Data visualization and mining in Python - course RUB 21,000. from Russian Economic University named after. G.V. Plekhanov, training 5 weeks, date March 27, 2023.
Miscellaneous / / November 27, 2023
During the training, the basics of data analysis and programming in the Python environment, methods and means of entering and primary data processing will be covered. statistical means of graphical presentation of data in intelligent analysis and modeling, conducting controlled and uncontrolled classification; methods of associative, factor and cluster modeling; component analysis and decomposition of high-frequency dynamics series, neural network modeling and the basics of deep learning.
Choose a training format that is convenient for you - full-time (in the center of Moscow, in the historical buildings of the Russian University of Economics named after. G.V. Plekhanov) or remotely (from anywhere in the world).
Benefits of studying under the program
- The ability to choose a convenient learning format - online or face-to-face at the Russian University of Economics. G.V. Plekhanov.
- Opportunity to participate in master classes and specialized events of the Russian Economic University. G.V. Plekhanov and his partners.
- Availability of a discount system for corporate customers.
- Competitive advantage in the labor market with a certificate from REU. G.V. Plekhanov, the leading economic university in Russia.
- A flexible lesson schedule allows you to study even taking into account business trips and busy work.
How to proceed
Requirements for students
Persons who have or are receiving higher/secondary vocational education are allowed to complete the program
Documents for admission
A copy of a diploma of higher or secondary vocational education with an attachment or a certificate from the place of study (for students)
Passport: 1 spread (photo), 2 spread (registration)
SNILS
The program is aimed at forming and developing the user’s skills in processing, visualization and analysis of data, starting from the simplest descriptive methods statistics and ending with modern methods that have become widespread (gradient boosting, analysis of high-frequency series, neural network modeling and etc.). The program develops the basics of data analysis in the Python environment, including obtaining data via API, and studies features of intelligent analysis (“Data mining”), the place and role of these methods in the field of data analysis and machine training. The tools for data visualization (matplotlib, seaborn libraries), analysis and modeling of large data (pandas, scipy, researchpy, statsmodels libraries), formulation of a research problem in an intellectual analysis.
Statistical tools for graphical presentation of data. Libraries matplotlib, seaborn (10 hours)
Grouping and classification. Supervised and unsupervised classification (8 hours)
Associative modeling. APRIORI algorithm (10 hours)
Component analysis and factor modeling of financial and economic dynamics series (10 hours)
Cluster modeling and dynamic timeline transformation (6 hours)
Analysis of singular spectrum and local empirical modes (8 hours)
Local weighted regression. Social network analysis (8 hours)
Feedforward neural networks and convolutional neural networks. Deep learning (10 hours)