# data[].attributes.body
A Portuguese hotel chain with resorts in Lisbon and Algarve has experienced spikes in cancellation rates for the 3rd straight year, and needed to quantify the impact on the business and find potential improvement opportunities.

The goal of the dashboard was to answer these three questions:

1. What impact do the cancellation rates have on revenue?

2. Do the cancelation spikes show any patterns?

3. Are there opportunities to fill the rooms left empty by cancelations?

The analysis was conducted using PivotTables in Microsoft Excel, and the results are presented in a dashboard.

# data[].attributes.headline
Distribution of Average Revenue per Customer (Seaborn/Matplotlib)

# data[].attributes.subject
Distribution of Average Revenue per Customer (Seaborn/Matplotlib)

# data[].attributes.headline
Median Revenue per Site Visit by Sales Method and State (Seaborn/Matplotlib)

# data[].attributes.subject
Median Revenue per Site Visit by Sales Method and State (Seaborn/Matplotlib)

# data[].attributes.body
I facilitated a Strengths-based workshop to help the team understand the diversity of strengths across the company. I collected, cleaned and visualised the team's VIA strengths using a Chord diagram.

# data[].attributes.headline
Distribution of Revenue by Sales Method presented as a Box Plot.

# data[].attributes.subject
Distribution of Revenue by Sales Method presented as a Box Plot.

# data[].attributes.headline
Sequence of Sales Methods used over 6 Weeks, Grouped by Revenue (Seaborn/Matplotlib)

# data[].attributes.subject
Sequence of Sales Methods used over 6 Weeks, Grouped by Revenue (Seaborn/Matplotlib)

# data[].attributes.body
This project analyzes the total annual cost to run animal shelters, focusing on costs by animal type and size (e.g., small dogs, large cats). It calculates costs based on animal size/type, age, and shelter location, excluding sponsored animals. The analysis uses SQL to process data from multiple tables, categorizing animals by updated size criteria and incorporating additional costs for age and location. The output includes a table with total costs per animal type/size combination and a percentage of the total cost for visualization purposes. The project is built using PostgreSQL and includes a sample query to demonstrate the cost calculation logic.

# data[].attributes.attachment_link.description
Capstone SQL Project. Contribute to DaveRizzle/animal-shelter-analysis development by creating an account on GitHub.

# data[].attributes.body
Scenario:

3 Regional Sales Managers have a monthly performance call with their COO and need a dashboard they can use to track revenue trends, compare performance across stores, and identify growing & declining products.

The goal was to create a dashboard to help drive the conversation and answer these questions:

1. How much revenue did the region generate this month?

2. How does this compare to previous periods?

3. Are there any seasonal trends worth noting?

4. What were the best-performing stores across the country?

5. Which products stand out in terms of growth?

The dashboard automatically updates as new data is added and lets users filter by different regions.

# data[].attributes.body
This project visualizes the global impact of a pandemic from March 2020 to June 2022, focusing on infection rates and death counts. It includes:

1. Global Numbers: Displays total cases (576,257,147), total deaths (6,357,999), and death rate (1.10%).

2. Total Death Count by Continent: A bar chart showing Europe with the highest death count (~2M), followed by North America, South America, Asia, Africa, and Oceania.

3. Percent Population Infected by Country: A world map highlighting infection rates, with darker shades indicating higher percentages (e.g., Europe and North America show higher infection rates).

4. Average Percent Population Infected Over Time: A line graph comparing regions like Africa (0.87%), the European Union, the United States (26.57%), Saudi Arabia, South Africa (6.73%), Europe, North America, and South America, showing a sharp rise in infections from late 2021 to early 2022, peaking around March 2022.

# data[].attributes.body
The goal was to create a dashboard to help seniors answer these questions:

1. Which industries have the highest annual wages?

2. How large is the employment pool by industry relative to the whole market?

3. Are there any yearly trends worth noting?

4. Which states present the best opportunities for each industry?

The dashboard was built using Microsoft Excel and lets students scroll through and highlight different industries and change the metric visualized on the map.

# data[].attributes.headline
Apple Ultra IOS Data (Pandas + Google Sheets)

# data[].attributes.subject
Apple Ultra IOS Data (Pandas + Google Sheets)

# data[].attributes.body
I created this dashboard to better understand my personal health metrics. I exported and ingested this data into Pandas for filtering and groupby operations. I used Google Sheets to visualise the data using pivot tables.

