{"id":19773067,"url":"https://github.com/shvetsihorr/excel-sheets-portfolio","last_synced_at":"2026-03-03T03:39:09.087Z","repository":{"id":256538684,"uuid":"855692351","full_name":"shvetsihorr/Excel-Sheets-Portfolio","owner":"shvetsihorr","description":"Data Analyst Portfolio - Microsoft Excel \u0026 Google Sheets","archived":false,"fork":false,"pushed_at":"2024-09-12T10:59:01.000Z","size":52,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-28T11:45:03.267Z","etag":null,"topics":["google-sheets","microsoft-excel"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/shvetsihorr.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-09-11T09:56:32.000Z","updated_at":"2024-09-14T13:17:58.000Z","dependencies_parsed_at":null,"dependency_job_id":"e07357dc-f9aa-4c9b-90ec-335e1a3b99dd","html_url":"https://github.com/shvetsihorr/Excel-Sheets-Portfolio","commit_stats":null,"previous_names":["shvetsihorr/m.excel-g.sheets-projects"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/shvetsihorr/Excel-Sheets-Portfolio","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shvetsihorr%2FExcel-Sheets-Portfolio","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shvetsihorr%2FExcel-Sheets-Portfolio/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shvetsihorr%2FExcel-Sheets-Portfolio/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shvetsihorr%2FExcel-Sheets-Portfolio/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/shvetsihorr","download_url":"https://codeload.github.com/shvetsihorr/Excel-Sheets-Portfolio/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shvetsihorr%2FExcel-Sheets-Portfolio/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30031256,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-03T03:27:35.548Z","status":"ssl_error","status_checked_at":"2026-03-03T03:27:09.213Z","response_time":61,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["google-sheets","microsoft-excel"],"created_at":"2024-11-12T05:08:32.484Z","updated_at":"2026-03-03T03:39:09.046Z","avatar_url":"https://github.com/shvetsihorr.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Data Analyst Portfolio: Excel \u0026 Google Sheets\n\nWelcome to my **Data Analyst Portfolio**, where I showcase my skills in **Excel** and **Google Sheets** through various data analysis projects. Each project demonstrates my expertise in handling real-world datasets, performing detailed analysis, and providing actionable insights.\n\nFeel free to connect with me via: LinkedIn https://www.linkedin.com/in/ihorshvets/, email: shvets.ihor@outlook.com\n\n## Table of Contents\n\n- [Projects](#projects)\n  - [Project 1: Analyzing Game Performance and Monetization Metrics](#project-1-analyzing-game-performance-and-monetization-metrics)\n  - [Project 2: User Demographics and Device Analysis](#project-2-user-demographics-and-device-analysis)\n  - [Project 3: Daily and Weekly Active Users Analysis](#project-3-daily-and-weekly-active-users-analysis)\n  - [Project 4: Forecasting Daily and Weekly Active Users (WAU/DAU)](#project-4-forecasting-daily-and-weekly-active-users-waudau)\n  - [Project 5: Cohort Analysis with Retention Rate Calculation](#project-5-cohort-analysis-with-retention-rate-calculation)\n  - [Project 6: Functions: XLOOKUP, SPLIT](#project-6-functions-xlookup-split)\n- [Skills Demonstrated](#skills-demonstrated)\n- [Tools Used](#tools-used)\n- [Key Features Used](#key-features-used)\n- [Contact](#contact)\n\n## Projects\n\n### Project 1: Analyzing Game Performance and Monetization Metrics!\n   \n[View Project](https://docs.google.com/spreadsheets/d/1F9Uwg5q6XEsSFQx10aBxMZngsFyvJbU7XD-G8UP7-DA/edit?usp=sharing)\n\nI cleaned data and calculated KPIs to improve game performance and provide insights for game managers.\n\nKey Metrics:\nTotal Revenue: For each game.\nPaid Users Count: Across all games.\nConversion Rate: Users to paid users.\nARPPU: Average revenue per paying user.\nAge Statistics: Average, median, minimum, and maximum ages of paying users.\n\nThis project provided insights for improving game monetization and user engagement.