Profession Data Analyst - course RUB 168,840. from SkillFactory, training 10 months, Date August 14, 2023.

Google Sheets for Data Analysis Trainer
3 weeks, 7 hours/week
Mastery of tables is a basic competency of an analyst. And solving complex problems without reinventing the wheel is a skill:

• 6 modules aimed at advanced users
• Data Analysis and Visualization
• 240 exercises
• Collections of external materials
• Opportunity to ask questions to experts

Trainer "Databases and SQL"
3 weeks, 7 hours/week
According to the analysis, 84% of analyst vacancies with 1 to 3 years of experience require SQL knowledge:

• 6 modules ordered by difficulty
• 240 exercises
• Collections of external tools for additional practice
• Opportunity to ask questions about training to experts

Python for Data Analysis Trainer
8 weeks, 7 hours/week
Scripting language is required in 83% of vacancies for employees with 1 to 3 years of experience. In the near future, mastering Python will become a blocker for growth in the following areas:

• 16 modules, from introduction to programming to working with APIs
• 480 exercises
• Collections of external materials
• Opportunity to ask questions to practitioners

Course "Statistics for Analysts"
6 weeks, 7 hours/week.
Mathematical statistics is the third most popular requirement for analytical candidates:

• 12 modules, from basic concepts of probability theory to multiple regressions
• 400 exercises
• Opportunity to ask questions to experts

Course “Building reports in BI systems”
Data visualization and collection of dashboards is a necessary skill for analysts:

• Install and configure Power BI
• Connecting data sources
• Presentation of results using visualization

Specialization to choose from

Option 1: Product analytics
In product specialization, you will understand the basic metrics of a product, gain an understanding of what data needs to be collected and where to collect it store, learn to structure information, build graphs, test hypotheses and obtain valuable business insights based on data.

• Product Thinking: 3 weeks
• Customer analytics: 5 weeks
• A/B testing: 6 weeks
• Data-driven culture: 2 weeks

Option 2: Marketing Analytics
In the marketing specialization, you will learn how to set up end-to-end analytics, understand the relationships between different traffic sources, conduct cohort and RFM analysis, and compile simple and understandable reports and dashboards, build hypotheses, run statistically based A/B testing and draw intelligent conclusions using mathematical apparatus.

• Segmentation and personalization of target audience: 2 weeks
• Cohort and RFM analysis: 2 weeks
• Working with databases: 2 weeks
• Setting up end-to-end analytics: 2 weeks
• External data sources: 2 weeks
• Data Analysis Tools: 2 weeks
• A/B tests - statistics and mathematics: 2 weeks
• A/B tests - problems with A/B testing and their solutions: 2 weeks