# data[].attributes.headline
Data Literacy Essentials: Decision Intelligence

# data[].attributes.subject
Data Literacy Essentials: Decision Intelligence

# data[].attributes.body
Intelligence

# data[].attributes.attachment_link.title
Data Literacy Essentials- Decision Intelligence

# data[].attributes.headline
Best Practices For Effective Requirements Gathering

# data[].attributes.subject
Best Practices For Effective Requirements Gathering

# data[].attributes.body
Requirements Gathering

# data[].attributes.headline
Putting People First as a Business Analyst

# data[].attributes.subject
Putting People First as a Business Analyst

# data[].attributes.body
Analyst

# data[].attributes.headline
Understanding and Applying Leadership Styles

# data[].attributes.subject
Understanding and Applying Leadership Styles

# data[].attributes.body
Leadership Styles

# data[].attributes.headline
GitHub Pull Requests from Start toFinish

# data[].attributes.subject
GitHub Pull Requests from Start toFinish

# data[].attributes.headline
DataCamp Certified Professional Data Analyst

# data[].attributes.subject
DataCamp Certified Professional Data Analyst

# data[].attributes.body
The DA201 is a 60-minute exam where I used Python to work through timed questions with adaptive difficulty. This exam assessed my ability to use Python for data management, exploratory analysis, and statistical experimentation within a fixed timeframe of 60 seconds per question. Hours of Training: 140

# data[].attributes.body
In this project, we embark on a comprehensive analysis of the Android app market by examining over ten thousand apps on Google Play across different categories. The objective is to derive insights into app preferences, performance, and market dynamics, which can aid developers and companies in strategizing for growth and retention.

# data[].attributes.attachment_link.description
Contribute to DaveRizzle/The-Android-App-Market-on-Google-Play development by creating an account on GitHub.

# data[].attributes.body
This project explores the evolution of machine learning by analyzing papers from the NIPS (Neural Information Processing Systems) conference from 1987 to 2017. Utilizing natural language processing (NLP) techniques, we delve into the content of over 50,000 papers to uncover trends and topics within the machine learning community.

# data[].attributes.headline
Exploring the Bitcoin Cryptocurrency Market

# data[].attributes.subject
Exploring the Bitcoin Cryptocurrency Market

# data[].attributes.body
In the ever-evolving world of cryptocurrencies, Bitcoin has emerged as a dominant force, but it's just the tip of the iceberg. With the explosion of blockchain technology since Bitcoin's inception in 2008, a myriad of cryptocurrencies have entered the market. As of December 6, 2017, Bitcoin's market capitalization soared above $200 billion, highlighting the astronomical growth of the cryptocurrency market. However, this market is known for its volatility, with values that can skyrocket or plummet overnight.

# data[].attributes.headline
Dr. Semmelweis and the Discovery of Handwashing

# data[].attributes.subject
Dr. Semmelweis and the Discovery of Handwashing

# data[].attributes.body
This project revisits a pivotal moment in medical history: Dr. Ignaz Semmelweis's discovery of the importance of handwashing in preventing childbed fever in the mid-19th century. Despite facing opposition and skepticism from his peers, Semmelweis's work at the Vienna General Hospital revealed a simple yet groundbreaking solution to a deadly problem. This analysis aims to demonstrate the impact of his discovery using data analysis techniques.

# data[].attributes.body
I scraped 2 years of health data from my wearable and deployed a dashboard detailing my analysis using Streamlit.

https://david-robertson67.medium.com/data-project-part-1-sleep-analysis-e1272d6c39ab

https://daverizzle-portfolio-projects-my-sleep-data-yhdaxn.streamlit.app/

# data[].attributes.headline
Data Storytelling and Data Visualization Mastery

# data[].attributes.subject
Data Storytelling and Data Visualization Mastery

# data[].attributes.headline
DataCamp Certified Associate Data Analyst

# data[].attributes.subject
DataCamp Certified Associate Data Analyst

# data[].attributes.body
The DA101 is a 2-hour exam that assesses proficiency in data management, exploratory analysis, and statistical experimentation using SQL. The assessment tested my ability to complete tasks within a pre-defined time range of 60 seconds per question.

