Here is how we’ll do it:<\/p>\n
Activity 1: Get introduced to \u201ctext analytics\u201d<\/h3>\n
Read the post Let\u2019s talk about text analytics<\/a> and make a contribution by sharing a technique or application you find interesting (mentioned in the article or a new one). You can add a comment to the post or write a new one (one per group). Please explain clearly and include references\/links accordingly.<\/p>\n Set up a database with food product reviews from Amazon<\/a> (ask further directions). Do some descriptive statistics to refresh query basics<\/a>. Then try to apply some of the techniques mentioned in the post such as stemming or word counting to get some insights from the reviews (comparison keywords<\/a> might be handy). For instance, are there words with higher correlation with positive or negative product reviews? Don’t get too fancy, but try to see if you can get something interesting. Put your findings in a blog post (one per group), explaining briefly how you crunched the data. Don’t forget the data visualization<\/a> tips (or your post might not be published).<\/p>\n Get Python set up in your computer and do Google’s Python Class<\/a>. This is a 2 day tutorial and you might need to start working\u00a0on your project before you finish, but try to get as far as you can. Alternatively, you can do the Python course in Codecademy<\/a>, doesn’t require to install anything and is very easy to follow. Even if you don’t use Python, the coding skills will be handy sooner than you think. If you feel confident and prepared for new challenges, try to parse the<\/a> original <\/a> food review dataset<\/a> using Python.<\/p>\n Good luck…<\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":" Welcome to the project season, padawans! You’ve learned quite a bit of theory during the last months and now you are ready to face some real world OR problems. Before diving into the projects we will spend a couple of days brushing up some practical skills that will be useful during the project season and […]<\/p>\n","protected":false},"author":22982,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[1054146,1054145],"class_list":["post-1121","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-industry-projects","tag-mmor"],"_links":{"self":[{"href":"https:\/\/blogs.ubc.ca\/coetoolbox\/wp-json\/wp\/v2\/posts\/1121","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.ubc.ca\/coetoolbox\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.ubc.ca\/coetoolbox\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.ubc.ca\/coetoolbox\/wp-json\/wp\/v2\/users\/22982"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.ubc.ca\/coetoolbox\/wp-json\/wp\/v2\/comments?post=1121"}],"version-history":[{"count":15,"href":"https:\/\/blogs.ubc.ca\/coetoolbox\/wp-json\/wp\/v2\/posts\/1121\/revisions"}],"predecessor-version":[{"id":1212,"href":"https:\/\/blogs.ubc.ca\/coetoolbox\/wp-json\/wp\/v2\/posts\/1121\/revisions\/1212"}],"wp:attachment":[{"href":"https:\/\/blogs.ubc.ca\/coetoolbox\/wp-json\/wp\/v2\/media?parent=1121"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.ubc.ca\/coetoolbox\/wp-json\/wp\/v2\/categories?post=1121"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.ubc.ca\/coetoolbox\/wp-json\/wp\/v2\/tags?post=1121"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Activity 2: Analyze text data using SQL Server<\/h3>\n
Activity 3: Get prepared to code in Python<\/h3>\n
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