Data Scientists Help Each Other

By | 29th October 2017

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According to the ‘theory of mind’ humans have the ability to adopt the perspectives of others. This can increase competition as much as it increases cooperation. Adam Waytz mentions the theory of mind in this short piece. You can also read further on Wikipedia and the scholar articles found on Google. Similarly I can see the pattern of cooperation when data scientists help each other.

Students helping students

From the first day at the university of Stirling our lecturers have been talking about cooperating learning. They want students to provide their own inputs in the class and help each other as we all come from different backgrounds.

Student backgrounds

It is worth mentioning that most of my classmates are experienced professionals. Everyone has worked in different fields such as banking, media, oil & gas, statistics, social workers, the list goes on.

All of those sectors seem to have data in common; more and more industries are starting to use data science to help them drive informed decisions and the lack of data scientists provides an opportunity for many to change their career.

If you’re interested to know more about the demand of data science related roles, this Forbes article is providing a nice summary of The Quant Crunch: How The Demand For Data Science Skills Is Disrupting The Job Market, published by Burning Glass Technologies, Business-Higher Education Forum (BHEF), and IBM.

Implementation of student collaboration

One day, during the first couple of weeks of the academic year, one of my classmates (Jason) and I were leaving the gym together and Jason asked me if I’m interested to work on projects so we can practice technical skills such as Python and SQL. I said yes.

Forming a group

After a short conversation we decided that we can ask our classmates if they want to meet once a week and help each other with different tasks within a project. As we expected most of our classmates were interested so Jason and I are holding weekly sessions since.

So far we’ve helped each other with assignments, Jason introduced to the group why we think that we need to learn how to use Python, SQL and Github, and right now I am preparing a short session to introduce people to Python and Github.

Data scientists collaborate outside the University boundaries

My experience so far is that data scientists are very passionate about what they do. We’re very few in the field and we want to help each other with every chance we get.

The hackathone

A good example is my hackathone experience. Product Forge in collaboration with The Data Lab, the Edinburgh Tourism Action Group and the Scottish Enterprise organised the Edinburgh Tourism Innovation Challenge on the 5th of October 2017.

The Edinburgh Tourism Action Group (ETAG), wants to utilise data to help them improve the tourists experiences in the city, increase the tourists numbers, particularly from emerging markets, and motivate tourists to expand their visits to places outside the hot-spots such as the Royal Mile and Princes Street.

My personal challenges during the hackathone

During the hackathone we had to form teams with people we didn’t know prior to the event. We also had to work with datasets unfamiliar to us prior to the event.

I had a clear goal, be the only data scientist in a group and challenge myself to practice data science.

I had no prior experience working as a data scientist, I have just started my studies, but I wanted to challenge myself to learn. As soon as the team decided what problem we were addressing and what solution we wanted to propose and build, I had to figure out how to use data to illustrate:

  1. The problem we were addressing is a real problem.
  2. How we can understand it better further use of data.
  3. Why the solution we propose is valid.

The problem we addressed and an example of my work

The problem we decided to tackle was:

“Overcrowding is damaging city reputations, delivering poor visitor experiences and impacting negatively on city residents. Globally!”

Our solution was

“To Digitally Help City Planners Manage People Flow and Improve The City Experience For Visitors and Locals”

Proving that overcrowding is real

ETAG’s representatives theorised that most tourists and locals, spend most of their time visiting attractions and businesses on hot-spots such as the Royal Mile and Princess Street. Based on personal experiences our team assumed the same but we wanted to create a theory based on data.

To provide a prove based on some data I chose to use the data provided by IntechnologyWifi the company that provides free internet in Edinburgh city. With the help of data science mentors such as Martina Pugliese of Mallzee I managed to use Python to organise the data available to me. With the help of Mycchaka Kleinbort a data scientist who is very good with Python and visualisation, I used Tableau to create the map below.

Edinburgh Wifi login Hotspots

Using wifi login data we we are able to get a “historical” idea of where people spend their time when in Edinburgh.

Without the help from the fellow data scientists I wouldn’t be able to produce the results I wanted to and I am wish one I will be able to give back to other data scientists.

The hackathone in general

The hackathone was fun in general and all attendees were smart and helpful, not just the data scientists. Check the vlog below for some behind the scenes conversations with the hackathone part-takers.

 

More hackathones to come

With a plethora of hackathones throughout Scotland and the UK I will try my best to attend more data science related hackathones and push myself to better myself.

I suggest that you attend a hackathone at-least ones. It will be an experience which will provide you with new knowledge, new ways of thinking, new friends and network.

 

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