Data turning you on? Get the sexiest job of the 21st century.

What you will learn

System tools and GIT control system

System tools are a collection of useful techniques for operating your system with commands. Every self-respecting Data Scientist should know how to operate the console and we will teach you how to do it.

You will also learn how to use the GIT version control system that allows smooth collaboration in a team.

Python in Data Science

Python is the most popular language in Data Science. It has the largest number of libraries and frameworks that perform many Data Science tasks.

In this module, you will learn the Python basics required for creating application code.

Statistics and probability

Data Science is based entirely on mathematics. This module will help you understand the operations performed with the Numpy library, where you can calculate basic operations from linear algebra (operations on vectors/matrices).

In the second part, you will learn about probability – for example, why tests with a 90% success rate need to be repeated.

Processing of data sets

Data Science is data, data, and more data. Data come from different sources and in a variety of formats. They say that 90% of a Data Scientist’s job is data processing. It’s hard to disagree with that. The skills acquired in this module will be ones that you are going to use most frequently in your future professional life.

You will learn how to process data downloaded by an API or from an SQL database.

Data visualization

One chart can say more than many words on a given topic. The ability to present data in an appropriate way is valuable in the context of working with customers that buy by eye.

In this module, you will learn how to create interactive charts necessary for the work of Data Science.

Machine Learning in practice

Predicting stock market prices?

Choosing the right moment to buy an apartment, taking into account historical prices?

What does the future hold if we know the past?

Regression tries to answer these questions. It enables the prediction of unknown values ​​of one quantity from the known values ​​of others. In addition, this module will introduce the entire subject of classic Machine Learning methods – the ones on which many modern solutions are based. You will also learn about classification problems in supervised learning and what unsupervised learning is.

TensorFlow library

You will learn about the most popular framework for creating network solutions. It enables easy implementation of given network architecture, its learning process, and subsequent actions.

Neural networks

Artificial neural networks are structures that mimic the structures found in our brains. We have an input signal that gets processed differently by different neurons that generate some information at the end of the process. It is one of the most popular ML methods, so you need to know it very well.

They allow you to solve the problem of regression and classification, but also many others. We will teach you methods to learn such networks with the help of TensorFlow.

Image processing – Computer Vision

How do autonomous vehicles work?

How do they recognize road signs or pedestrians? In this module, you will learn how to process image data.

How do you filter the noise? What information can be extracted from images?

Moreover, you will learn how to use special structures of neural networks used to process image data. By understanding transfer learning, you will be able to create solutions that can explain most image processing problems and more.

Natural Language Processing (NLP)

Alexa and Siri are two examples of voice assistants. How do they understand what we say to them?

You will learn how to deal with textual data in this module (and learn the answer to this question).

Working with sequences – recursive neural networks

How can neural networks consider sequences and not a single object? How does the algorithm know which word should follow in a sentence?

We will try to answer these questions using recursive neural networks.

Practical projects

During the course, you will complete four final projects. Three of them are hands-on projects (Regression, Classification, and Image Processing) carried out at different stages of the course. And at the end, you will be tasked with one large final project.

An example of a final project could be a Covid-19 prediction project using publicly available data.