Data Analyst simulator course

Simulator Program

1. Product metrics

   An analyst's work is aimed at improving the product, user experience, and optimizing company resources - for this you need a deep understanding of how to "translate" the product into the language of numbers and metrics. That's why in the first module we study in detail:

   • Introduction to product metrics
   • Engagement Metrics
   • Product Metrics
   • Engagement metrics
   • Additional metrics

   Let's reinforce the studied material with practice and calculation tasks:

   • Building a marketing model
   • Retention calculation
   • Calculation of lifetime
   • Calculation of MAU/WAU/DAU

   Additionally, consider interesting topics (e.g., false correlation) using blitz testing.

2. SQL for data analysis

   The SQL language is an analyst's primary tool. It is most often asked in interviews and used in work. Module Schedule:

   • Chapter 1: "Introduction to Databases"
   • Chapter 2: "Database Fundamentals"
   • Chapter 3: "First Steps in SQL"
   • Chapter 4: "How I Know How to Google."
   • Chapter 5: "JOIN: Joining tables is the most important operation in RDBMS"
   • Chapter 6: "GROUP BY: calculating aggregate metrics and descriptive statistics"
   • Chapter 7: "Subqueries, CTEs, correlated subqueries, and nested logic"
   • Chapter 8: "Window functions: the most powerful tool for problem solving"
   • Chapter 9: "Code Style: How to write beautiful code in SQL"
   • Chapter 10: "DDL and DML: Not SELECT alone"
   • SQL module final test
   • SQL module final project

   150+ hands-on assignments await you, as well as 3 final projects to choose from.

   Examples of practical assignments: ABC/XYZ analysis, cohort analysis of rolling retention, segmentation of customer base, balance distribution by percentiles, SCD, turning a long table into a wide table (pivot table), analytics of bank transactions.

   Final Project Topics:

   • RFM analysis of the pharmacy network's customer base
   • Increased retention of IT Resume platform
   • Student activity analytics of a corporate client
3. Data visualization: Metabase

   Building graphs and dashboards is another common task of an analyst. And Metabase synergizes perfectly with data visualization and SQL: you can write queries and build charts at once. In this module we look at:

   • Getting to know Metabase
   • How to log into Metabase
   • Chapter 1: "Visualizing our SQL queries"
   • Homework analysis
   • Chapter 2: "Working with Questions"
   • Chapter 3: "Filter Fields"
   • Chapter 4: "Building Dashboards."
   • Additional chips
   • Final work

   As a final project, you will have to build accompanying visualizations for the project you did in the module on SQL. In addition, you will need to build a complete dashboard.

4. Git / Github

   To work in a team, you need to know how to work with the Git version control system. In this module, we'll cover how to use all the basic commands, resolve merge conflicts, and do pull-requests.

   • Chapter 1: Introduction
   • Chapter 2: Working with the local repository
   • Chapter 3: Working with a remote repository
   • Chapter 4: Working with branches
   • Chapter 5: Working with History
   • Chapter 6: Relocation and merger
   • Chapter 7: Working with Foreign Repositories
   • Chapter 8: Conclusion
   • Chapter 9: Synopsis
   • Chapter 10: Final Test
   • Chapter 11: Case Study
   • Chapter 12: Case Study Analysis

   At the end of the module you will have to do a project where you will act as a developer in a large team and you will need to design a pull request into one of the libraries used.

5. Basic Python

   The Python language is one of the most powerful tools in an analyst's arsenal; no serious task can be done without it. Therefore, we will consider all aspects of Python programming in detail:

   • Chapter 1: "Introduction."
   • Chapter 2: "Variables. Basic Data Types"
   • Chapter 3: "Complex Data Types."
   • Chapter 4: "Additional Data Types"
   • Chapter 5: "Functions."
   • Chapter 6: "Terms and Conditions."
   • Chapter 7: "For and While Loops"
   • Chapter 8: "Modules and Libraries"
   • Chapter 9: "Basic File Management"
   • Chapter 10: "Intermediate Business Cases"
   • Chapter 11: "Lambda Functions and Functional Programming"
   • Chapter 12: "Comprehensions"
   • Chapter 13: "Iterable objects, iterators, generators, generator expressions"
   • Chapter 14: "Intermediate Business Cases"
   • Chapter 15: "Exceptions."
   • Chapter 16: "The * Operator"
   • Chapter 17: "Working with date and time."
   • Chapter 18: "Intermediate Business Cases"
   • Chapter 19: "Regular Expressions"
   • Chapter 20: "Working with Databases through Python"
   • Chapter 21: "Requests Module"
   • Chapter 22: "Final Project."

   You will have to solve more than 200 tasks, as well as more than 20 business cases. And at the end you will have a big final project.

   Examples of practical tasks: calculate MAU/DAU/WAU/retention metrics, automate check processing in a chain of stores from different cash registers, analyze the activity of platform users, set up integration with Google Sheets.

   Final project: Build an ETL pipelines to receive data via corporate client API and load it into the database. Additionally, it is necessary to set up notifications to mail about successful processing.

