SkillsUp - Data Analyst

Data Analyst

Course Goal

To teach analysts the basic skills of working with data so that the tools they acquire help improve specific project metrics and contribute to successfully solving business tasks.

This Course is Designed For

The program is intended for those who want to dive into analytics from scratch, learn to think abstractly, formulate hypotheses, identify patterns, and draw logical conclusions based on conducted analysis.
Marketers, developers, PMs, and specialists in related fields who want to enhance their analytical skills.

Learning Format

Course duration: 13 sessions × 7 weeks
Homework after each lecture and personalized feedback from the trainer
Access to video recordings and materials in Google Classroom

What does a graduate of the online course receive

🔗 View benefits

The “recorded version” of the course was created in Russian before the war began. The course presentations are in English.

Program of the course "Data Analyst"

The role of a Data Analyst in the analytics system and the structure of working with data.

Evolution of data-driven companies.
The value of analytics (descriptive/predictive analytics).
Organization of the data processing workflow (data science).
Roles and tools for data processing. The place of a data analyst in the data processing workflow.
Main tasks and responsibilities of a Data Analyst.
Principles of data analytics work.
Core skills. Main tools of a Data Analyst.
The classic definition of the data analyst role.
Where to start and development paths.
Basic terminology.

Analytics systems. Marketing, product, and UX analytics.

Types of analytical tasks and corresponding analytics systems. The AAARRR funnel.
Marketing analytics systems and the tasks they solve.
End-to-end advertising analytics.
Product analytics systems and the tasks they solve.
Overview of analytics system types: from advertising to deep product analytics.
User analytics.
Optimal set of analytics tools for mobile and web products.
Main stages of applying analytics.

Product analytics. Product funnel. Product subsystems.

Product analytics as the foundation of working with data.
Product analytics methodologies.
Product. Product types.
Monetization.
Product subsystems.
User journey. Product funnel.

Metrics. Cohort and RFM analysis.

Marketing, product, and financial metrics.
Product subsystems and their metrics.
Hierarchy of metrics.
Mapping metrics to the product funnel.
RFM analysis.
Cohort analysis.

A/B testing.

Selecting metrics for testing.
Selecting data.
Calculator. A/B tests: statistics and mathematics.
A/B tests: problems and solutions.

Working with data.

Working with databases. Tools.
What data to collect. Where to store it.
Extracting information for processing.
Data requirements.
Data processing: completeness, integrity, presence of noise, errors, outliers, gaps.
Data validation.
BI systems.