School of Analytics

Курс "Школа аналитики" (School of Analytics) появился по инициативе команды "Школы финансов ЭФ МГУ", ведущей дискуссионной площадки по финансам.

Годовой цикл School of Analytics состоит ­из еженедельных мероприятий от дата- и бизнес-аналитиков,­ включает обсуждение теоретических и практических аспектов тем по статистическому анализу, процессам структурирования и управления базами данных, бизнес-моделированию, а также командный и индивидуальный ситуационный разбор кейсов­.

Структура курса:

1. Введение в дата-аналитику (Introduction to Data Analytics)

  • What is data analytics?
  • A day in life of a data analyst
  • Key terms, definitions and wordings used in the industry
  • Key technical tools used globally, brief overview and their local substitutes (AirFlow, Amplitude, Python, SQL, Tableau, PowerBI, in-house solutions, etc)
  • Overview of the research process
  • Process of communication with the team, peer departments, clients, etc
  • Process of creating technical request

2. Управление базами данных в PowerPivot, SQL и AirFlow (Database Management with PowerPivot, SQL and AirFlow)

  • Data import
  • Database optimization
  • SQL compilations
  • Syntax, functions, SQL tabs
  • Typical mistakes
  • Nested queries, joins, table relationship
  • AirFlow technical overview and basic functionality

3. Описательная аналитика (Descriptive Analytics)

  • Describing and summarizing data
  • Data visualization (Tableau, PowerBI and local substitutes)
  • PowerPoint timesavers
  • Exploratory Data Analysis. Visualisation in Python (matplotlib, seaborn, plotly)

4. Прогнозная и предписывающая аналитика (Predictive and Prescriptive Analytics)

  • Statistical tools
  • Hypothesis testing, incl. A/B, A/A, etc.
  • Sampling and estimation

5. Анализ ключевых метрик (Key Metrics and Analysis)

  • Marketing, product, financial, operational and other metrics
  • Metrics tree analysis

6. Анализ юнит-экономики (Unit Economics)

7. Анализ данных в Python (Data Analysis and Programming with Python)

8. Продвинутые методы анализа данных (Solving Data Problems and Advanced Data Toolkit)

  • How to structure work with lack of understanding what to do?
  • Situations with lack of data, usage of alternative tests, etc
  • Process of data request from development, layout of front and back-up events
  • Procedural work with DWH (Data Warehousing) and ETL (Extract, Transform, Load)

9. Разбор примеров и ситуационный анализ (Case Studying and Business Application)

  • Customer
  • Operations
  • HR
  • Financial
  • Market
  • Product
  • Problem solving and consulting method
  • Factor analysis

Руководитель и координатор курса: Ахмедзянов Руслан Германович


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