Profession “Data Analyst” - course 65,412 rubles. from Moscow Digital School, training 4 months, Date: December 15, 2023.
Miscellaneous / / December 08, 2023
This profession is ideal for remote work, including freelancing. Customers are willing to pay well to those who can visualize business performance, justify their decisions with numbers and make forecasts.
The data-driven approach is gaining momentum. The profession is in demand in many areas: IT, finance, marketing, trade, medicine, education, services, etc.
With us, you will become an analyst in just 4 months, because there will be a lot of practice and feedback from experts. The best students will have the opportunity to get an internship at Ozon, Data Sfera, Agima.
Who is it suitable for?
For beginners
Is your work experience as far away from analytics as possible? Do you want to learn something new and interesting, or master an in-demand profession? We start from 0 and consistently structure your knowledge of analytics and reinforce it with practice after each lecture.
Managers
Learn to quickly process and analyze data to build forecasts, strategies and plans. You will become more autonomous when making decisions.
Marketers
You will understand how to use data to evaluate advertising effectiveness, formulate and test hypotheses using A/B testing, and learn how to quickly create clear reports on advertising campaigns.
Business and entrepreneurs
For sellers who want to learn how to analyze sales data on marketplaces. And also for all entrepreneurs who want to find growth points and optimize business resources using a data-driven approach.
Module 1. Introductory.
Tasks and types of analysts
Data Analytics
Analytics skills
Module 2. Excel.
Why Excel Analytics?
Filtering and sorting data
Methods for entering a function
Formula editing and error tracking system
Useful functions in Excel
Pivot tables: definition, construction conditions, setting up areas
Grouping data
Chart types
Sparklines
Power Query
Add-on “Search for a solution”. Connection and settings
Dropdown lists
OLAP cube
Macros
Module 3. SQL.
History of the language, SQL standard, basic concepts
SELECT Statement Structure
Basic Data Types
Logical operators
Aggregate functions and grouping
Subqueries
Joining tables
Creating, updating and deleting tables
Common Table Expressions, Views
Window functions
Typical Application Scenarios
Introduction to Query Optimization
Module 4. BI and dashboards.
Introducing the capabilities of the Power BI tool.
Power Query
Preparing data for analysis.
Working with the advanced editor: M language.
Principles of building a data model, organizing tables, managing relationships
DAX Data Analysis Language
Loading Linked Data Sources
Directories and automatically calculated directories for loaded queries
Common examples of advanced DAX calculations
Formation of measures
Calculated columns and data groupings
Dashboard for company key performance indicators
Render block in Power BI Desktop
Dashboard design and application of themes
Working in PowerBI from the browser
Infrastructure, data loading and dashboard development in Tableau. Tableau: main features and comparison with Power BI
Module 5. Python for data analytics.
Introducing Jupyter Notebook and markdown
Simple operations with numbers and strings
Conditional statement and for loop
Functions
Introduction to Pandas
Simple visualizations
Data outlier analysis
Loading and modifying data
Online store traffic analysis
Sales funnel, bar charts, conversion analysis
Introduction to mathematical statistics.
Descriptive Statistics
Introduction to Statistical Hypothesis Testing
A/A and A/B testing
Data merging
Cohort analysis
Comparison of traffic quality from two advertising sources
Module 6. Final project.
Build a management dashboard with key business indicators
Try yourself as a data analyst for the Brazilian marketplace Olist. Discuss the business process of the marketplace and the data model. Learn to work with a database on a remote server. Explore approaches to solving business problems