Applied Statistics and Data Science
There are many data science jobs available in the job market right now. With the exponential growth of information, data scientists are becoming indispensable to companies in all industries. In the coming years, the field of data science will develop dynamically, and the search for interesting projects and work will become competitive, and employers will become more demanding of the competencies of applicants.
The basis of modern data analysis is applied statistics. Applied statistics allows one to apply advanced methods of mathematical statistics and process statistical data to analyze various areas of society using computer data processing. The technical knowledge and skills that enhance this direction are provided by Data Science, or Data Science, as a field that combines sections of computer science related to data: collection, processing, analysis and making effective decisions. Combining the two directions makes it possible to analyze large volumes of unstructured data – that complex information about modern society that becomes available to the researcher thanks to new information technologies. That’s why our track is called 'Applied Statistics and Data Science.'
«More than anything, what data scientists do is make discoveries while swimming in data. It’s their preferred method of navigating the world around them. At ease in the digital realm, they are able to bring structure to large quantities of formless data and make analysis possible. They identify rich data sources, join them with other, potentially incomplete data sources, and clean the resulting set. In a competitive landscape where challenges keep changing and data never stops flowing, data scientists help decision-makers shift from ad hoc analysis to an ongoing conversation with data.
What kind of person does all this? What abilities make a data scientist successful? Think of him or her as a hybrid of data hacker, analyst, communicator, and trusted adviser. The combination is extremely powerful—and rare.
If “sexy” means having rare qualities that are much in demand, data scientists are already there. They are difficult and expensive to hire and, given the very competitive market for their services, difficult to retain. There simply aren’t a lot of people with their combination of scientific background and computational and analytical skills».
Davenport Thomas H., Patil DJ Data Scientist: The Sexiest Job of the 21st Century. Meet the people who can coax treasure out of messy, unstructured data // Harvard Business Review — October, 2012.
Data scientists are constantly searching for new solutions and hypotheses for business. Data analysts require not only the ability to present data systematically, but also a creative approach to visually displaying information, a desire and willingness to look deep into the problem, find the questions underlying it, and formulate them into a testable set of hypotheses. It is important to remember that data analysis is primarily about research. You can explore data at different levels: automate the analysis process, formulate many hypotheses and test them using various methods. Data analysts in modern business help analyze key metrics, solve operational problems and achieve strategic goals.
A data analyst must understand the development trends of artificial intelligence (including Gen AI), data analytics patterns and strategies, and not only be able to program, but also have a deep understanding of the product being created, as well as a good understanding of statistics. In 2012, Thomas H. Davenport and colleagues published the article 'Data Scientist: Sexiest Job of the 21st Century,' and in 2022 – its continuation entitled 'Is Data Scientist Still the Sexiest Job of the 21st Century?', in which they noted, that it is important not only to be able to build models, but also to load the necessary data into them, as well as manage the operation of systems based on the goals that the business sets.
Companies and organizations of all sizes in different industries have a demand for specialists who can manage data flows and find valuable information in them. Specialists in this field must have a good knowledge of statistics and have knowledge in the relevant subject area. Knowledge of statistical methods is enhanced by skills from computer science.
The request for specialists in the field of data analysis is relevant not only for companies, but is also naturally included in the national strategic agenda for technological development (national projects 'Data Economy' and 'Artificial Intelligence'). In particular, it is the national project 'Data Economy' that confirms and updates the demand for specialists in the field of statistics and data analysis. It is important to be able to plan the economic development of individual industries, regions and cities, as well as to effectively and proactively structure the work of any organization to achieve results as quickly as possible.
«These developments mean that coding, which was perhaps the single most common job requirement when we wrote a decade ago, is somewhat less essential in data science. It has migrated to other jobs or is being increasingly automated. (Data cleaning is a notable exception to this trend, however.) The key focus of the job continues to shift towards predictive modeling and the ability to translate business issues and requirements into models. These are collaborative activities, but unfortunately there are as yet no great tools for structuring and supporting collaborative data science activities».
Davenport Thomas H., Patil DJ Is Data Scientist Still the Sexiest Job of the 21st Century? // Harvard Business Review – July 15, 2022.
The main components for the Applied Statistics and Data Science track are training in working with data and learning advanced statistical methods. It is important to understand how to extract information from different sources, as well as how to further work with this information, obtaining statistically accurate and reliable results that can be used in making business decisions. Study of a variety of advanced statistical methods of data analysis (Contemporary Methods of Data Analysis, Bayesian Statistics, Stochastic Models, Time Series, Network Analysis and many others) and computer methods of data processing and analysis (Data Mining, Machine Learning, Programming in R and Python, Unstructured Data Analysis and others) will be completed with the implementation of applied projects. Having mastered modern methods of data analysis, you can solve problems in the most efficient way, processing data arrays in software products that require different levels of user participation. Students of the program will become familiar with programs, packages and databases for the full cycle of working with data: R, Python, SAS, STATA, Orange, Pajek, Gephi, and others. Students will learn to work with algorithms that can receive, process data, calculate and make decisions. Knowledge of mathematical statistics, skills in testing hypotheses and estimating unknown parameters are complemented by a deep understanding of how current research is conducted in business, including using artificial intelligence technologies.
The 'Applied Statistics and Data Science' track is suitable for students who want to develop in the field of data analysis – a current and in-demand area in any subject area. Basic education can be anything: the track will be of interest to students with both an education in the social sciences and an education in the exact sciences. On the one hand, students on the track will be able to systematize and deepen their knowledge in the field of social sciences, and on the other hand, master data research skills in order to perform data analytics tasks and more effectively manage teams of data analysts and data scientists.
Studying on this track will allow students to acquire the necessary knowledge and skills for employment in various companies and corporations. Graduates of the 'Applied Statistics and Data Science' track can work as data analysts or product analysts in various fields, solving both research problems and applied problems in managing products and processes within their organizations. If desired, track graduates will be able to continue working in an academic environment by enrolling in PhD programmes or graduate school.