These are questions that global leaders are trying to answer as the development of skilled data professionals becomes a priority for countries striving to build new and improved future economies. In fact, LinkedIn listed Statistical Analysis and Data Mining as the number two skill in their list of the “25 Professional Skills That Will Be Hot in 2016,” and a joint study from Burning Glass Technologies and General Assembly reported that demand for data science skills has tripled over the past five years. In Singapore, the government has recently launched the TechSkills Accelerator, a skills development and job placement hub for the information and communications technology (ICT) sector. In general, this is part of a continued push for the development of IT skills among students and workers, along with the growing accessibility of data for everyone which is in line with the country’s Smart Nation initiatives.
Analytics in the classroom
There is no doubt that data analytics has a place in today’s classrooms and school curriculum. Data analytics is a powerful tool. When put to good use in educational institutions, data analytics empowers educators and students to find answers, identify trends, make projections, and basically see and understand their data.
In fact, educators have long used data analytics to track student performance. With data analytics, educators can better identify weaker students and determine what steps to take to help these students improve. Many school administrators use analytics too, for managing and tracking budget, registration numbers, and resources.
For students, it is crucial that they get access to analytics tools and learn adequate data analytics skills. Data skills are already much needed in today’s workplace. According to the Infocomm Development Authority in Singapore, 15,000 technology professional vacancies could not be filled in 2014. The authority also projected that another 15,000 technology specialists – among them data professionals – will be required by 2017. The need will only continue to grow.
Getting to the answers
Most importantly, data analytics teaching cannot be a class on computer programming. It is key that students get enough time to actually analyze data, ask questions of their data and find their own answers. They should not spend most of the course on figuring out or learning how to use their analytics tools.
This is why classroom analytics needs to be easy to learn, with simple drag-and-drop technology, and the capability to handle both big and small data sets. Students and teachers need the ability to create and share interactive dashboards quickly. They need to have access to technology that they will be able to use on their own, without help from an IT department or a computer science degree.
Seeing and understanding
At Tableau, we say that ‘a picture is worth a million rows of data.’ We focus on making insights come alive with data visualizations that help people communicate complex ideas simply. Expressive data visualization goes beyond static charts to create multi-faceted views of data. Add that expressiveness to easy-to-master, drag-and-drop navigation and you have a powerful data analytics tool that everyone can use.
This is extremely applicable in the classroom too. Educators often use visual aids to help students grasp difficult concepts. Taking this a step further, modern day analytics software makes data easy to understand and derive meaning from. For students, working with their teachers to visually analyze data helps them derive true meaning and easily find the answers in their schoolwork.
Success in the process
Schools in Singapore have already begun making data analytics part and parcel of student life. One example of how analytics has been successfully introduced into a teaching curriculum can be seen at Nanyang Polytechnic (NYP), a leading institution of higher learning in Singapore. When the lecturers at NYP’s School of Information Technology first introduced data analytics into its curriculum in 2011, they were conscious that they want to allow more time for the students to get actual hands-on data analytics experience, and not have them work on complex, traditional data analytics tools, which require quite a bit of coding.
NYP has had much success with their data analytics program since then. Students who have graduated from NYP, and have already started working, have commented that what they picked up in these classes were the most useful skills they have learned for their future jobs.
Furthermore, the number of students who chose the data analytics modules have also grown significantly. Lecturers from other faculties, such as those from Business and Engineering, have also caught on and started experimenting with adding analytics into their classes. The lecturers themselves have started to use analytics tools for their administrative work.
Future ready data skills
Data analytics used to be specialized work that only trained analysts or data scientists engaged in. Not anymore, as data analytics tools are simpler to learn and deploy.
At the same time, data is becoming more accessible and more dependable to the everyday, non-technical user. In today’s workplace, workers outside of IT are demanding deeper, more meaningful analytics experiences. They are looking to data to help them find answers on sales performance, market projection, operation efficiency, resource optimization, and much more. Businesses are increasingly adopting platforms that allow everyone to apply statistics, ask a series of questions, and stay in the flow of their analysis.
Data analytics will soon serve as a common language, empowering people to reach insights quickly and collaborate meaningfully in data-smart communities. It will be important for non-analysts to have data analytics skills.
This is why it is important that we help students get comfortable working with data and analytics in their learning journey. The key is for students to gain the experience and confidence in working with data. We can only achieve this by offering them the opportunities for hands on experience in actualizing data, discovering insights and finding answers quickly.
