R language for data analysis - course 50,000 rub. from Otus, training 4 months, date August 31, 2023.

I have been developing algorithmic trading systems since 2016. Before that, he worked in IT consulting and did research for the largest Russian and international companies (Aeroflot, InterRAO, Central Bank, IMF, BNP Paribas investment partners, Monetary Authority of Singapore). Engaged in science and did research in scientific laboratories (Prognoz Risk Lab, Laboratory of Cryptoeconomics and Blockchain Systems). 15 years of work experience with R, organizer of the first R group in Russia, author of popular libraries for using R in finance (rusquant, tcsinvest), regular lecturer at the R in Finance conference (Chicago, USA). Since 2011 co-organizer of the international conference Perm Winter School. Professional teacher. Since 2016, he has given lecture courses on more than 10 disciplines at various universities (Higher School of Economics, Perm State National Research University). Candidate of Economic Sciences.

I have been involved in media analytics since 2011. I've been using R since 2014. I am part of the team of a communications agency that provides a range of communications services from PR support and press service work to a comprehensive analysis of the information field. Our primary client base consists of non-profit and government organizations. I use R in my work to automate processes, analyze texts (storytelling, topic modeling, entity recognition), analyze survey results and develop questionnaires. Created an interactive dashboard on shiny, combining data from the API of monitoring systems and the results of expert analysis. Main stack: R, Python, SPSS, git Teacher

Language Basics
-Topic 1.Practical application
-Topic 2. Basic data types, vectors and simple operations on them
-Topic 3. Vectorization as the basis of everything
-Topic 4.Lists and their special cases - dataframes
-Topic 5. Control structures
-Topic 6.Functions
Receiving data
-Topic 7.Flat table files and xlsx (x)
-Topic 8.JSON/XML and receiving data over the network
-Topic 9. Obtaining data from the database
-Topic 10.Object storage and data lakes – arrow, parquet, orc
-Topic 11.Recording results
Data processing
-Topic 12. Tidy data, processing with dplyr/tidyr
-Topic 13.Additional processing using tidyverse (lubridate, forcats, stringr)
-Topic 14. Functional approach to transformation with the purrr package
-Topic 15. A powerful and concise alternative – data.table
-Topic 16.Other options (dtplyr, dbplyr, collapse)
-Topic 17. A little about parallel computing
Data visualization
-Topic 18.Basic graphics in R
-Topic 19.ggplot2 – basic tools (various geoms)
-Theme 20.ggplot2 – additional features (faceting, changing scales, changing the coordinate system)
-Topic 21. Interactive graphics – plotly, dygraphs, echartr
-Topic 22. Visualization of tables using gt
Presentation of analysis results
-Theme 23.Rmarkdown and quarto
-Topic 24.Shiny – basics of developing analytical applications (UI + Server)
-Topic 25.Shiny – application reactivity and user activity tracking
Fundamentals of Statistical and Machine Learning
-Theme 26. Tidymodels package
-Theme 27.mlr3 package
-Theme 28. Package h2o
Development and delivery
-Topic 29.Testing, error handling and logging
-Topic 30.Running code – cron
-Topic 31.Running code – docker
-Topic 32.Running code – gitlab ci
Project work
-Topic 33. Selection of topic and organization of project work
-Topic 34. Consultation on projects and homework
-Topic 35.Protection of design work. Summing up the course