Igor Nikolaev Email and Phone Number

- Connected to PostgreSQL database.- Built two data schemes (on joins and relationships) with a live connection for performance research.- Created a dashboard with charts showing sales dynamics, total and via regions structure in a chosen month.- Recorded dashboard performance.- Published dashboard and performance recording workbook on Tableau Server.Tools: DBeaver, Tableau Desktop, Tableau Server.

- Based on wireframes and general requirements created 3 dashboards with complicated logic.- Developed an optimal data model;- Extracted data from a source .xlsx table and converted it into a form acceptable for visualization;- Made charts with drill-down Year-Quarter-Month capability;- Implemented a custom slicer, dynamic legends for charts, bookmarks and pop-ups for better user interactivity;- Built dashboards with diagrams of different types.Tools: MS Power BI.

In the process of training, the following skills have been obtained:- data extraction, preprocessing, aggregation and loading;- exploratory (EDA), marketing and product analysis;- users behavior analysis and aha-moment detection;- A/B-tests preparation and results analysis by statistical methods;- data pipelines automation and data visualization;- predictive model development and clustering using basic ML algorithms.Tech stack: Python, Pandas, NumPy, Matplotlib, Seaborn, Plotly, Dash, SciPy, Scikit-learn, PostgreSQL, Tableau, Ubuntu, ClickHouse, Yandex.Cloud, Jupyter Notebook.My projects on Github https://github.com/NI-coder/data_analytics_projects: 1. Online store marketing costs optimization. Evaluating the advertising sources effectiveness based on revenue and customers acquisition costs ratio.Results: discovered unprofitable advertising channels, which are recommended to be abandoned in favor of more efficient sources of traffic attraction.2. Testing hypotheses for increasing revenue in an online store. A/B-test results evaluation.Results: detected stable statistically significant differences in conversion rate and average order value between groups A and B.3. Mobile app users segmentation. Users behavior analysis and clustering them based on user event logs.Results: A) Users are divided into three segments. B) Aha-moment was found that describes the user behavior performing the target action.

- customer requirements business analysis; - analysis of competitors;- road map and technical specifications drawing;- test cases writing and testing online store functionality;- unit economics metrics improvement;- web analytics;- development team management.Achievements:- Increased website loading speed by 8 times.- Fully automated the process of ordering, paying and delivering goods.- Increased the revenue of the online store 7 times in 5 months.

Support of leasing transactions.

- Leasing project management.- Expertise of projects: analysis of enterprises financial and economic activities , managerial, technological and other risks.