\n\n\u003cimg width=\"957\" alt=\"Analyzing Game Performance and Monetization Metrics\" src=\"https://github.com/user-attachments/assets/26736ffd-e1d0-45f1-ae04-c645b372e646\"\u003e\n\n\n### Project 2: User Demographics and Device Analysis\n\n[View Project](https://docs.google.com/spreadsheets/d/1LHg_NAnDhJs7B9ctFCGpdEKKXfptMYYg-y1Fdkvz8l8/edit?usp=sharing)\n\nAs part of my analysis of user demographics, I focused on the 'active users' dataset. By calculating key statistical measures such as mean, standard deviation, median, interquartile range, and 10th and 90th percentiles for user age, I gained valuable insights into our user base's age distribution. These findings will be instrumental in tailoring our products and marketing strategies to serve our target audience better.\n\n- **Outcome**: Provided insights into user age distribution, language preferences, and device model trends.\n\n\u003cimg width=\"1300\" alt=\"User Demographics and Device Analysis\" src=\"https://github.com/user-attachments/assets/6141e0bc-b5df-4984-97b8-815a98e29591\"\u003e\n\n\n### Project 3: Daily and Weekly Active Users Analysis\n\n[View Project](https://docs.google.com/spreadsheets/d/1XgtfhbdW503GjHmgtdPYeKhkjHSXRfmUKU4CgEMyUUA/edit?usp=sharing)\n\nI analyzed user engagement by creating and managing sheets for Daily Active Users (DAU) and Weekly Active Users (WAU), including statistical calculations and data visualizations.\n\n- Activity Month: Created a column to extract the month from each activity_date.\n- First Activity Month: Added a column to capture the first month of activity for each user, using the MINIFS function to find the earliest activity_date.\n- Activity Month Number: Calculated how many months have passed since the user's first activity month, ensuring all values are 0 or greater.\n- **DAU Sheet**: Created a sheet with unique `activity_date` values, calculated DAU using `COUNTUNIQUEIFS`, and added `week_start_date`.\n- **WAU Sheet**: Developed a sheet with unique `week_start_date` values,\n- **WAU Calculation**: Calculated WAU using the formula `=COUNTUNIQUEIFS(activity!A:A,activity!B:B,\"\u003e=\"\u0026A2,activity!B:B,\"\u003c\"\u0026A2+7)` to ensure accurate weekly user counts., and included columns for Average DAU and User Stickiness (DAU/WAU).\n- **WAU Trend Analysis**: Created a chart on the \"WAU\" sheet with weeks on the horizontal axis and WAU values on the vertical axis. Added a linear trendline to visualize trends in weekly active users over time.\n\nThis project provides a comprehensive view of user engagement, including trend analysis to support strategic decision-making.\n\n\u003cimg width=\"1368\" alt=\"Project Daily and Weekly Active Users Analysis\" src=\"https://github.com/user-attachments/assets/744f0020-ae6c-443e-b129-6c74c5858b1f\"\u003e\n\n\n### Project 4: Forecasting Daily and Weekly Active Users (WAU/DAU)\n[View Project](https://docs.google.com/spreadsheets/d/1LRXWWGK_6Y1-qU6vuvjK7WRTNRVLUpg9p6oHu-mYKIw/edit?usp=sharing)\n\nI forecasted Daily Active Users (DAU) and Weekly Active Users (WAU) for the next 20 weeks using historical data and built trend analysis.\n- Extended Week Data: Added 20 new rows for future weeks on the \"WAU\" sheet.\n- Forecasting: Used ROUND and FORECAST functions to predict DAU and WAU values for the new weeks, fixing historical data ranges for accurate forecasting.\n- Stickiness Calculation: Filled in the DAU/WAU ratio for the forecasted weeks.\n- Visualization: Created a chart showing WAU over time with a linear trendline to highlight user stickiness trends.\n\nThis project demonstrates my ability to forecast user metrics and visualize trends for strategic insights.\n\n\u003cimg width=\"1191\" alt=\"Project Forecasting DAU and WAU\" src=\"https://github.com/user-attachments/assets/dae1baab-22d5-4c4b-8ccc-cef756829c53\"\u003e\n\n### Project 5: Cohort Analysis with Retention Rate Calculation\n[View Project](https://docs.google.com/spreadsheets/d/1Z3gW2RgGniv1nwym5bCdX90N9PQBOtYxrChfdxG9KLA/edit?usp=sharing)\n\nI conducted a cohort analysis based on users' first activity month, using pivot tables for unique user counts and retention rates, with slicers for filtering.