6. OOP in Python

   Object-oriented programming is an approach that allows you to write usable and scalable code. We will use real examples to show where it can be useful for an analyst.

   • Chapter 1: "The Basics of OOP."
   • Chapter 2: "Encapsulation."
   • Chapter 3: "Inheritance."
   • Chapter 4: "Polymorphism, abstract classes and methods"
   • Chapter 5: "Additional techniques - property, staticmethod, getters-setters, decorators"
   • Chapter 6: "Magical Methods, Singleton."
   • Chapter 7: "Final Business Cases: OOP"

   You will have to solve 20+ tasks and also write your own class-connector to the database using Singleton template, which you will use for many years in all your projects, remembering us with a kind word.

7. Python for data analysis

   The main task of an analyst is to collect data and extract useful hypotheses from it by studying it from different angles. For this purpose, in this module we analyze libraries:

   • Numpy
   • Pandas
   • Matplotlib
   • Seaborn

   Full module program:

   Numpy submodule

   • Chapter 1: "Introduction."
   • Chapter 2: "Basic Array Operations"
   • Chapter 3: "Core Functions"
   • Chapter 4: "Statistics"
   • Chapter 5: "Downloading and Saving Data"
   • Chapter 6: "Linear Algebra."
   • Chapter 7: "Logic Functions."
   • Chapter 8: "Random variables"
   • Chapter 9: "A Practical Case Study"

   Pandas submodule

   • Chapter 1: "Introduction to Pandas"
   • Chapter 2: "Loading and Processing Data"
   • Chapter 3: "Exploring the Dataset - Conducting Exploratory Data Analysis (EDA)"
   • Chapter 4: "Metrics Calculations".
   • Chapter 5: "Additional Methods"
   • Chapter 6: "Practice Blitz Test."
   • Chapter 7: "A Practical Case Study"
   • Chapter 8: "A few life hacks from Andron"

   Visualization in Python sub-module

   • Chapter 1: "Matplotlib: The Basics"
   • Chapter 2: "Matplotlib: Chart Tuning"
   • Chapter 3: "Matplotlib: The rest of the visualizations"
   • Chapter 4: "Matplotlib: Graph Matching"
   • Chapter 5: "Seaborn: The Basics."
   • Chapter 6: "Seaborn: Advanced Visualization."
   • Chapter 7: "Plotly: Interactive Graphics"
   • Chapter 8: "Practical Work."
   • Chapter 9: "Dissecting the practical work"
   • Chapter 10: "Helpful Hints from Andron."

   Final project

   • Final Project - Test in Samokat
   • Final Project - Disassembly

   Examples of practical assignments: ABC/XYZ assortment matrix analysis, product mix analysis, EDA exploratory analysis.

   Final work: a big test assignment to the Samokat company.

8. Automation and Deployment

   Many tasks in an analyst's job are routine and can be automated. However, to do this, you also need to be able to deploy and configure a server, set up the environment, install all the necessary programs, and much more. Module Schedule:

   • Chapter 1: Introduction
   • Chapter 2: Creating and Configuring a Server
   • Chapter 3: Project Development
   • Chapter 4: Deploys
   • Chapter 5: Automation
   • Chapter 6: Configuring the Web Server and Metabase
   • Chapter 7: Windows Scheduler
   • Final project

   At the end of the module, you will have to fully configure the server, as well as automate the script that will daily fetch data via API from a third-party server and put it into a database that you also deployed on the server.

9. Data visualization: Power BI

   Partner of the module - "BI is easy" platform

   Power BI is one of the most powerful visualization systems used in most large companies. It allows you to build flexible dashboards and customize end-to-end analytics.

   We will break down the entire cycle of working with Power BI, DAX, Power Query, and dashboards:

   • Chapter 1: Introduction
   • Chapter 2: Installing Power BI
   • Chapter 3: Getting to Know Power BI
   • Chapter 4: The first dashboard in Power BI
   • Chapter 5: Power Query
   • Chapter 6: Quick Start in DAX
   • Chapter 7: An Important Beginning - Modeling, Variables, and Beyond
   • Chapter 8: Iterating DAX X-Functions
   • Chapter 9: Mini-Practice 1 - Creating a Universal Calendar
   • Chapter 10: Practice Problem
   • Chapter 11: Mini-Practice 2 - Iterative X-functions
   • Chapter 12: Case Study
   • Chapter 13: Filter Functions in DAX
   • Chapter 14: Pre-aggregation. Creating internal tables in DAX
   • Chapter 15: Mini-Practice 3 - Working with non-contiguous tables
   • Chapter 16: Case Study
   • Chapter 17: DAX Contexts
   • Chapter 18: CALCULATE function and filter context management
   • Chapter 19: Converting the Filter Context
   • Chapter 20: Working with dates in DAX
   • Chapter 21: Mini-Practice 4 - CALCULATE Function and Working with Dates
   • Chapter 22: Case Study
   • Chapter 23: Working with Table Relationships in DAX
   • Chapter 24: Mini-Practice 5 - Linked Tables
   • Chapter 25: Practice Problem
   • Chapter 26: Mini-Practice 6 - Multiple Datasheets
   • Chapter 27: Practice Problem
   • Chapter 28: Mini-Practice 7 - Condition Functions
   • Chapter 29: Case Study
   • Chapter 30: Working with Power BI Service
   • Chapter 31: DAX technical tricks for building smart and interactive visualizations in reports
   • Chapter 32: Static and Dynamic ABC Analysis in DAX
   • Chapter 33: Final Project
   • Chapter 34: DAX and Power BI Masterclasses

