Data examination. SQL. Power BI. These tools and skill sets have grown in popularity over the last decade as businesses increasingly rely on data to drive crucial business decisions and strategies. Learning these talents is becoming increasingly important as data analytics-related skills grow increasingly in demand across businesses. Fortunately, there are numerous online courses available to teach the principles of data analysis, preparation, and visualization.

Fundamental data analysis does not necessarily mean you need to learn complex mathematics and specialized domain knowledge such as machine learning, linear regression, or artificial intelligence (unless your goal is to be a data scientist). You can do plenty of data analysis if you’re familiar with tools like Python and Excel. 

Below, you’ll find the following skills are excellent for both data analyst aspirants and professionals looking to round out their analytical thinking abilities. 

1. Data visualization

Data visualization, or the graphical representation of data, conveys a story with large data sets. Data analysts often use visualization tools like Tableau or Google Charts to create charts and graphs to communicate data.

Data visualization skills are critical for most major business functions. Marketing professionals who run campaigns, for example, are generally expected to present their results and key findings in an easily digestible manner. 

2. Programming

Data scientists and analysts often use programming to compute complex equations or scrape data. But programming has other applications and uses, even if you aren’t trying to analyze massive data sets. One of the best uses of programming is to automate tasks. 

The most popular programming languages for data science professionals are Python and R. Python is generally considered the most beginner-friendly programming language because of its easy-to-read syntax and ability to support many of today’s technologies. 

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“For beginners, I would suggest Python (or similar scripting languages) to start with. It is an easy language to pick up, highly productive, and also is useful in many practical situations such as data analysis,” said Dr. S.M. Yiu, professor at the Department of Computer Science of the University of Hong Kong.

3. Spreadsheets

According to Forrester Research, over 750 million people use Excel spreadsheets. While that number seems surprising, there are many reasons why spreadsheets have retained their popularity. Spreadsheets are versatile tools that can perform data manipulations, data processing, and even create data visualizations.

Get educated. Many outlets exist to help educators learn about this subject. Take advantage of data analytics information and conferences offered by national organizations, or boot camps provided by firms such as KPMG, Deloitte, PwC, and EY. Use these resources “as a way to see what other academics are doing right now,” said Felski. The AICPA also offers certificate programs in data analytics, with discounted pricing for educators.

Don’t expect perfection. If you’re new to teaching analytics, expect the road to be bumpy, and be flexible not only with your students but also with yourself, said Zimmerman.

Start with Excel. Pickard advocates starting with the more familiar Excel, which he calls the “entryway” to data analytics and computer programming. In Excel, students can learn to merge datasets, clean up data, and understand concepts like data aggregation. “Pivot tables are one of the best kept secrets of Excel,” Pickard noted. “You can use them to teach how to answer questions with data.” 

Get students comfortable with tech. Start by requiring students to complete tutorials for the analytics software you’re using before giving other assignments, Zimmerman suggested. She assigns IDEA and Tableau tutorials first and then asks “students to submit screenshots indicating completion and answers to tutorial questions,” she said. She next assigns a group case that gives students a chance to “use the software to solve auditing problems.” Zimmerman also assigns LinkedIn Learning videos to help students navigate Tableau. You can also enlist a former student familiar with the assignments to help current students troubleshoot the technology, she said.

Conduct live workshops for students. Dzuranin hosts workshops on Blackboard Collaborate and Zoom where she demonstrates the technology and facilitates group discussion on cases and assignments.

Make classes fun. While teaching remotely poses challenges, the best way to keep students engaged is to make classes interesting and enjoyable. Choose topics or cases on companies that students can relate to, such as Uber, Amazon, and Netflix, Dzuranin suggested.

Zimmerman brings in guest speakers from public accounting firms to talk about how they use software tools on the job. “Students are more excited and motivated when they know it’s being used in practice,” she said.

Find new sources of data. Felski’s students glean data provided by the Big Four firms, such as Deloitte, and tap sites such as Makeover Monday, a U.K.-based website that continually offers new datasets. Her students choose datasets that appeal to them and then redo the visualizations. 

Pickard gets datasets from Kaggle.com. Many cities, such as Chicago, have open data portals as well, he said. 

Let students interact. While teaching remotely, Dzuranin puts students in small breakout groups to work on data analysis problems. “When we are back in one group, I ask teams to share their screens and walk us though their analyses,” she said. “It’s a great way to get students excited about the material. It’s also a great way for students to share insights and tips.”

Don’t lose sight of critical thinking skills. Developing critical thinking skills can be as important as learning different software programs, noted Dzuranin. “Critical thinking is the foundation because no matter how good a student is at using technology if they can’t identify the question, the analysis won’t matter,” she observed.

The real added value to learning data analytics skills is to derive solutions to real-world problems that businesses are facing. Data analysis solves high-level problems such as figuring out how companies can generate more revenue, create more efficiencies, or identify ways to save money. 

Of course, you do not need to start solving these complex problems right away. You can try tackling more minor business problems you encounter at work. Some examples of these include: 

• Creating reports for management
• Building dashboards to automate manual data analysis
• Identifying trends within large data sets using tools like Excel