4 Big Data trends for higher education in 2021
By James (Jiaqi) Wang, 20 April 2021
Big Data is undoubtedly a very hot topic in recent years, and many industries are using Big Data to analyse and improve their performance. For higher education institutions, Big Data is also very important and relevant. Higher education institutions can use Big Data to improve the efficiency of recruitment and the quality of teaching. In addition, courses related to Big Data are becoming more and more popular. Here are 4 Big Data trends for higher education (HE) in 2021.
#1 HE institutions can use Big Data to improve recruitment efficiency （from the viewpoint of HE institutions）
Big Data from current and previous students such as historical performance and demographics could be used to increase efficiency of recruitment. After collecting the Big Data, higher education institutions can analyse Big Data to come up with a prospective student profile. From there, higher education institutions can use Facebook, Wechat, and other platforms to place target ads to increase the efficiency of recruitment.
Source: QS
#2 HE institutions can use Big Data to enhance retention and teaching quality（from the viewpoint of HE institutions）
By using Big Data and predictive analytics, HE institutions can track the progress of students within their given major and career goals and assess the likelihood of them being successful versus dropping out. If students are flagged early on in their educational career, advisors can reach out and attempt to correct the course before it becomes a major problem. Sometimes this involves providing customised learning plans and other times a suggested change of major is in order. At IMC, if a student fails the same subject twice in a row, the Student Service advisor will talk to the student and provide advice.
In addition, at IMC, we also encourage students to express their suggestions and satisfaction of teachers to the academic department. The relevant department will examine these data and give awards to teachers who perform well.
Source: inside BIGDATA
#3 Big Data is widely used and will broaden your horizon （from the viewpoint of students）
Learning Big Data is a rewarding and fun investment of your time. The domain of Big Data and data analysis in general is full of puzzles to solve, and will greatly enhance your analytical skills. After you have learned Big Data for sometimes, you will notice that you will start applying ‘Big Data’ techniques into your presentations or reports, because they provide a very solid basis for discussion and decision making.
In addition, Big Data professionals are not restricted to work for just a few industry segments but their contribution is for lots of different industries. Many different industries are in need of Big Data talent. For example, finance, manufacturing, retail, logistics to name a few. Data-driven decisions are very important for many industries.
Source: Enterprise Big Data Framework
#4 Studying Big Data can help boost your career prospects（from the viewpoint of students）
For many people, when choosing a major in higher education, high significance is placed to the job prospects of that major. For Big Data, as mentioned above, the discipline is widely used, and more companies are recognising the importance of data driven decisions, so the job prospects for Big Data majors are very compelling. According to Salary Expert, in Australia an entry level Big Data engineer (1-3 years of experience) earns an average salary of $107,324. On the other end, a senior level Big Data engineer (8+ years of experience) earns an average salary of $191,460. At IMC, we offer a number of Courses related to Digital Applications, including the Master of Big Data Auditing.
Source: Salary Expert
Disclaimer: While we make every effort to ensure that we accurately represent all figures and potential income, it should be noted that the salary figures are estimates of what we think you can possibly earn. There is no guarantee that you will make these levels of income and you accept the risk that the salary differs for each individual.
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