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Oksana Liesnikova

Offer Oksana work on your next project.

Ukraine Podolsk, Ukraine
9 months 22 days back
Available for hire available for hire
on the service 10 months

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Databases & SQL
504 place out of 1209
Data Processing
543 place out of 1486

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Programming

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Portfolio


  • Tableau project "User funnel"

    Databases & SQL
    Tableau project "User funnel" Lesnikova Oksana
    Tableau project "User funnel" Lesnikova Oksana
    Jan 2025 – Feb 2025 Jan 2025 – Feb 2025
    Emblem GoIT - start your career in IT
    Related to GoIT - start your career in IT
    Related to GoIT - start your career in IT
    The task for the project was to create a visualization that would display the user funnel, the number of new registered users per month, and the average payout by day.
    The task for the project was to create a visualization that would display the user funnel, the number of new registered users per month, and the average payout by day.
    Skills: SQL · Tableau · Data Analysis · Intellectual · Soft Skills
  • Tableau Project "Cohort performance"

    Databases & SQL
    The project objective was to create a visualization that would help analyze revenue trends, track customer retention, revenue patterns, and cohort performance.
    The project objective was to create a visualization that would help analyze revenue trends, track customer retention, revenue patterns, and cohort performance.
    Skills: SQL · Tableau · Financial Analysis · Data Analysis · Intellectual · Soft Skills
  • Sales and Profit Analysis in Power BI

    Databases & SQL
    As part of a business intelligence case study, I created a multi-page interactive report using Power BI to help a global distributor identify key drivers of profitability and optimize sales strategies.

    Dataset: Superstore (sales data across multiple years, regions, categories)

    Key components:

    Financial Overview Dashboard: Total Sales, Profit, Average Order Value, Top Products & Regions

    Monthly Trends & Year-over-Year (YoY) Analysis: Time series and seasonal patterns with dynamic filters

    Discount Impact Analysis: Effect of discount levels on profitability and recommendations

    Category & Regional Analysis: Profit and sales distribution across product categories and global regions

    Drillthrough View: Detailed order breakdown by filters (region, discount, etc.)

    Tools & Skills:
    Power BI Desktop, DAX, data modeling (star schema), calculated measures, slicers, drillthrough, date tables

    The project helped uncover:

    Most profitable categories and customer segments

    Losses caused by excessive discounting

    Regional patterns in sales and margin performance

    Full report includes Power BI Desktop file, published report in Power BI Service, and a presentation in PowerPoint.
  • Sales Performance Project

    Databases & SQL
    Sales Dashboard in Tableau
    As part of a data analysis project, I built an interactive sales dashboard using Tableau to explore key trends in product sales and customer behavior. The dataset included order-level data with product, pricing, quantity, customer, and category information.

    Main dashboard insights:
    Monthly sales by category – time-series analysis to observe seasonality and category trends

    Top 10 customers by revenue – horizontal bar chart highlighting top spenders

    Quantity sold by product – helps identify best-selling items

    Average price by product category – for pricing and margin analysis

    Sales by day of the week – uncovers weekly patterns in demand

    Detailed monthly sales table – with quantity, revenue, and categories for easy reference

    Tools used:

    Tableau Public

    Calculated fields

    Aggregations (SUM, AVG)

    Filters and sorting

    Custom date formatting

    The dashboard combines six different visualizations in one interactive view to support decision-making for retail business analysis.
    Sales Dashboard in Tableau As part of a data analysis project, I built an interactive sales dashboard using Tableau to explore key trends in product sales and customer behavior. The dataset included order-level data with product, pricing, quantity, customer, and category information. Main dashboard insights: Monthly sales by category – time-series analysis to observe seasonality and category trends. Top 10 customers by revenue – horizontal bar chart highlighting top spenders. Quantity sold by product – helps identify best-selling items. Average price by product category – for pricing and margin analysis Sales by day of the week – uncovers weekly patterns in demand Detailed monthly sales table – with quantity, revenue, and categories for easy reference.
    Tools used: Tableau Public Calculated fields Aggregations (SUM, AVG) Filters and sorting Custom date formatting.
    The dashboard combines six different visualizations in one interactive view to support decision-making for retail business analysis.

    Skills: Data Analysis · Tableau · Analytical Skills · English · Business Intelligence (BI) · Intellectual
  • Revenue_Metrics_Dashboard

    Databases & SQL
    The main idea is to create a dashboard that allows you to track key monetization metrics in real time, helping the business team quickly assess monetary performance and user behavior in the game. This tool helps you identify why revenue is falling, which users are leaving the game, and where there is potential for growth.
    The main functionality is:
    • display of key metrics (MRR, ARPPU, Churn Rate, LTV)
    • revenue dynamics graphs
    • filters by date, language, game, and age
    • analysis of new/lost users, expansion/reduction of MRR
    For implementation, I used SQL in BigQuery and Tableau Public for visualization.
    The main idea is to create a dashboard that allows you to track key monetization metrics in real time, helping the business team quickly assess monetary performance and user behavior in the game. This tool helps you identify why revenue is falling, which users are leaving the game, and where there is potential for growth. The main functionality is: • display of key metrics (MRR, ARPPU, Churn Rate, LTV) • revenue dynamics graphs • filters by date, language, game, and age • analysis of new/lost users, expansion/reduction of MRR For implementation, I used SQL in BigQuery and Tableau Public for visualization.
    Skills: Analytical Skills · Creative Problem Solving · SQL · Tableau · Data Analysis · Intellectual · Soft Skills