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The Importance of Data Science

The Importance of Data Science

Docent, PhD, Luigia Petre, Åbo Akademi University, explains the importance of Data Science and why people in the work force should sign up for the Open University online courses Data Analytics Software and Machine Learning starting on January 15, 2019.

Course registration

The news often report about cars that drive themselves; about trucks that drive themselves; about medical devices that adapt to the patient needs; about elections that were influenced by certain algorithms. More mundanely, we have vacuum cleaners that clean our floors when scheduled; Netflix, Spotify, and Facebook which offer us entertainment matching our taste; if I leave home in the morning and it’s a working day, my phone tells me I have a three minutes’ drive till the daycare address and then a 17 minutes’ drive to the University. This is wonderful and a little bit worrying at the same time. What seems inevitable is that this type of technology is here to stay, and scientists often say that it’s simply the top of the iceberg.

What is it about then?

This type of technology is based on the rather recent availability of massive amounts of data that can be analyzed and employed towards certain goals. Another component is the seamless integration of advanced sensors (such as video cameras) into various mechanisms. Self-driving vehicles can work due to the availability of maps, GPS devices, and cameras.

Medical devices learn how to treat a patient by studying hundreds of thousands of cases and their treatments; they start to learn to make predictions on what works and what not for a certain patient, based on that particular patient’s own data and symptoms. Elections can be influenced because the profiles of voters are so easily available (publicly) and links can be drawn between certain lifestyles and preferences to the type of candidates a certain person profile will most likely vote. Vacuum cleaners have algorithms that calculate the area they cover and the paths not yet taken. The entertainment software is based on information collected from millions of users on what movies/songs/post types people with a particular family status, age, education, etc. have preferred in the past and they use this to suggest new movies/songs/posts to the users fitting the profiles.

And the GPS on my phone collects dutifully, day after day, my destinations at certain times and makes educated assumptions about the place where I stay every night as being home, the place where I spend most of my working days being work, and so on. It’s all not so complicated, but is mind-blowing nevertheless. It’s also not perfect, but according to Prof. Moshe Vardi (Rice University, USA), it does not need to be: “it just has to be (slightly) better than you”.

Is it hard to become a data science specialist?

This type of technology is commonly known as data science. It’s the field where most money are being invested and where most of the jobs are and likely will be for years to come. Is it hard to become a data science specialist? Like most roads, the road to becoming a data scientist has several components and it starts with that first step: preparation. The preparation for becoming a data scientist starts with studying the basics of data science.

At Åbo Akademi University we have prepared several courses packed in a module named ”Computational Data Analytics” that offers the possibility of that first step. Remarkably, these courses are offered online, and thus, people can study at their own pace, from whatever location they find themselves at. This offers a great flexibility to people already involved in the workforce.

Two courses starting in Spring 2019

Among other courses, two are starting soon, in Spring 2019: Machine Learning and Data Analytics Software.

  • Machine Learning: Machine Learning skills are fast becoming necessary for data scientists as companies navigate the data and try to build automated decision systems that hinge on predictive accuracy. At the end of this course students get familiar with the foundation of machine learning. They would be able to analyze the data with standard machine learning skills and would be able to write the relevant code in MATLAB/Octave to facilitate these analyses.
  • Data Analytics Software: This course gives an overview to some of the data analysis software. The focus in this course is on software that require minimal coding and can be efficiently used by students from all branches of studies, ranging from computer science to biology, to economy, and engineering.

These courses are structured so that every week new lectures and tasks are opened, and students can learn and return the solved tasks at their own pace. We never physically (need to) meet, but we are always a message away. We have forums where both students and instructors participate and exchange ideas about solving tasks and understanding concepts. The reaction time is remarkably short, the feedback is personalized and the overall experience is that of being part of a huge community of people just like you.

Here are a few of the most recent feedback to two courses that took place in November-December 2018:

– I’m surprised at how much I actually learned during this course.

– I’ve gotten more feedback than I thought was possible in such a big course.

– Good feedback and good reactivity when we have questions.

– The feedback was inspiring and descriptive.

– I like the freedom of the MOOC-like course, because I had a lot of courses on the same time.

– As I’m working full time, this approach is better for my current situation.

– One advantage is that you can always see the lecture again; in classroom-based courses, if you let your thoughts wander for a few minutes you’ve missed that information forever.

– I joined this course on accident. I meant to choose another one and accidentally applied for this one. But I thought I would take the challenge since I happened to have some experience with python. I expected it to be hard but it turned out to be doable with the great lectures and learning material provided.

– It’s awesome for me, mostly because it also has an element of classroom teaching from the videos. It would be entirely different if it were just slides. But having videos, materials, examples, and assignments as well as ability to do it in one’s free time makes it perfect for me.

– A wonderful course and I hope there would be more like it.

Although the data science related jobs are among the popular trends in Finland, the formal education is lacking behind in the country and the only path for the local professionals to obtain expertise in this field is through self-study attempts. The package which we have designed tries to address this issue and opens a road to becoming a data science professional.

Sign up!

Sign up for the Open University online courses Data Analytics Software and Machine Learning starting on January 15, 2019: https://web.abo.fi/fc/anmalningsdb/


Written by: Luigia Petre, Docent, PhD, Faculty of Science and Engineering at Åbo Akademi University.

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Updated 28.10.2019