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Oleksandr Salnikov

Offer Oleksandr work on your next project.

Ukraine Kyiv, Ukraine
2 months 13 days back
Available for hire available for hire
age 38 years
on the service 8 months 7 days

Rating

Successful projects
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Rating
184
Python
1221 place out of 4457
Databases & SQL
320 place out of 1203

Language proficiency level

Українська Українська: fluent
Русский Русский: fluent
English English: intermediate

Skills and abilities

Portfolio


  • Statistical funnel, correlation

    Data Processing
    The goal was to identify the number of users at each stage (from registration to the first purchase), calculate correlations of each step to the initial one, and create clear visual maps for every stage. Result: A user activity funnel, stage-by-stage visual maps, and charts with calculations were built. It was revealed that 6.8% of users reached the purchase stage, while 19–23% on average proceeded to the trial start.
  • Cohort Analysis, user_activity

    Data Processing
    Cohort and segmentation analysis was conducted to evaluate the number of users on a monthly basis. The average monthly revenue per unique user was calculated, and the most productive month was identified. Data was extracted, key metrics (MRR, ARPPU, LTV, churn) were calculated, and reports were prepared. Result: Analytical insights with visualizations were provided, which allowed for optimizing the allocation of advertising expenses during specific time periods.
  • Average days on delivery

    Data Processing
    In this project, we needed to find the number of days by states on the map, the average delivery time for each delivery method, and the number of orders for each day during the 2020–2023 period of the dataset.
    Skills: Data Visualization (Tableau) · Data Analysis · Data Aggregation & Grouping · Geospatial Analysis · Statistical Analysis · Data Visualization · Exploratory Data Analysis (EDA) · Data Aggregation
  • Average Time Spent by Unique Age Groups of Players

    Data Processing
    In this project, it was necessary to calculate the number of unique players added from month to month, as well as to divide them into age groups with a 5-year step and calculate the total time spent (in hours and minutes) for each age group.