If data analytics is still a foreign topic to you, you are probably not the only one feeling this way.
However, should you still be feeling this way in 2021?
People who crunch data are not necessarily just data scientists who are phenomenal at math or brilliant at computer science and information technology.
There are probably way more people who have mastered some level of data science or analytics.
In fact, assuming data scientists are “unicorns” is the biggest misconception about the study of data science, said Teo Susnjak, a Senior Lecturer at Massey University of New Zealand’s School of Natural and Computational Sciences.
Teo did not expect to embark on data science studies. In fact, he was a competitive tennis player prior to taking up data science.
During his graduate studies, he was looking to combine tennis with computer science research which led to his journey with data science.
“This was actually before the term ‘data science’ came to exist,” he quipped.
Now, Teo leads the Massey University's Data Science specialisation and teaches its courses, as well as supervises Masters and PhD students in this area of study.
Regardless of what industry you are in, data analytics will come into use
Data runs our lives these days.
Each time we use a search engine and begin typing a term, we are presented with suggestions uncanny in their accuracy.
When we look at our junk email box, we see all the unnecessary emails we’ve been shielded from, with the limits to the accuracy of this technology occasionally becoming apparent.
“These data science-enabled solutions are so ubiquitous that we rarely stop and consider them anymore.”
Data is also being used in areas which are of consequential impact to our lives, he added.
For instance, the Los Angeles Police Department (LAPD) has been using updated crime data to make regular adjustments to needed police presence under operation LASER.
Doctors at University Hospital of Marburg’s Centre for Undiagnosed and Rare Diseases are testing out automatic diagnosis through a machine learning-based solution, which can provide diagnosis for patients within seconds.
One recent journal suggested that data simulation by data science researchers can possibly project the trend of covid-19 infections.
These are just some examples that Teo shared and the list can go on. It’s up to you to decide how to make use of data.
Now is the time
Teo said that now is perhaps the best time to equip yourself with data analytics skills as the pandemic-induced digital transformation across sectors will create opportunities.
Today, leading companies such as Intel are developing solutions for predictive data analytics to help healthcare systems to enhance patient care effectively and efficiently, Teo observed.
Other notable sectors that can tap on data to become more adaptive to the disruptions that Covid-19 brings are probably the supply chain and tourism sectors.
When the travel industry begins to recover in the near future, data science, automation and optimisation can possibly enable industry players to get ahead of competitors in understanding and serving the needs and wants of consumers better.
Armed with these essential skills, you will be able to contribute to the decision-making process or solve problems more effectively than others.
So how do you get started?
If you do a quick Google search with “data science” or “data analytics”, you will get a few suggestions for courses and bootcamps.
Those who are more disciplined and like some flexibility in their schedule can consider online options.
For those who prefer to be guided by a lecturer and can commit to a time slot, try signing up for courses offered by local academic institutions.
Time, cost and the depth of study are also factors you need to take into consideration.
There are introductory courses which only require weeks to complete or part/full-time courses that take up to a year or two and come with an academic qualification or professional certificate.
Additionally, you may also want to check up on the background of the lecturer of the course to determine if he/she is able to conduct the course in a relatable manner and ascertain his/her credentials.
For those who really want to put the knowledge into practice, it might be a better idea to spend more time on mastering this new skill.
How to prepare yourself for a data analytics course
Teo, who teaches the module "Introduction to Analytics" for the Master of Analytics programme by Massey University, conducted at PSB Academy, emphasised the importance of doing the analytic work through exercises and hands-on workshops for those who wish to upskill or do a career switch.
He compares learning data analytics with learning how to play a musical instrument.
“I tell all my students entering this programme that learning the skills we teach them is like learning to play a musical instrument. No one can learn to play the piano by reading books about how to play pianos.”
He further elaborated that this programme offered in Singapore through PSB Academy is designed for professionals who are looking to upskill or move deeper into the analytics space.
While the programme does not require any prior experience, he recommends potential students to brush up on basic statistics first.
For those who wish to get a head start, you can look for an online Python tutorial and get familiarised with such topics before the lessons start.
These are general, useful tips for anyone who has not done any programming before to make the most from the money spent on a data analytics course.
Master of Analytics programme at PSB Academy by Massey University
For those who are interested in the Master of Analytics programme, no prior knowledge in programming is required for interested applicants and you can complete it in 12 months (full-time) or 24 months (part-time).
The SAS Institute sponsors the use of SAS software in the Master of Analytics programme, hence students receive ample opportunity to use SAS software as an integral part of their curriculum.
Upon graduation, students will receive both a Master of Analytics degree and the SAS industry certification which adds more value to graduates’ skills portfolio.
The course will cover fundamental theoretical concepts in analytics as well as how to use statistical analysis software like SAS, programming in Python and R, as well as SQL (Structured Query Language).
Towards the end of the programme, you will get to put what you’ve learnt into practice with an applied analytics project.
Find out more about the Master of Analytics programme here.
This sponsored article by PSB Academy has opened the writer’s eyes to how data analytics have influenced her life.