The current digital world is increasingly becoming reliant on data, which has opened new doors for persons with relevant skills to collect and analyze data. According to the BLS, job positions that involve data analytics are expected to increase by 27% by 2026, this is the reason why a lot of good reputed institutes started providing Data Analysis Certification to help people upskill in their career. This means an increase in career opportunities for individuals with skills or those looking to learn data analysis skills.
However, the benefits of learning data analysis aren’t limited to future careers. Data analysis skills can help learners excel in school and is a hobby for students interested in math. Teachers can use the following tips to teach students data analysis.
1. Make Your Learners Comfortable to Participate
Like any other lesson, begin by creating an inclusive learning environment that makes your learners comfortable and free to participate. Creating such an environment ensures that students with and without technical backgrounds keep up with the lessons. Learning data analysis is challenging, and maximizing student participation can improve understanding.
2. Identify the Best Data Analytics Learning Tools
Using various data analytics tools can help students learn and understand faster. Note that these tools don’t necessarily mean items that students and professionals encounter in their careers. They include:
- Tools for Studying and Research
Microsoft Excel is a widely used data analytics tool for studying and research. It is a popular choice because most students can access it from school computers and at home. Excel also allows students and teachers to analyze big data collected using internal software programs. Students can also use the science journal app to collect, analyze, and interpret different external data types.
- Data Analytic Tools for Classroom Learning
Teachers should delve deeply into the four main data analytics categories: descriptive, predictive, prescriptive, and diagnostic analytics. Understanding these types of analytics helps students predict what may happen, what’s happening, why it is happening, and possible solutions.
For instance, learners can use diagnostic and descriptive analytics to evaluate recent weather conditions and determine the prevailing weather events. After analyzing this data, students can apply prescriptive and predictive analytics to make an educated guess of future weather events. Students can learn to do this via Microsoft packages in the form of STL Excel training in which they can pick up data modelling and analytical skills as well as a technical understanding of how to better use excel for data analysis.
That said, visual formats, such as charts, graphs, and images, are the best tools for students in a classroom environment. STEM students can build charts to aid their current studies and future careers. Google Analytics Academy can help advanced students master professional analytics skills.
3. Identify the Best Resources for Data Analytics Activities
Like programming, students should be subjected to rigorous data analytics activities to master analytical skills. Unfortunately, creating fun and engaging data analysis activities to be used in the classroom environment is difficult and time-consuming. Fortunately, teachers can leverage various free data analysis activities available online. These activities include:
- Data Analytics News Websites and Blogs
Several data analytics websites provide endless data analysis activities that allow students and IT professionals to sharpen their skills. They also provide updated blogs that discuss an array of data subjects and news that can help learners. Top websites include:
- Dataversity
- Data nuggets
- Data classroom
Whether you are teaching online or physically, these websites provide real data, enabling students to interact with data drawn from current issues affecting the world.
- Data Analytics Groups and Societies
Teachers can also expose students to various activities sponsored by data analytics groups and societies. These groups sponsor STEM competitions for data analytics, programming, and engineering students aged between 6 and 25. By doing this, learners are exposed to various learning activities, enabling them to sharpen their analytic skills.
The Bottom Line
Learning data analysis can prove complicated, especially if teachers don’t use the right approach, tools, and resources. As such, you’ll find students struggling to observe and interpret data sets even after several lessons. Using the tools and resources mentioned above can fast-track the learning process.
