You will master the skills required to become a data scientist and build a portfolio of projects over the course of this 8-month, 20-hour-per-week program. Projects you will work on include:
Yandex is one of the top five tech companies in Europe. We build reliable and refined solutions, products, and services powered by machine learning. We have 1000+ developers and 30 offices in 10 countries around the world.
Fully immersing yourself in the world of tech will expose you to industry technologies and allow you to apply them in hands-on projects under the guidance of a tutor. We’ve developed our own exclusive learning environment to help you do just that.
You’ll start performing real-life tasks on day one. You’ll get information in manageable amounts and apply it by writing your own code on our interactive platform.
The interactive platform is just one element of the learning process. You will also acquire essential skills while working on independent projects using the same tools professionals use.
Our professional team of tutors will check and review your code, help you overcome obstacles, and give you the professional advice you need to succeed.
They are experienced analysts from Yandex and other prominent tech companies. Some of them transitioned from other industries before finding success. Just like you, they had to start from the ground up.
The certificate is an official document that proves you completed professional training and is only issued upon passing your final project. It shows employers that you are competent in data analysis and personally completed all the projects in your portfolio.
Pay for the full 8 months of the program with
a one-off payment of $4,000
or
Split the cost into monthly payments of $500
The process and stages of the data scientist’s work — essential terms, methods, and tools of data analysis. Data preparation. Python programming language and its Pandas library. Jupyter development environment.
Learning what it means to be a data scientist. An overview of spheres where data scientists can find work. Organizational aspects of the training process.
Performing initial scans to detect patterns in data. Building basic graphs and generating your first hypotheses.
+ 1 project for your portfolio
Probability theory, the most common distributions, and statistical methods in Python. Sampling and statistical significance. Identifying and handling anomalies.
+ 1 project for your portfolio
Identify patterns to help you determine whether a given video game will succeed or not.
+ 1 project for your portfolio
How databases are organized and how to pull data from them using SQL queries. Finding data online.
+ 1 project for your portfolio
Mastering the basics of machine learning. How the scikit-learn library works and how to use it in order to complete your very first machine learning project.
+ 1 project for your portfolio
Diving into the most highly demanded area of machine learning. Understanding how to tune machine learning models, improve metrics, and work with imbalanced data.
+ 1 project for your portfolio
Applying the acquired machine learning knowledge to business tasks. Discover business metrics, A/B testing, the Bootstrapping technique, and data labeling.
+ 1 project for your portfolio
Prepare a prototype of a machine learning model to help the company develop efficiency solutions for heavy industry.
+ 1 project for your portfolio
Taking a more in-depth look at some algorithms you’ve already learned and understanding how to apply them. Get a hands-on feel for the main concepts behind linear algebra: vectors, matrices, and linear regression.
+ 1 project for your portfolio
Pulling apart a number of algorithms that use numerical methods and applying them to practical tasks. Learning about gradient descent, gradient boosting, and neural networks.
+ 1 project for your portfolio
Exploring the time series. Understanding trends, seasonality, and feature creation.
+ 1 project for your portfolio
Applying machine learning to text data. Finding out how to convert text into numbers and how to use bag-of-words, TF-IDF, as well as embeddings and BERT.
+ 1 project for your portfolio
Learning how to handle simple computer vision tasks using premade neural networks and the Keras library. Taking a quick look at deep learning.
+ 1 project for your portfolio
Figuring out what to do when you have no target features. Handling the clustering tasks and looking for anomalies.
Apply everything you’ve learned to a two-week bootcamp that simulates the experience of working as a junior data scientist.