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
Program
1
- 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.
2
- 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.
3
- Product analytics as the foundation of working with data.
- Product analytics methodologies.
- Product. Product types.
- Monetization.
- Product subsystems.
- User journey. Product funnel.
4
- Marketing, product, and financial metrics.
- Product subsystems and their metrics.
- Hierarchy of metrics.
- Mapping metrics to the product funnel.
- RFM analysis.
- Cohort analysis.
5
- Selecting metrics for testing.
- Selecting data.
- Calculator. A/B tests: statistics and mathematics.
- A/B tests: problems and solutions.
6
- 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.
7
- Interface overview.
- Data types, file types.
- Basic terminology.
- Data loading.
- Basic calculations.
8
- Working with filters.
- Chart types.
- Visualization. Building dashboards.
9
- Process of adding/removing events.
- Audit and monitoring of metrics.
- Growth hypotheses along the funnel.
- Running experiments in product and marketing.
- Evaluating experiment results and finding insights.
- Building a systematic experimentation process.
10
Reviews
I took the “Data Analyst” course from SkillsUp, taught by Maxim Fedorov, and was very satisfied! The course gave me a clear understanding of the role of a data analyst and how data work is organized within a company. I had previously been introduced to various analytics tools, but didn’t know how to apply them correctly to solve real-world business problems.
The course curriculum was very well-structured: we covered analytics systems, product analytics, metrics, cohort and RFM analysis, A/B testing, data handling, and learned how to use Tableau. The combination of theory and hands-on exercises was particularly valuable—it helped me understand how to use data for product development.
After the course, I felt more confident working with data and understood how to take a systematic approach to analytics in real-world projects. I recommend this course to anyone who wants to become a true data analyst!