DATA 201 | Thinking with Data | Final Project | Wildfires in Alberta

   

   

   

   

   

    DATA 201 | Data Wrangling | Digital Visualizations Assignment 4: Digital Visualization | Examining Rental Properties in Calgary Displaying rental prices in descending order. Visualization of how many rentals there are in each neighbourhood. The average going prices for rentals in each neighbourhood. 1. What are some distinctive observations between the attributes of neighbourhood and price? Which neighbourhood, on average, houses the most expensive rentals? Are there common attributes among the most expensive neighbourhoods? Looking at the raw data in Excel, one can infer that the neighbourhood “WEST HILLHURST '' had the highest sum of the prices in the neighbourhoods with a whopping $485. (I created a table, aggregated the sum of prices, and sorted it from largest to smallest.) I wanted to test whether the MEAN prices reflect the same story in my analysis. First, I looked at the rental count in each neighbourhood by using the Calculated Field with the function Count on T...

    "Data Analytics for Business Professionals" - This course is only 1 hour and 16 minutes long and covers data visualization, data-driven decision-making, and business applications of analytics. "Learning Data Analytics: 1 Foundation" - While slightly longer at 3 hours and 29 minutes, this course provides a comprehensive introduction to data analytics, covering topics like SQL, data cleaning, and working with tools like Excel and PowerBI. "Data Analytics: Dashboards vs. Data Stories" - This is a very short 17-minute course that introduces you to two common data science and analytics tools: dashboards and data stories. "Data Analytics: Graph Analytics" - Another quick 19-minute course focusing on graph analytics9. "Introduction to Career Skills in Data Analytics" - This 2-hour 21-minute course provides an overview of data analytics concepts and prepares you for the Microsoft GSI Data Analytics certification exam.

    DATA 201 | Data Wrangling Assignment 3: Data Wrangling  Screenshot of the raw data (with the empty spaces highlighted) Omitting Omitting Irrelevant Attributes The first thing I did was to omit any attributes that were either empty or had useless information. This included the attributes “name”, “keywords”, “language”, and “currency symbol”. I decided to keep the attributes “occasion” and “currency” because although the data is very small, I thought They could still be meaningful to the research being done. This step addresses the data quality issues of missing data. What I did using Excel was click on the columns I wanted to omit and press delete. This could have also been done using OpenRefine under edit column, and clicking “remove column”. Removing unnecessary data and attributes is important because it increases the quality of the data, especially since they will have no use in the analysis process. Omitting Useless entities After Omitting the attributes, I went ...