Masaya Kitani Aspiring Data Analyst
Actionable data-driven insights
About Me
I'm an aspiring data analyst that loves spreadsheets and data, and using that data to uncover the reasons behind user behavior. I like making complex ideas easier to digest, and providing actionable insights using data wrangling, visualization, and analytics with quantitative and qualitative data. I'm currently based in Shikoku and I'm excited to combine my love of storytelling and logical reasoning with data and information.English proficiency: Native
Japanese proficiency: N4
Open to Relocation
Portfolio
Video Games Sales Data Analysis Using SQL
Objective
Utilize data wrangling tools and SQL to explore a dataset that delves into various factors that influenced the sales of video games globally.Tools Used
LibreOffice Calc
SQLite
DB Browser for SQLite
Notepad++
GitHubSummary
Utilized Calc to clean and enrich over 16,000 rows in a .csv file containing video game sales data, including regional sales, platform details, and publisher information. By using data wrangling techniques and SQL queries, the dataset was transformed into a format suitable for analysis, enabling the exploration of trends and insights into the gaming industry's global sales dynamics.Key Insights
• Identified which publishers had the highest sales
• Analyzed the most popular systems at certain time periods.
• Investigated the regional game preferences of major sales areas
Click here to see the project on GitHub along with documentation.
Titanic Passenger Survival Analysis
Objective
Investigate which factors influenced passenger survival rates.Tools Used
LibreOffice Calc
Tableau PublicSummary
Analyzed the survival rates of Titanic passengers to uncover patterns based on class, sex, family size, and cabin location. The project focused on data wrangling, visualization, and exploratory analysis using LibreOffice Calc. Used Tableau to create a visually appealing dashboard that presents major factors that had trends which affected survival likelihood.
Key Insights
• Class: First-class passengers had a 62% survival rate, whereas third-class passengers had a 24% survival rate.• Sex: Female passengers had a survival rate of 74%, compared to male passengers who had a 19% survival rate.• Family Size: Families of 4 had the highest survival rate at 72%, while families of 8 or more had a 0% chance.• Cabin Deck: Though severely lacking in quantitative data, cabin decks showed passengers on decks B, D, E, and F having roughly 75% survival rates.• Combined Class & Gender: Female passengers in first and second class had a demonstrably higher survival rate than those in third class, while male passengers in first class had a notably higher survival rate than male passengers in other classes.
Insight: First-class passengers were over 2.5 times as likely to survive when compared to third-class passengers.Why it Matters: This highlights the influence of socioeconomic status on probability of survival, which was a well documented pattern of the Titanic disaster.
Insight: As famously depicted by the 1997 film Titanic, female passengers had a much higher rate of survival.Why it Matters: This reflects the priority given to women and children during the evacuation, which shows the strong impact of gender on survival odds.
Insight: Female passengers in first and second class were over 40% more likely to survive, however male passengers in first class were 2 times more likely to survive when compared to their second and third class counterparts.Why it Matters: This graph combines class and gender to show how these factors combined to influence survival odds.
After working through the data in LibreOffice Calc, I exported the data to Tableau to create a dashboard, which you can find here, on Tableau Public.