The application process is the same for everyone, and students choose their area of focus after being accepted. Applicants are encouraged to have a close look at the curriculum beforehand and decide which course they are most interested in. However, students may switch courses during the first semester.
Each company (or even each team within a company) may have its own idea of what this job title means. As we see it, a data scientist is an expert in machine learning and statistics who also knows how to apply this knowledge.
Graduates of this course will be able to use data analysis to solve practical challenges: set requirements, define the metrics, mine the data, and offer a solution. They will also go beyond the model environment to evaluate the effectiveness of a solution under real conditions. For example, they will know how to optimize removing duplicates from search results, speed up the search for similar images, identify fraudulent ad clicks, or determine whether a new feature would add value to a service.
There are two sides to creating high-tech products (like a voice assistant or machine translation): You need enough expertise in machine learning to be able to optimize existing algorithms and invent new ones. But you also need to be able to implement your ideas and write efficient industry-grade code. This course is a good fit if you like to code and you also want to build services and applications that will be used by thousands or even millions of people.
Big Data requires robust systems for data storage and processing. Designing and maintaining such systems is a demanding and highly creative undertaking. On the one hand, you need a solid understanding of the algorithms and the system architecture and possible applications. On the other hand, you need to have deep insight into how the file system, disks, networks, and CPUs work.
This course is a perfect match if you like working with algorithms and data and you enjoy programming, but you don't want to dedicate your life to machine learning.
Data science has permeated virtually every science and field, from physics and biology to the oil and gas industry and even finance. Data scientists focused on applied tasks have to take informal observations and transform them analytically (e.g., into vectors in a Euclidean space). They also have to classify the vectors and assign attributes in order to obtain a useful multidimensional space as a result.
The only way to learn this is to study examples of successful use cases and to tackle at least one new task like this on your own. In this course, the second year is devoted entirely to independent practical research.