To many parents, data analytics sounds like a skill that would be required by students aspiring to a STEM career. Analysing data is the stuff of maths and engineering, right? According to former university mathematics professor and Hong Kong tutor RAYMOND LEUNG, that inference is incorrect. Here, he sheds some light on why parents of senior students should ensure this skill is mastered.
#1 Big data, big business
“In recent years, with the rapid development of the internet and mobile technology, data has accumulated extremely rapidly, providing new opportunities and challenges for research and practice in the humanities and social sciences. Big data analysis is gaining more and more attention in various fields,” explains Raymond. A report released in January 2019 by jobs site Indeed showed a 29% increase in demand for data scientists year over year. In fact, since 2013 the demand has more than tripled.
Understanding this data analysis will be important, even in non-analytics roles. Why? Because big data focuses on analysing large amounts of information to reveal patterns, trends and associations, especially relating to human behaviour and interactions. This all points to the notion that data analytics will soon rear its head as a necessary skill in any big business role. If our kids want top jobs in today’s world, they’ll need to understand the basics.
#2 Humble humanities
A lack of understanding in data analytics can be the stumbling block of student success in the humanities, according to Raymond. “Needless to say, data analysis has been playing an important part in scientific research. However, nowadays, it is increasingly being found to be crucial in humanities studies and social science too. In fact, PhD candidates in almost all aspects have to learn statistics in research methodology.”
It’s at this point that Raymond often begins to help guide students. “I recall one student, Bo, who is a psychology master’s student. Her research question was: The relationship of smartphone usage on work engagement and stress among finance workers in Hong Kong. Her analytics experience was limited, so at the master’s level she needed to seek my help to test the relationship and analyse her quantitative data.”
#3 Start early
When it comes to maths, teaching an old dog new maths tricks can be a struggle. “We believe that maths and science training are more effective when developed at a younger age,” says Raymond. Emily Cairns, writing for the British Council, shared this point: “Studies have found that children are more open to learning at a young age (five or six), and by successfully teaching the fundamentals of maths at this stage in their lives, children find it easier to develop further maths skills in later life.” Five years old is a bit early to study big data, even in Hong Kong, but continuing to lean into maths through high school can help in the long run.
Raymond also believes mastering maths opens more opportunities for students once they’ve reached University. “Secondary students with a strong ability in maths, science and statistics have comparative advantages. Maths and science subjects are more objective subjects. Secondary students in a maths and science stream have more stable scores. Once they’re granted a college offer, students are able to pursue their field of interest, and are open to more choices in the majors they pursue.” Raymond is also of the belief that it can be more difficult for humanities secondary students to switch to science fields.
It’s all very interesting information to consider for parents of secondary school students. If you’d like to learn more about Raymond and how he can help, visit eAsy Tutor A Pros.
Keen to dive more deeply into data analysis? Raymond recommends Statistics Made Easy with Graphing Calculators, an upcoming book by award-winning educator Professor Thomas Hu at HKUST. The text applies graphing calculators with Computer Algebra Systems (CAS) to make traditional topics far easier to tackle than manual methods that rely on hand calculations with ordinary calculators. It details efficient techniques for performing statistical computations, making it an essential source for both the theoretical and practical sides of statistics and probability.
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