How to Master Essential Hard Skills for the Data Analyst
The Learning Pyramid, developed by the National Training Laboratories in 1969, suggests that learners retain more information when they are actively involved in the learning process. According to the pyramid, learners remember 10% of what they read, 20% of what they hear, 30% of what they see, 50% of what they see and hear, 70% of what they say and write, and 90% of what they do (Dale, 1969).
Learning data analysis can be a challenging task, too much to learn, too much to remember. In this article, I am attempting at discussing the best ways to learn essential data analysis hard skills such as SQL, Python, Excel, R, PowerBI, Tableau and other tools, by focusing on Bloom’s Taxonomy of levels of learning in psychology.
1. Remembering and Understanding
The first step of the Bloom’s taxonomy is to understand the basic concepts and terminology related to the field you’re studfying.
One way to do this is by taking online courses, reading books, or watching video tutorials. Platforms such as Coursera, edX, and LinkedIn Learning offer a wide range of courses on data analysis, data science, and related topics.
For example, the Data Science Fundamentals course on Coursera provides an introduction to data science and covers topics such as data exploration, data visualization, and basic statistical analysis.
Another way to understand the basics of data analysis is by reading books. For example, “Data Smart” by John W. Foreman provides a practical guide to data analysis using Excel and covers topics such as regression analysis, decision trees, and clustering. Similarly, “Python for Data Analysis” by Wes McKinney provides a comprehensive guide to data analysis using Python and covers topics such as data wrangling, data visualization, and machine learning.
2. Applying
Once you have a basic understanding of data analysis, the next step (third step of Bloom) is to apply your knowledge to real-world situations. One way to do this is by working on data analysis projects.
If you are interested in finance, you can work on a project that involves analyzing stock prices or predicting market trends. If you are interested in healthcare, you can work on a project that involves analyzing patient data or predicting disease outbreaks.
Participating in data analysis competitions and hackathons is another way to apply your skills. Platforms such as Kaggle and DataHack offer a wide range of data analysis competitions that allow you to work on real-world problems and compete with other data analysts. These competitions provide an excellent opportunity to apply your skills, learn from others, and build a portfolio of projects.
3. Creating
The final step in learning data analysis is to create something new using your skills (final step in the Bloom’s taxonomy). One way to do this is by sharing your knowledge with others.
You can create video tutorials, write blog posts, or teach a course on data analysis. Teaching others is one of the best ways to learn because it requires you to have a deep understanding of the subject matter.
Creating, teaching, and sharing are the best ways to learn essential data analysis hard skills such as SQL, Python, Excel, VBA, and PowerBI. These skills are highly technical and require a deep understanding of the concepts and the ability to apply them to real-world situations. Creating, teaching, and sharing these skills can help solidify your understanding and improve your ability to apply them in practice.
One of the main benefits of creating, teaching, and sharing data analysis skills is that it requires a deep understanding of the subject matter. To teach a topic effectively, you need to be able to explain the concepts in a clear and concise manner. This requires you to have a solid understanding of the underlying principles and the ability to communicate them effectively to others. By teaching and sharing your knowledge, you can improve your understanding of the concepts and refine your communication skills.
Activate Your Learning
Research has consistently shown that active learning, which involves students engaging with the material through activities like creating, teaching, and sharing, is more effective than passive learning. A meta-analysis of 225 studies by Freeman et al. (2014) found that students in active learning classrooms scored higher on exams and had a greater understanding of the material than students in traditional lecture-based classrooms.
Teaching Helps You Solidify your Understanding and Identify Gaps
Creating tutorials, videos, and blog posts is another way to solidify your understanding of data analysis skills. By creating content that explains the concepts and provides examples of how to apply them, you can reinforce your own knowledge and help others learn at the same time. Creating content can also help you identify gaps in your understanding and areas where you need to improve.
Teaching Helps You Develop a Portfolio of Projects
Furthermore, teaching and sharing data analysis skills can also help you develop a portfolio of projects that demonstrate your skills and abilities. For example, if you create a series of tutorials on how to use SQL to analyze data, you can use these tutorials to showcase your skills to potential employers or clients. Similarly, if you teach a course on Python for data analysis, you can use the course materials as evidence of your expertise in the field.
Teaching Helps You Stay Up to Date
Another benefit of teaching and sharing data analysis skills is that it can help you stay up to date with the latest developments in the field. By following blogs, attending webinars, and participating in online communities, you can stay informed about the latest trends and techniques in data analysis. This can help you identify new opportunities to apply your skills and stay ahead of the curve in your career.
Teaching Improves Communication, Collaboration, and Leadership
In addition to these benefits, creating, teaching, and sharing data analysis skills can also help you develop soft skills such as communication, collaboration, and leadership. By working with others to create content or teach courses, you can develop your ability to work in a team and lead others. Similarly, by communicating with others about data analysis, you can improve your ability to explain complex concepts and work effectively with others in a professional setting.
In conclusion, creating, teaching, and sharing data analysis skills is the best way to learn essential hard skills such as SQL, Python, Excel, VBA, and PowerBI. By teaching and sharing your knowledge, you can solidify your understanding of the concepts, develop a portfolio of projects, stay up to date with the latest trends, and develop soft skills that will help you succeed in your career. Whether you are a beginner or an experienced data analyst, creating, teaching, and sharing your skills is an effective way to improve your knowledge and skills in data analysis.
If you want to know alternative ways to teach (and monetize) your data analytics skills, join my The Data Analyst Toolkit free email course. You’ll get to teach to improve your skills, make money, and help others be more proficient as well.
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