From product and ops analytics to machine learning projects by Tania Vasilikioti

machine learning
Tweet about this on TwitterShare on FacebookGoogle+Share on LinkedIn

Learning here is like drinking from a firehose.

Hi, I’m Tania and I come from the world of operations and data analytics for online tech. After studying economics in the US, I worked as a Project Analyst at Expedia and then as Head of Operations for a UK/Spanish start-up – DogBuddy (which got acquired last week, to my huge excitement!). After working on the intersection of data, tech and project management, I realized that I actually enjoy quite a lot the technical side of analytics, coding and helping build smart data solutions, which brought me to the IE Data Science Bootcamp.

So, what’s the program like? As the end of the fifth week approaches, I’m feeling more and more the contrast between a) lack of time and b) the insatiable desire to learn. There are so many opportunities both in and out of class that the hardest thing is not memorizing any code or studying for exams, but choosing how to allocate your time.

First, there’s the coursework: this week we started with Machine Learning in R with Iván Martín Maseda, the Academic Director of the program. Iván is an amazing teacher and I’m really excited to improve my understanding of various machine learning algorithms and their industry application. We’ve also finished the SQL module, concluded all chapters on Inference Statistics (with a subsequent test of course!), and continued with the daily 3-hour Python grind.

Then, there’s the capstone project: my team is working with two data scientists from Nielsen, to deliver an end-to-end project on beverage product pairings in Spain. We’ve done some solid work cleaning the massive dataset in R, and now need to tackle EDA. The final product is meant to be presented in a R Shiny App, so we need to learn about that tool as well.

Next: self-study and my own projects. In any free time I have, I try to study some new topics or read on a different approach to topics we’ve discussed in class. For example, I’ve just finished reading the O’Reilly guide Practical Statistics for Data Scientists and I’ve started with some chapters on Machine Learning with R by Brett Lantz. The latter is very helpful for the project with Nielsen and to brush up on the theory of specific algorithms before we discuss them in class. I also have my own projects that I wish I had more time to work on, but that’s what the weekends are for! Currently I’m trying to figure out what Barcelona’s most “eco-friendly” areas are, by looking at the location of bikeshare stations, metro stops, electric car charging spots and of course, green space.

Finally, there’s all the incredible data science and startup events that Madrid has to offer, such as the events hosted at Google Campus by various groups. Last week I went to the Data Beers event, where I learned about applications of “fake” data by Amadeus, a new methodology to quantify culture through Facebook, and how the French spend their mobile data. This week there was a talk on Artificial Neural Networks at IE by our own professor Iván Martín Maseda. Next week there’s yet another event by PyLadies Madrid, which is a great group to follow if you’re a woman interested in learning Python.

And the best thing – I’m never really tired of this hustle and bustle. I’m incredibly thankful that I have discovered a topic that fascinates me, a community that is interested in practical applications of data and a group of great classmates and mentors to learn from. Can we just make the day have 40 hours?

Tweet about this on TwitterShare on FacebookGoogle+Share on LinkedIn