Data Science for managers - course 60,000 rub. from HSE, training 2 days, Date: June 17, 2023.

Miscellaneous / by admin / November 30, 2023

Additional professional education programs are practice-oriented and allow you to develop in a shorter period of time (from several weeks to two years) acquire a new profession, acquire current professional and managerial competencies or expand your knowledge in a particular subject areas.

The following are allowed to master additional professional programs:

Persons with secondary vocational and (or) higher education;
Persons receiving secondary vocational and (or) higher education.

Learning Objectives

1 Move to a new level of professional development

2 Meet the rapidly changing demands of the market and social environment

3 Become a successful business manager

4 Satisfy educational needs in various fields of economics, science, culture and art

Further education programs

Advanced training programs
Increasing professional level within the framework of existing qualifications and (or) improving and (or) obtaining new competence necessary for professional activities

From 16 academic hours
Certificate of advanced training

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For persons who have (or are completing) higher or secondary vocational education

Professional retraining programs
To obtain the competencies necessary to perform a new type of professional activity

From 250 academic hours
Diploma of professional retraining, with the right to conduct new professional activities
For persons who have (or are completing) higher or secondary vocational education

Professional retraining programs to obtain additional qualifications

For persons who have or are receiving higher or secondary vocational education and at least 3 years of work experience in a managerial position

Professional retraining programs to obtain additional qualifications in the field of management “Master of Business Administration” (MBA - Master of Business Administration)", including for senior managers (EMBA - Executive Master of Business Administration)

From 2040 academic hours
Diploma of professional retraining, with additional qualification “Master of Business Administration”

Professional retraining programs to obtain additional qualifications in a specific professional field "Master in...", including for senior managers (Executive Master in…)

From 2040 academic hours
Diploma of professional retraining, with additional qualifications

Doctor of... programs

Professional retraining programs to obtain additional qualifications for professional degrees, in particular Doctor business administration (DBA - Doctor of Business Administration), Doctor of Law (Doctor of Law), Doctor of Education (Doctor of Education) and others degrees

From 2040 academic hours
Diploma of professional retraining, awarding a professional degree
For persons with higher professional education and at least 5 years of work experience in a managerial position

Master's position: Expert of the Center for Continuing Education, Faculty of Computer Science.

Started working at the Higher School of Economics in 2017. He teaches courses on machine learning in marketing and introduction to data science. Professional interests: machine learning in Bioinformatics bioinformatics data analysis in biology Education 2018 Master's degree: National Research University "Higher School of Economics", specialty "Applied Mathematics and Informatics" 2015 Bachelor's degree: National Research University "Higher School" Economics", specialty "Applied Mathematics and Computer Science" Professional experience 2020 - present: Lead Data Scientist, X5 Retail Group 2019 - 2020: Head of Big Data Team, Azbuka Vkusa 2019 - 2019: senior manager for big data analysis, X5 Retail Group 2018 - present: teacher at the Center for Continuing Education, Faculty of Computer Science 2017 - present: guest lecturer at the Department of Big Data and Information Retrieval 2016 - 2016: junior analyst, project manager, IIDF 2014 - 2015: junior Product Manager, Alfa-Bank.

Position: Senior Lecturer, Faculty of Computer Science, Department of Big Data and Information Retrieval.

Graduated from the Faculty of Computational Mathematics and Cybernetics of Moscow State University in 2013. Started working at the Higher School of Economics in 2016. He teaches courses on Introduction to Data Analytics, Introduction to Machine Learning, and Applied Data Science.

Deputy Head of Department, Senior Lecturer, Faculty of Computer Science, Department of Big Data and Information Retrieval; Project Manager, Academic Supervisor, Faculty of Computer Science, Center for Continuing Education; Head of Laboratory, Faculty of Computer Science, Department of Big Data and Information Retrieval, Research Laboratory for Data Analysis in Financial Technologies; Academic director of the educational program "Applied Mathematics and Computer Science".

Professional interests: data analysis, machine learning, analysis and automatic text processing Education 2013 Specialty: Moscow State University. M.V. Lomonosov, specialty "Applied mathematics and computer science" Professional experience Worked in the companies Bioclinicum, Forecsys, Ozone. Since 2014 he has been working at Yandex. Since 2016, he has been working at the Faculty of Computer Science at the National Research University Higher School of Economics, where he teaches courses in the “Intellectual” minor. data analysis”, developed and teaches a course on machine learning in the program “Applied Mathematics and Informatics". Since 2019 - academic director of the “Applied Mathematics and Informatics” program. Awards and achievements Best teacher – 2019, 2018, 2017

Analytical Templates

The main objective of the course is to examine techniques for constructing a conceptual model of an application based on the use of templates, as well as the possibility of reusing developments made within the framework of object-oriented analysis. The course provides practical guidance on how to build a UML representation of a conceptual model and how to further transform the conceptual model into a design.

4,2

Data Science with Gleb Mikhailov

🏆Winner of Stepik Awards 2022 in the category “Breakthrough of the Year”🏆 Practical video course on the basics of data science. No mathematics, no theory, only examples of solving real problems using pandas and CatBoost. An introductory part of the course on python and pandas for data analysis is available for free!🔥

Python Libraries for Data Science

The course introduces students to the basic concepts of Data Science. We will look at basic algorithms (linear models, decision trees, KNN, compositions), and analyze data preparation (cleaning, generation of new features and their selection). The knowledge gained will be enough to solve a wide range of problems.

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