# data[].attributes.headline
Functions for Manipulating Data in PostgreSQL

# data[].attributes.subject
Functions for Manipulating Data in PostgreSQL

# data[].attributes.headline
PostgreSQL Summary Stats and Window Functions

# data[].attributes.subject
PostgreSQL Summary Stats and Window Functions

# data[].attributes.body
An MTA 98-391 certified individual can work with Python on a professional level and is ready to explore the advanced aspects of the high-level, general-purpose programming language and related technologies.

# data[].attributes.body
Foundational level knowledge on cloud concepts; core Azure services; and Azure management and governance features and tools.

# data[].attributes.headline
Investigating Netflix Movies and Guest Stars in The Office

# data[].attributes.subject
Investigating Netflix Movies and Guest Stars in The Office

# data[].attributes.body
This project explores the changing landscape of movie durations on Netflix from 2011 to 2020. Leveraging Python's pandas and matplotlib libraries, we delve into a dataset containing details of Netflix's offerings to understand trends in movie lengths over the last decade. Our friend, keen on analyzing Netflix's vast catalog, provided initial insights suggesting a decline in the average duration of movies. This analysis aims to explore that assertion further.

# data[].attributes.headline
What and Where Are the World's Oldest Businesses?

# data[].attributes.subject
What and Where Are the World's Oldest Businesses?

# data[].attributes.body
This project delves into the fascinating realm of the oldest businesses still in operation around the globe. With a dataset curated by BusinessFinancing.co.uk, we embark on a journey through history, tracing the endurance of companies that have weathered centuries of economic shifts, wars, and technological advancements.

# data[].attributes.headline
Introduction to Data Visualization with Seaborn

# data[].attributes.subject
Introduction to Data Visualization with Seaborn

# data[].attributes.headline
Introduction to Importing Data in Python

# data[].attributes.subject
Introduction to Importing Data in Python

# data[].attributes.headline
Name: David Robertson. Interests: Analytics, strength training and rock climbing.

# data[].attributes.subject
Name: David Robertson. Interests: Analytics, strength training and rock climbing.

# data[].attributes.body
https://www.linkedin.com/in/david-robertson11/

# data[].attributes.headline
Introduction to Relational Databases in SQL

# data[].attributes.subject
Introduction to Relational Databases in SQL

# data[].attributes.body
This project dives into the international debt data collected by The World Bank, exploring the intricacies of the financial obligations that developing countries have towards this global institution. The main objective is to analyze the dataset to uncover insights into the total amount of debt owed by these countries, identify the country with the maximum debt, and understand the distribution of debt across different indicators.

# data[].attributes.attachment_link.description
Contribute to DaveRizzle/Analyze-International-Debt-Statistics development by creating an account on GitHub.

# data[].attributes.headline
Coach Attendance Tracker (Pandas + Google Sheets)

# data[].attributes.subject
Coach Attendance Tracker (Pandas + Google Sheets)

# data[].attributes.body
I assisted CodeSpace with the creation and implementation of a Coach Attendance Tracker. This data was ingested via multiple reports from the Google Workspace API and cleaned using Python Pandas. I visualised the data using Google Sheets.

# data[].attributes.body
I’m expanding my coaching services by incorporating data analytics into my approach, creating a unique blend of insights and strategy for my clients. To document my journey and showcase progress, I’ve built a website hosted on GitHub: GitHub Repository.

# data[].attributes.headline
Student Attendance Tracker (Pandas + Google Sheets)

# data[].attributes.subject
Student Attendance Tracker (Pandas + Google Sheets)

# data[].attributes.body
I assisted CodeSpace with the creation and implementation of a Student Attendance Tracker. This data was ingested via multiple reports from the Google Workspace API and cleaned using Python Pandas. I visualised the data using Google Sheets.

# data[].attributes.body
In this project, we delve into the evolution of video games from 1977 to 2020, examining the sales, critical acclaim, and player reception of the top 400 best-selling games. With the global gaming market's exponential growth, understanding the trends and qualities that define successful video games has become crucial for game developers and publishers.

# data[].attributes.body
In this case study I investigated cases of food poisoning. I identified 4 locations with varying numbers of cases and 9 instances of linked cases. Causes of claims predominantly revolved around food poisoning, with meat and vegetables being the most common sources.

# data[].attributes.attachment_link.description
Contribute to DaveRizzle/datacamp-case-study development by creating an account on GitHub.