“Python: Introduction to Data Analysis” - course RUB 30,000. from MSU, training 4 weeks. (1 month), Date: November 30, 2023.
Miscellaneous / / December 03, 2023
The advanced training program is aimed at gaining skills in working with the Python programming language for big data analysis.
Duration of training – 36 hours (24 hours of classroom lessons with a teacher, 12 hours of independent study of materials).
Form of study – face-to-face with the possibility of remote connection.
Cost of education 30,000 rubles.
Start of classes - autumn 2023 academic year.
Training agreements are concluded with individuals and legal entities.
Registration for courses is carried out by e-mail [email protected] (for individuals).
You can contact the course administrator, Anton Martyanov, to register or with questions via WhatsApp or Telegram at +79264827721.
1. Python programming language libraries.
Main purposes and functions of libraries;
Types of libraries for data analysis: Pandas, Numpy, Statsmodels, Sklearn, Seabourne;
Types of libraries for data visualization;
2. Types and data structures in Python.
Types of data types: Integer, float, bool, srting, object;
Types of data structures: Dataframe, series, array, tuples, lists, etc.;
3. Loading data into the program and preliminary analysis.
Loading data in different formats (xlsx, csv, html, etc.);
Determining the number of rows and columns;
Identifying missing values;
Identifying data types in a matrix;
4. Python functions for data analysis.
Functions for obtaining descriptive statistics (finding max, min, mean, median, quartiles);
Functions for visualizing the density of data distribution (Normal Gaussian distribution);
Functions for creating binary variables (dummies var);
Functions of machine learning algorithms for building models (least squares, support vector machines, random forest, logistic regression, time series);
5. Construction of regression models.
The purpose of constructing linear regressions using the least squares method;
Proposing hypotheses and setting a problem (based on working data);
Building a regression model in Python;
Assessment of the significance of the obtained coefficients and the model as a whole (t-statistics, F-statistics);
Model quality assessment (R2);
Checking Gauss-Markov assumptions;
Interpretation of the results obtained;
6. Construction of classification models.
Random Forest algorithm;
Logistic regression;
Support Vector Machine;
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