https://github.com/shvetsihorr/excel-sheets-portfolio
Data Analyst Portfolio - Microsoft Excel & Google Sheets
https://github.com/shvetsihorr/excel-sheets-portfolio
google-sheets microsoft-excel
Last synced: 2 months ago
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Data Analyst Portfolio - Microsoft Excel & Google Sheets
- Host: GitHub
- URL: https://github.com/shvetsihorr/excel-sheets-portfolio
- Owner: shvetsihorr
- Created: 2024-09-11T09:56:32.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-09-12T10:59:01.000Z (8 months ago)
- Last Synced: 2025-01-11T01:12:04.460Z (4 months ago)
- Topics: google-sheets, microsoft-excel
- Homepage:
- Size: 50.8 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Data Analyst Portfolio: Excel & Google Sheets
Welcome 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.
Feel free to connect with me via: LinkedIn https://www.linkedin.com/in/ihorshvets/, email: [email protected]
## Table of Contents
- [Projects](#projects)
- [Project 1: Analyzing Game Performance and Monetization Metrics](#project-1-analyzing-game-performance-and-monetization-metrics)
- [Project 2: User Demographics and Device Analysis](#project-2-user-demographics-and-device-analysis)
- [Project 3: Daily and Weekly Active Users Analysis](#project-3-daily-and-weekly-active-users-analysis)
- [Project 4: Forecasting Daily and Weekly Active Users (WAU/DAU)](#project-4-forecasting-daily-and-weekly-active-users-waudau)
- [Project 5: Cohort Analysis with Retention Rate Calculation](#project-5-cohort-analysis-with-retention-rate-calculation)
- [Project 6: Functions: XLOOKUP, SPLIT](#project-6-functions-xlookup-split)
- [Skills Demonstrated](#skills-demonstrated)
- [Tools Used](#tools-used)
- [Key Features Used](#key-features-used)
- [Contact](#contact)
## Projects
### Project 1: Analyzing Game Performance and Monetization Metrics!
[View Project](https://docs.google.com/spreadsheets/d/1F9Uwg5q6XEsSFQx10aBxMZngsFyvJbU7XD-G8UP7-DA/edit?usp=sharing)
I cleaned data and calculated KPIs to improve game performance and provide insights for game managers.
Key Metrics:
Total Revenue: For each game.
Paid Users Count: Across all games.
Conversion Rate: Users to paid users.
ARPPU: Average revenue per paying user.
Age Statistics: Average, median, minimum, and maximum ages of paying users.
This project provided insights for improving game monetization and user engagement.
### Project 2: User Demographics and Device Analysis
[View Project](https://docs.google.com/spreadsheets/d/1LHg_NAnDhJs7B9ctFCGpdEKKXfptMYYg-y1Fdkvz8l8/edit?usp=sharing)
As 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.
- **Outcome**: Provided insights into user age distribution, language preferences, and device model trends.
### Project 3: Daily and Weekly Active Users Analysis
[View Project](https://docs.google.com/spreadsheets/d/1XgtfhbdW503GjHmgtdPYeKhkjHSXRfmUKU4CgEMyUUA/edit?usp=sharing)
I analyzed user engagement by creating and managing sheets for Daily Active Users (DAU) and Weekly Active Users (WAU), including statistical calculations and data visualizations.
- Activity Month: Created a column to extract the month from each activity_date.
- 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.
- Activity Month Number: Calculated how many months have passed since the user's first activity month, ensuring all values are 0 or greater.
- **DAU Sheet**: Created a sheet with unique `activity_date` values, calculated DAU using `COUNTUNIQUEIFS`, and added `week_start_date`.
- **WAU Sheet**: Developed a sheet with unique `week_start_date` values,
- **WAU Calculation**: Calculated WAU using the formula `=COUNTUNIQUEIFS(activity!A:A,activity!B:B,">="&A2,activity!B:B,"<"&A2+7)` to ensure accurate weekly user counts., and included columns for Average DAU and User Stickiness (DAU/WAU).
- **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.
This project provides a comprehensive view of user engagement, including trend analysis to support strategic decision-making.
### Project 4: Forecasting Daily and Weekly Active Users (WAU/DAU)
[View Project](https://docs.google.com/spreadsheets/d/1LRXWWGK_6Y1-qU6vuvjK7WRTNRVLUpg9p6oHu-mYKIw/edit?usp=sharing)
I forecasted Daily Active Users (DAU) and Weekly Active Users (WAU) for the next 20 weeks using historical data and built trend analysis.
- Extended Week Data: Added 20 new rows for future weeks on the "WAU" sheet.
- Forecasting: Used ROUND and FORECAST functions to predict DAU and WAU values for the new weeks, fixing historical data ranges for accurate forecasting.
- Stickiness Calculation: Filled in the DAU/WAU ratio for the forecasted weeks.
- Visualization: Created a chart showing WAU over time with a linear trendline to highlight user stickiness trends.
This project demonstrates my ability to forecast user metrics and visualize trends for strategic insights.
### Project 5: Cohort Analysis with Retention Rate Calculation
[View Project](https://docs.google.com/spreadsheets/d/1Z3gW2RgGniv1nwym5bCdX90N9PQBOtYxrChfdxG9KLA/edit?usp=sharing)
I conducted a cohort analysis based on users' first activity month, using pivot tables for unique user counts and retention rates, with slicers for filtering.
- 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.
- 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.
- Conditional Formatting: Applied gradient formatting to highlight key values.
- This analysis simplifies tracking user retention over time with flexible filtering and clear visuals.
### Project 6: Functions: XLOOKUP, SPLIT
[View Project](https://docs.google.com/spreadsheets/d/1UwEmHPn1qVHTHPm8Y45TkmckpetqHCia8FYy4rN3QVY/edit?usp=sharing)
I split the game activity data and mapped user languages using lookup functions to enhance data organization and analysis.
- 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, ":")
- 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)
This improved data clarity and allowed for a more granular analysis of user activity.
## Skills Demonstrated
Across these projects, I demonstrated a range of skills essential for data analysis, including:
- Data Cleaning: Handling raw datasets, transforming messy data into organized, usable formats, and ensuring accuracy.
- Statistical Analysis: Performing calculations like averages, medians, standard deviations, and percentiles to extract meaningful insights from the data.
- KPI Calculation: Expertise in calculating critical metrics such as total revenue, conversion rates, ARPPU, and retention rates to evaluate performance.
- Forecasting: Using functions like ROUND and FORECAST to project future user engagement and analyze trends.
- Cohort Analysis: Implementing cohort-based retention analysis using pivot tables to track user behavior and trends over time.
- Advanced Functions: Utilizing functions like XLOOKUP and SPLIT to map user data and organize activity logs for deeper analysis.
- Data Visualization: Creating charts and trendlines to communicate data insights clearly, aiding in decision-making and strategic planning.
- Google Sheets Automation: Efficient use of Google Sheets functions and tools for dynamic analysis and streamlined data handling.
## Tools Used:
- Microsoft Excel: For data cleaning, statistical analysis, KPI calculation, and financial tracking using advanced formulas and visualizations.
- Google Sheets: For organizing data, performing complex calculations, creating dynamic tables, and automating workflows with functions like XLOOKUP, SPLIT, and FORECAST.
### Key Features Used:
- Pivot Tables: To summarize and analyze large datasets efficiently, particularly for cohort analysis and tracking key metrics.
- Google Sheets Charts: For visualizing trends and patterns in user engagement, monetization, and forecasted data over time.
- 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.
## Contact
Feel free to connect with me via:
- LinkedIn https://www.linkedin.com/in/ihorshvets/,
- Email: [email protected]