem of open-source libraries.
- Machine Learning (ML): ML is about building software from data. It is used to automate and improve operations and business decisions.
- Cloud services: Most companies have their infrastructure on the cloud (e.g. AWS, Google Cloud, or Azure). It is important you feel comfortable working in a cloud environment and building solutions that integrate with cloud services.
- Deep Learning libraries: If you wanna dive deep into computer vision or natural language processing, you need to understand neural networks, how to train, and how to fine-tune them.
- Presentation and visualization: a data scientist is an "interface" between business stakeholders and data engineers. As such, you need to talk and present information in an actionable way, focusing on its business impact.