\n\n- Pivot Table: Displayed first activity month (rows), activity month number (columns), and unique users (values). I added slicers for the game, activity type, and user language.\n- Retention Rate Table: Created a dynamic table below the pivot table, calculating retention rates by dividing users in a given month by users in their first month.\n- Conditional Formatting: Applied gradient formatting to highlight key values.\n- This analysis simplifies tracking user retention over time with flexible filtering and clear visuals.\n\n  \u003cimg width=\"1272\" alt=\"Cohort Analysis with Retention Rate Calculation\" src=\"https://github.com/user-attachments/assets/95eb5908-ad6a-4274-ad39-1e6e4c9bb02e\"\u003e\n\n  ### Project 6: Functions: XLOOKUP, SPLIT\n  [View Project](https://docs.google.com/spreadsheets/d/1UwEmHPn1qVHTHPm8Y45TkmckpetqHCia8FYy4rN3QVY/edit?usp=sharing)\n\n  I split the game activity data and mapped user languages using lookup functions to enhance data organization and analysis.\n\n- Split Game and Activity Names: On the \"activity\" sheet, I separated the game_activity_name column into two parts: game and activity, using the delimiter \": \". =SPLIT(F2, \":\")\n- User Language Mapping: Added a new column for user language on the \"activity\" sheet, and populated it by referencing the \"active users\" sheet with the XLOOKUP function. =XLOOKUP(A2,'active users'!$A:$A,'active users'!$C:$C)\n\n  This improved data clarity and allowed for a more granular analysis of user activity.\n\n\u003cimg width=\"1297\" alt=\"Project XLOOKUP, SPLIT\" src=\"https://github.com/user-attachments/assets/09551b49-d775-4023-b54c-de88e0909dd9\"\u003e\n\n## Skills Demonstrated\n\nAcross these projects, I demonstrated a range of skills essential for data analysis, including:\n\n- Data Cleaning: Handling raw datasets, transforming messy data into organized, usable formats, and ensuring accuracy.\n- Statistical Analysis: Performing calculations like averages, medians, standard deviations, and percentiles to extract meaningful insights from the data.\n- KPI Calculation: Expertise in calculating critical metrics such as total revenue, conversion rates, ARPPU, and retention rates to evaluate performance.\n- Forecasting: Using functions like ROUND and FORECAST to project future user engagement and analyze trends.\n- Cohort Analysis: Implementing cohort-based retention analysis using pivot tables to track user behavior and trends over time.\n- Advanced Functions: Utilizing functions like XLOOKUP and SPLIT to map user data and organize activity logs for deeper analysis.\n- Data Visualization: Creating charts and trendlines to communicate data insights clearly, aiding in decision-making and strategic planning.\n- Google Sheets Automation: Efficient use of Google Sheets functions and tools for dynamic analysis and streamlined data handling.\n\n## Tools Used:\n- Microsoft Excel: For data cleaning, statistical analysis, KPI calculation, and financial tracking using advanced formulas and visualizations.\n- Google Sheets: For organizing data, performing complex calculations, creating dynamic tables, and automating workflows with functions like XLOOKUP, SPLIT, and FORECAST.\n\n### Key Features Used:\n- Pivot Tables: To summarize and analyze large datasets efficiently, particularly for cohort analysis and tracking key metrics.\n- Google Sheets Charts: For visualizing trends and patterns in user engagement, monetization, and forecasted data over time.\n- Conditional Formatting: to apply formatting (such as colors and font styles) to cells based on their values or other criteria. This helps in visually analyzing data by highlighting trends, outliers, and important metrics.\n\n## Contact\nFeel free to connect with me via: \n - LinkedIn https://www.linkedin.com/in/ihorshvets/,\n - Email: shvets.ihor@outlook.com\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshvetsihorr%2Fexcel-sheets-portfolio","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshvetsihorr%2Fexcel-sheets-portfolio","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshvetsihorr%2Fexcel-sheets-portfolio/lists"}