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“Modeling and quantitative methods of analysis in business” - course 32,000 rubles. from MSU, training 4 weeks. (1 month), Date: November 29, 2023.
Miscellaneous / / December 01, 2023
Mastering the course is associated with studying the theoretical foundations of statistics, probability theory and obtaining comprehensive knowledge on the practical use of information processing and analysis methods in business - environment.
Studying the course allows you to use the acquired knowledge in practice when processing primary data, presenting the results obtained in the form of tables, graphs, diagrams, constructing generalizing indicators.
On their basis, it is possible to use the most effective statistical and quantitative methods and models in economic analysis, including the construction of distributions, quantitative methods for assessing probabilities, methods for making decisions under conditions of uncertainty, methods for constructing confidence intervals, methods for constructing and evaluating statistical hypotheses.
The course is conducted in two versions: basic and advanced. The volume of classes in hours is the same.
The basic program involves classes and studying materials together with master's students of the faculty. The extended program is a separate group within the framework of advanced training.
Category of listeners – heads of companies and departments, employees of corporate venture funds, specialists in the field R&D, project and product managers, innovation and change managers, analytical staff departments
Start of classes - autumn 2023.
Duration – 72 hours (32 hours of classroom lessons with a teacher, 40 hours of independent study of materials).
Form of study – full-time and part-time.
Cost of education - 32,000 rubles.
Training agreements are concluded with individuals and legal entities.
Registration for courses is carried out by email [email protected], through the registration form on the website.
You can contact the course administrator, Anton Martyanov, to register or with questions via WhatsApp or Telegram at +79264827721.
Doctor of Technical Sciences Position: Professor of the Higher School of Management and Innovation of M.V. Lomonosov Moscow State University
Topic 1. Methods of personal data analysis
Histograms, scatterplots, time series, pivot tables, summary metrics, box plots, pairwise correlation matrix.
Topic 2. Quantitative methods of probability theory and mathematical statistics
Probability theory. Basic rules of probability theory. Discrete and continuous random variables. Expectation and variance. Derived probability distributions. Normal, binomial distributions. Multi-step decision-making procedures under conditions of uncertainty. Evaluation of Strategies (EMV). Decision tree and its software implementation (TreePlan).
Math statistics. The main task of mathematical statistics. The concept of statistical estimates and their properties. Estimation of confidence intervals. General plan for analyzing situations under conditions of uncertainty. Controlling the length of the confidence interval. Typical statistical problems. Testing statistical hypotheses.
Extended Course Program
Topic 1. Preparing data for statistical analysis
General methods of data monitoring and preprocessing (identifying gaps, duplicates, anomalies, violations of input data formalization requirements, etc.). Demonstration of automation of the process of data preprocessing and consolidation. Methods for constructing statistical samples (simple random sampling method, systematic method, stratification method, cluster approach, multi-stage sampling methods).
Topic 2. Methods of statistical data analysis
Correlation analysis. Factor analysis. Discriminant analysis. Conjoint analysis.
Topic 3. Regression Analysis Methods
Least square method. Selection of independent factors. Selecting a function class. Paired and multiple regression. Methods for assessing the significance of regression coefficients. Assessing the accuracy of the regression model. Statistical tests of model adequacy. Methods for linearization of regression analysis problems. Working with non-numeric data (dummy variable method).
Topic 4. Data Mining Methods
Analytical reporting and multidimensional data presentation. Data store. Measurements and facts. Basic operations on a data cube. Construction of automated data analysis models. Types of problems solved by Data Mining methods: classification, clustering, regression, association, search for consistent patterns. The most widely used algorithms for each type of problem are: self-organizing maps, decision trees, linear regression, neural networks, associative rules. Methods for visualizing research results.
Address
119991, Moscow, st. Leninskie Gory, 1, bldg. 51, 5th floor, room 544 (Dean's office)
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