   At the end of the module, you will have to build a full-fledged commercial dashboard of several pages with complex measures and visualizations - for example, a check-up dashboard of an executive or a visualization of financial statements.

10. Mathematics and Statistics

    Analytics is about hypothesis testing, A/B testing and calculations. And for this you need a strong mathematical foundation. Therefore, in this module, we will cover complex university topics in as simple a language as possible:

    • Lesson 1: Introduction to Statistics
    • Lesson 2: Implementing Samples in Python
    • Solving the problem from Lesson 2: Implementing Samples in Python
    • Lesson 3: Types of Variables
    • Lesson 4: Basic Concepts from Mathematical Analysis
    • Lesson 5: Basic concepts of probability theory
    • Lesson 6: Discrete Distributions
    • Lesson 7: Continuous Distributions
    • Lesson 8: Useful statistical functions in Python
    • Lesson 9: Descriptive Statistics. Part 1
    • Lesson 10: Descriptive Statistics. Numerical characteristics of a distribution
    • Lesson 11: Calculating Statistical Characteristics in Python
    • Lesson 12: Descriptive Statistics Project
    • Lesson 13: Normal Distribution
    • Lesson 14: The Law of Large Numbers and CPT
    • Parsing the problem from Lesson 14
    • Lesson 15: Parametric point methods for estimating distribution parameters
    • Analyzing the assignments from Lesson 15
    • Lesson 16: Properties of Statistical Estimates
    • Analyzing the assignments from Lesson 16
    • Lesson 17: Confidence Intervals
    • Lesson 18: Quantitative Representativeness
    • Lesson 19: Statistical Hypothesis Testing
    • Lesson 20: Hypothesis testing: the concept of p-value
    • Lesson 21: Hypothesis testing for a single sample
    • Parsing the problem from Lesson 21
    • Lesson 22: Testing parametric hypotheses for two samples
    • Parsing the problem from Lesson 22
    • Lesson 23: Parametric criteria for two or more independent samples
    • Parsing the problem from Lesson 23
    • Lesson 24: Checking Data for Normality
    • Lesson 25: Transforming Data: Normalizing to a Normal Distribution
    • Lesson 26: Lesson 26: Nonparametric criteria for mean and variance for two or more independent samples
    • Parsing the problem from Lesson 26
    • Lesson 27: Nonparametric Criteria for Homogeneity of Independent Samples
    • Lesson 28: Criteria for dependent samples
    • Parsing the problem from Lesson 28
    • Lesson 29: How to Determine Data Dependencies
    • Parsing the problem from Lesson 29
    • Lesson 30: Analyzing Qualitative Data
    • Parsing the problem from Lesson 30
    • Final project on statistics

    At the end of the module you will have to analyze a commercial data set, formulate a set of hypotheses and, armed with mathematical formulas, test them.

11. A/B testing

    In this module, we will talk in detail about A/B tests, sampling, and more. We will talk about confidence intervals, statistical tests, sampling:

    • Chapter 1: What are experiments
    • Chapter 2: Prioritization of hypotheses
    • Chapter 3: A/B Test Design
    • Chapter 4: Conducting the AV test
    • Chapter 5: Data preparation
    • Chapter 6: Analyzing the data obtained
    • Chapter 7: Presentation of results

    As a hands-on activity, you will design, run, and analyze an A/B test to update the list of ads from partners on Marketpele Feed, a platform that helps publishers make money from advertising.

12. Thesis

    The diploma thesis is the final stage of the simulation. You will have to combine all your acquired knowledge together and make a big final project, which will be a living proof that you have not just acquired knowledge and skills on some training tasks, but that you can actually apply them in practice, implement and customize analytics from scratch in the company, and generate high-quality hypotheses and business conclusions.

    You will have to work with sales information from a large online marketplace (Ozone format). You will need to fully customize analytics for this company: set up and automate data collection, configure the server, install BI systems, build dashboards, conduct analytics and generate valuable business insights.

13. Master Classes

    On a monthly basis, we host cool masterclasses from practitioners. You will have access to recordings of old masterclasses as well as the opportunity to attend new ones.

    Examples of topics:

    • Analysis of a test assignment for an analyst position at OZON
    • Parsing of SQL problems of increased complexity
    • LTV cohort analysis with SQL (with conclusions)
    • ABC-analysis of assortment in Pandas (with conclusions)