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
- 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.
- 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 as the foundation of working with data.
- Product analytics methodologies.
- Product. Product types.
- Monetization.
- Product subsystems.
- User journey. Product funnel.
- Marketing, product, and financial metrics.
- Product subsystems and their metrics.
- Hierarchy of metrics.
- Mapping metrics to the product funnel.
- RFM analysis.
- Cohort analysis.
- Selecting metrics for testing.
- Selecting data.
- Calculator. A/B tests: statistics and mathematics.
- A/B tests: problems and solutions.
- 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.
- Interface overview.
- Data types, file types.
- Basic terminology.
- Data loading.
- Basic calculations.
- Working with filters.
- Chart types.
- Visualization. Building dashboards.
- 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.
FAQ
The program is designed for beginners who want to dive into analytics from scratch, as well as for marketers, programmers and PMs who want to improve their analytical skills. You will learn to think abstractly, formulate hypotheses, find patterns and draw conclusions to solve business problems.
You will learn how to work with databases, validate and clean information from noise. The program covers product analytics techniques, working with financial and marketing metrics, cohort and RFM analysis, as well as A/B testing. A separate block is devoted to the Tableau visualization tool.
The online course is designed for 7 weeks and consists of 13 intensive classes. After each lecture, practical homework is provided with feedback from the trainer. All videos and learning materials will be conveniently organized for you in Google Classroom.
You will receive a robust set of tools that will help improve specific project metrics and influence successful solutions to real business challenges. To start your journey in Data analytics, just click the "Apply" button on this page.
