Cherie
Pavico-Tsukayama

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Data-Driven 📊 Marketing Creative 🎨 @ Voiceplace | Data Analytics | Data Visualization | SQL | Tableau | Excel | Kajabi Expert | HTML | CSS | JS

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I recently used Tableau and SQL to analyze data from competitive games in League of Legends, and the results were fascinating. By visualizing the most popular champions by position, I gained a clearer understanding of player preferences and strategies. In addition, using SQL allowed me to create a data frame and visualize the player with the most kills during a specific time frame. I found that Bang had the highest number of kills from 2015-2018, and his most used champions varied based on the team he was playing against. These insights can be incredibly valuable for players and teams looking to improve their gameplay and increase their chances of winning. Check out my article to learn more about the power of data analysis in competitive gaming! #Tableau #SQL #DataAnalysis #LeagueOfLegends #GamingInsights

Tools Used

Data Mining with Python: Uncovering Insights on Iron and Silica Concentration

I’m excited to share my latest article on data analytics! In this article, I delve into the world of iron ore mining and showcase how I used Python, Seaborn, Pandas, and Matplotlib to analyze data on Iron and Silica concentrate in a flotation plant. The goal of the project was to take a look at monthly production of Iron concentrate and Silica concentrate. If you’re interested in learning more about how I used python to analyze this data, give my article a read!

Tools Used

Score Big with Analytics: Unlocking the Secrets of Basketball Performance

I completed my Sports Data Analytics project, which was so fascinating to me. I don’t know much about basketball but by doing this analysis I learned great insights into the positions and players.

I decided to present my findings via video to practice my presenting skills more. It’s something I’m comfortable with and have done on other projects for my current position.

These are the key points that I use this data set for.

  1. How players did on total points, total assists, & total rebounds by position
  2. Create a reference sheet for where the best 3-point shooters are for each team.
  3. What were the total points scored by each team and how each player contributed to the total points?
  4. Show the assists made by each position.

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Patient Profiles: Understanding Who’s Coming Through the Doors

It was fun to delve deeper into the use of SQL clauses, keywords, and methods. I utilized two tables in the data set to obtain the results I was looking for. In this article, I’ll share my experience and show you how I used SQL to get the information I needed.

The specific terms that will be covered in this article are

Tools Used

Unlocking Insights from Historical IDA Credit and Grant Data with SQL and bit.io

By using the historical dataset provided by The World Bank and using SQL I was able gain the below insights.

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From Pennies to Plenty: A Data-Driven Study of DoorDash Spend

Marketing Analytics Project on DoorDash spend where I share how I found these results.

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Visualizing The Achievement Gap: A Tableau Project Highlighting Graduation Analytics in Massachusetts Education

Education Analysis Project built in Tableau Public for personal and professional knowledge.

Tools Used