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 ...

    DATA 201 - Thinking with Data | Survey Design and Development Assignments 2 What do you hope to understand from this dataset? I plan to compare the factors of immigrants and non-immigrants pursuing post- secondary education, specifically university education.  What is your goal for collecting this dataset? To identify whether there's a significant difference or trend in factors between Canadian immigrants and Canadian-born citizens pursuing post-secondary education university). Who are your target participants?  An even Canadian and non-immigrant ratio for university undergrads and grads would be ideal. Recently graduated students can also work; however, those still in university would be better candidates. Provide a clear description of the chosen data sets, including all the attributes and the type of question. Type of Question: Multiple Choice Question ⭐ Text Entry Question 💓 Likert Scale Question/Statement🔶 Rank Order Question ⬛ Q1. Were you born in Canada...