iLab1 – Key takeaways from each of the subjects completed

Key takeaways from each of the subjects completed

Data Science for Innovation (DSI)

English isn’t my mother tongue so I think my key takeaway for this subject will be academic writing. I have been living in Australia for 5 years and I use English everyday at work but it is quite informal in general. I mainly write emails, prepare some presentations or write project reports. It was the first time that I have to write some kind of essay in an academic way. Getting detailed feedbacks for the different assignments I submitted did really help me to understanding where were my limits and I have already seen some improvements compared to a semester ago.

Data, Algorithms and Meaning (DAM)

For this subject I learned from day 1 that I was already capable of running some machine learning techniques by myself. I used R in the last 2 years prior to enroll in MDSI at work mainly for data wrangling and data visualisation. But my main takeaway is really the ability to interpret the output of any algorithm but also to “challenge” it. It is extremely important to be able to take a step back and really think about how those results link back to the business objective first but also to their potential negative impacts. If too quickly taken for granted those results can change dramatically the life of individuals.s

Statistical Thinking for Data Science

I kind of knew it before but following this course did confirm the fact that having a strong statistical background for a data scientist is not an option but a must. Anyone can easily apply a machine learning algorithm or run a regression analysis in few lines of codes but the ability to interpret the results and assess the performance of a model is definitely key for a proper  data scientist. This course gave me an overview of some of the most useful techniques but I felt that I was still missing the basics and I really wanted to understand the “behind the scenes”. For this reason I picked an elective related to stats for the second semester called Multivariate Data Analysis.

 

I am really looking forward for semester 2 🙂

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