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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