Our group embarked on the quest to find an answer to the title question. Our approach consisted of trying to identify the terms that are commonly tagged along with the Twitter handle #Datascience. This provides the keywords that are most often associated with ‘data science’. The top 20 de-duped keywords will serve as a dictionary for the analysis. It is important to note that, these keywords may not be used in professional job listings. To validate the findings from Twitter, professional job listing sites in the US such as LinkedIn and Indeed will be used.
Twitter is a social network founded in 2006, it has over 325 million members and serves as a barometer of public opinion. Social media has played a fundamental role in the social activism of the 21st century. Nevertheless, people share their opinions and connect in a wide range of areas including careers. Is precisely this fact that motivated us to treat Twitter as a barometer for what hashtags people associate most commonly to Data Science.
To have access to the Twitter data, we used the twitteR package. An API account and app had to be created to be able to access Twitter’s API. After connecting to twitter, we asked for the top 1000 tweets containing the hashtag #DataScience. We extracted the words from these tweets and got 5200 words. If we group and count the words that are repeated we get about 493 words.
## `summarise()` ungrouping output (override with `.groups` argument)
count_word_cut<-count_word%>% filter(word_count>20) ggplot(count_word_cut, mapping=aes(x=reorder(word,word_count),y=word_count))+ geom_bar(stat="identity")+ coord_flip() + labs(title="Twitter Scrape for hashtags when #DataScience is Used",x="hashtags", y="count")