Going Back to the Basics?

With data science languages, sometimes learning the basics can be the hardest part. The QAC offers several .25 credit classes that introduce students to the necessities of different languages, but even fitting all the necessary information into a half a semester can be difficult. This past quarter, Professor Pavel Oleinikov utilized a website called DataCamp to help his students get comfortable with the basics of Python. DataCamp is an online collection of data science lessons that teaches users through videos and repetitive exercises. The website has an in-browser code box that allows users to code right on the website without having to download any software. Each lesson takes roughly 30 minutes to 1 hour to complete, making it a convenient way to nail down a specific skill.

Students in Pavel’s Working with Python class really enjoyed being assigned DataCamp lessons as homework. “We only have 3 hours, and that may seem long but that’s not a lot of time considering the concepts that we’re learning,” said Anthony Price. These .25 credit classes move quickly, and so there isn’t much time to backtrack if students are lost. And students can always Google around for answers, but sometimes the vast amount of material returned can be overwhelming. This is why it is important to have resources in place so that students don’t give up before they get comfortable.

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Can we Utilize Passion in Data Science?

It can be easy to think of data science as cut and dry analysis consisting solely of numbers. But according to Economics major Leah Giacalone ’17, if people think of it that way it’s just because they haven’t tried it yet. “Personally, I’ve always found being able to code super exciting,” she said. “The first time I wrote code and then it worked was the most exciting thing ever. I always tell people that and they don’t believe me.”

If you are someone who doesn’t believe in the passion underlying data science, then maybe it’s time to give it a go, because an increasing number of companies are utilizing passion as a power source for their problems. An example of this is Kaggle, a website founded in 2010 that allows companies to post their data and research problems online so that people from around the world can compete to create the best solution. Kaggle is using the overflow of big data to its advantage to create a sort of Kickstarter for data science. It’s engaging, fresh, and possibly a good way for data analysis hopefuls to break the ice with coding.

Recently, Kaggle was used at Wesleyan by Professor Pavel Oleinikov in his class “Introduction to Text Mining.” Pavel asked his students to use customer reviews from Yelp to create an algorithm that could best predict whether a Yelp review was positive (4 or 5 stars) or negative (1 or 2 stars). The students used reviews of Arizona hotels as a guide for creating dictionaries of 20 positive and 20 negative words that would then be built into their model. The accuracy of the students’ models was then tested on Pennsylvania hotel reviews and judged by a mean utility metric that assigned a score between -1 (wrong in every case) and 1 (got everything correct). Narin Luangrath ‘17, a Math and Computer Science double major, won the class competition with a mean utility score of .72.

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Seasons of Internships

I’ve feared the moment that my summers would be turned over to internships for a long time. I can’t remember for how long I’ve known internships are important – probably for as long as I’ve known about applying for college. My relationship with the idea of internships has gone through stages, with me sliding from thinking that they are silly resume builders to valuable and necessary work experience almost every day. I recently decided that I wanted to pursue some sort of consulting internship, and then felt a drop in my stomach similar to when I decided to apply for Wesleyan. But while there is a large and personalized application process still ahead of me, I don’t want to feel as scared as I did then. With this in mind, I sat down with Asie Makarova ’17 and Taylor Chin ’18 to discuss two of the main myths about internships and what truths, based on their experience, lie beneath.

Both Asie and Taylor had similar beginnings to their internship journey. “I started pretty early applying to things Junior fall,” Asie remembered. She found her connection through LinkedIn, by reaching out to a friend’s dad who then put her in contact with FTI Consulting. Taylor also came across his internship on LinkedIn when he noticed that an old friend from high school had connections at an energy intelligence software company called EnerNOC. From there, both Taylor and Asie got offered interviews at their respective companies.

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