End‑to‑end data analysis curriculum from collection to visualization on real datasets. Tools: Google Sheets, SQL, Python, statistics, A/B tests, Looker Studio, Tableau.
Language of course:
Ukrainian
Format of the event:
Online
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Course overview
Description generated based on course syllabus and open data.
Junior Data Analyst: scope and approach
The curriculum covers the full data analysis lifecycle: data collection and cleaning, transformation, exploration, statistics, A/B testing, visualization, and interpretation. Work with Google Sheets/Excel, SQL and relational databases, Python (NumPy, Pandas, SciPy, Matplotlib), and dashboarding in Looker Studio and Tableau.
Tools and formats for a Junior Data Analyst
- Spreadsheets: Google Sheets/Excel for initial analysis.
- Databases and SQL: SELECT, JOIN, aggregations, window functions.
- Python: processing, visualization, basic scraping.
- BI: Looker Studio, Tableau for interactive reporting.
- Statistics: hypotheses, confidence intervals, A/B tests, product metrics.
Lecturers — practicing data analytics experts
- Yuliia Larionova — Data Analyst at MEGOGO; large‑scale data work, reporting, user behavior research.
- Viktoriia Kyrychenko — Lead Analytics Engineer at Railsware; data warehousing, dashboards and financial analytics, BigQuery, Python, Looker Studio.
Who it suits / who it doesn’t: Junior Data Analyst (entry‑level)
Suitable
- Beginners without prior commercial analytics experience.
- Adjacent roles (marketing, product, finance) working with data.
- Those aiming to systematize SQL, Python, and BI skills.
Not suitable
- Those expecting theory only without hands‑on datasets.
- Learners not ready for independent homework practice.
- Experienced analysts seeking deep machine learning topics.
Problems → outcomes in data analysis for a Junior Data Analyst
- Problem: fragmented sources and unstructured tables.
Outcome: structuring, normalization, and SQL integration queries. - Problem: misinterpreted metrics and hypotheses.
Outcome: correct metrics, statistical tests, and transparent interpretation. - Problem: static reports without insights.
Outcome: interactive Looker Studio/Tableau dashboards with filters and drill‑downs. - Problem: manual repetitive tasks.
Outcome: automated data preparation with Python.
Comparison with alternatives in data analytics
- Self‑study (articles/videos): flexible but lacks structure and solution validation.
- Academic programs: strong theory, less focus on applied BI tools.
- Short workshops: targeted skills without full analytics lifecycle.
- Structured Junior Data Analyst path here: sequential skills from spreadsheets and SQL to Python and dashboards with practice‑oriented tasks.
Learning results and competencies for a Junior Data Analyst
- Data preparation: import, cleaning, transformations, data quality control.
- Analytics SQL: selections, aggregations, segmentation, time windows.
- Python for analysis: Pandas/NumPy/SciPy/Matplotlib, basic automation.
- Statistics and A/B testing: hypothesis formulation, effect estimation.
- BI reporting: metric design, dashboards in Looker Studio and Tableau.
- Communication: clear findings, visual arguments, data quality monitoring.
Guest workshops by invited experts are included to highlight practical aspects of data‑driven decision‑making.
Course Description
LecturersYulia Larionova
- has 5+ years of experience in data analytics, for the last 4.5 years has been working as a Data Analyst at MEGOGO- at Raiffeisen Bank Aval, she built analytical reports and prepared data for further use in credit scoring models- at MEGOGO, she calculated ad-hoc analytics for developers, participated in the development of an internal system for analyzing user behavior on web, mobile, and SmartTV platforms of the media service with DAU >1.5 million- works with >15 TB of data daily and processes >100 million records- provides full-cycle analytics, conducts in-depth quantitative research of user behavior
Victoria Kirichenko
- has 6+ years of experience working with data, the last 4 of which were in Railsware- managed data collection from scratch to building a warehouse and created a team that helped all departments of the company with ad-hoc reports- in Railsware, she developed dashboards for the company's product (Mailtrap), was engaged in financial analytics and automated the P&L report- works with BigQuery, Python, Google Colab, Google Sheets, Looker Studio, Git, Terraform- is engaged in consulting, helps external clients to build high-quality analytics
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