What is Data Analytics?
The field of data analytics is primarily focused on finding answers to questions about data. This involves devising methods to process, clean, and organize structured data so that statistical analyses can be run and key insights can be found. By finding answers to these questions, a Data Analyst is able to produce actionable insights that can yield immediate improvements. This process involves using a combination of computer programming, statistical analysis, and data visualization software.
How is Data Analytics used in Academia?
Educators and administrators rely on data analytics in a variety of ways to improve their students’ learning experiences. In particular, four kinds of data are collected and analyzed to shed better insights into how students, as well as educational institutions, are performing:
- Student data such as gender, age, or ethnicity is collected. In addition, information such as if a student is studying online or in-person, as well as if they are part-time or full-time, is often analyzed.
- Course data like attendance records, completion rates, and final grades are often collected.
- Instructor data pertaining to variables like salary, productivity, and age can help provide insights.
- Facility data about classroom use and resource allocation provide insights into how best to optimize facilities on campus.
The world of academia relies on these four kinds of data to make important improvements to all levels of their educational organization. The following are just some of the many ways big data analytics can play a vital role in academia:
- Intelligent curriculum development: Data analytics provides a way for educators to adapt course content to meet the needs of individual students. By collecting student data, specific skill sets and preferences can be noted, which allows educators to modify learning systems in order to create systems in which students could study the same content in different ways.
- Data mining: Predictive models can be created that can help indicate which students are most at risk of dropping out of a course. This allows educators to offer the necessary support and interventions to help these students stay enrolled and meet their learning goals.
- Management: In terms of allocating educational resources and aiding with decision-making, data analytics provides a helpful tool to streamline efforts. It can help identify which facilities students use most at a given institution, as well as those that are not as essential to operations.
- Enhance student success: Students benefit from data analytics in that it can provide insights into their unique learning habits, and identify places where they can improve.
- Improve pedagogical approaches to learning: By focusing on data related to student success and allotting the necessary support to ensure this success, an educational institution can better customize its teaching strategies and learning models to the resources and classes best suited to increase student engagement with course material.
- Improved parental involvement: Data collected about students in elementary, middle, and high school can be evaluated based on performance standards. Once information is collected, parents can be notified about various issues that may affect their child’s performance in athletic or academic endeavors.
- Enhanced focus on non-academic factors like mental health: When students get to college, they face many challenges that extend beyond the academic arena. Some students experience homesickness or have a hard time fitting into the campus culture. By studying big data, administrators can develop problems focused on improving students’ engagement, which can ultimately make the transition to college life easier and more productive.
- Updating software: Data analytics helps schools evaluate how successful the sorts of software its students use is for their learning. Doing so allows them to offer improved digital resources that are better suited to meet the needs of both students and educators alike. This can cut down on costs as well as ensure a better user experience.
- Course assessment: By mapping students’ knowledge domains that indicate if a student can successfully apply a given concept, teachers can have a clearer idea of what students have learned in their course that extends beyond simply getting answers correct on exams.
- Promoting online education: By looking at statistics, college administrators can get an idea of which students could work in an online educational setting rather than in-person. Not only does this allow colleges to promote their online degrees, but it also can lead to an improved educational experience for students, one that involves AI tools to deliver better remote educational outcomes.
- Improve decision-making: Incorporating predictive analytics into the world of academia has many benefits. This branch of analytics relies on historical data to help educators and administrators make choices about the future. When used at the university level, predictive analytics aids with retention, better recruiting efforts, and more successful learning outcomes for students.
The Future of Data Analytics in Academia
The COVID-19 pandemic has led to a drastic decrease in enrollment numbers at colleges and universities. An estimated 86% of college presidents consider these dropping numbers to be the paramount concern they must face. Big data has provided a way to help higher educational institutions address enrollment concerns, as well as other challenges they are facing.
Society’s current reliance on big data, in academics and beyond, is likely to continue to increase in the future. In order to best leverage this information to help students, faculty, administrators, and educational organizations meet learning outcomes and financial goals, it’s important that those working in the educational setting prioritize the importance of this data and the analytics process. They must focus on how the insights gathered from this data can improve the entire educational process, from applications and recruitment through alumnus targeting efforts. By creating and fostering a culture of data-driven decision-making, schools, colleges, and universities will be better able to collect meaningful information, share it with various stakeholders, and use it to drive improvements that will benefit everyone at the institution.
In the future, it’s likely that the IT infrastructure at schools will continue to change to meet the needs of students, administrators, and faculty. A greater reliance on cloud-based computing and storage, as well as advanced analytic tools, is expected in the future.
Begin Learning Data Analytics with Hands-On Classes
Do you want to learn more about data analytics? If so, Noble Desktop’s data analytics classes are a great starting point. Courses are currently available in topics such as Excel, Python, and data analytics, among others skills necessary for analyzing data.
In addition, more than 130 live online data analytics courses are also available from top providers. Courses range from three hours to six months and cost from $219 to $27,500.
Those who are committed to learning in an intensive educational environment may also consider enrolling in a data analytics or data science bootcamp. These rigorous courses are taught by industry experts and provide timely instruction on how to handle large sets of data. Over 90 bootcamp options are available for beginners, intermediate, and advanced students looking to master skills and topics like data analytics, data visualization, data science, and Python, among others.
For those searching for a data analytics class nearby, Noble’s data analytics Classes Near Me tool provides an easy way to locate and browse the 400 or so data analytics classes currently offered in the in-person and live online formats. Course lengths vary from three hours to 36 weeks and cost $119-$27,500.