If you don’t practice the skills you just learned, you won’t master them. Similarly, data analysis is no exception. You always have to tinker with data analysis tools. However, if you can work on real projects, you can take your skills to a unique level. Today, we’ll discuss the best practices in data analysis.
I always write based on my personal experience. My journey to mastering data analysis skills was very challenging. Those new to the field of data analysis can benefit from my experience.
To thrive in the world of big data, you must master different tools. One important aspect is domain knowledge. If you want to conduct data analysis on a topic you excel at, that field is the one for you. For example, a friend of mine was an HR professional who later became an HR data analyst after my guidance. He now has a vast network and has taken his career to the next level as an HR data analyst. Similarly, you can build a data analysis career based on your domain knowledge, such as medical data analysis, HR data analysis, business data analysis, digital data analysis, and so on.
Let’s begin practicing data according to our domain knowledge. This will enable us to work in any field. Here’s how we can start putting data analysis into practice.
After Learning Data Analysis Skills
In previous articles, we discussed the tools needed to perform data analysis. Today, we’ll discuss how to practice data analysis. For instance, you need to know some basic tools for data analysis, such as Microsoft Excel and Power BI. If you want to perform advanced work, you should learn Python. Based on these fundamentals, we will discuss below how to practice data analysis.
Work on Data Analysis Real Projects
Regardless of how much you practice data analysis, if you don’t work on real projects, you won’t gain good control over your skills. That’s why I recommend working on real projects to practice data analysis; these projects will boost your confidence. Many people wonder where to find real data. By “real projects,” I don’t mean taking data from any company or working for an organization.
For example, suppose I ask you to select 50 countries, find the unemployment rates for those countries, and prepare a report explaining why the last five countries have the lowest unemployment rates. This is a real project. By regularly working on projects like this, you will begin to master the skills effectively.
Kaggle, a platform for data science competitions, offers datasets and competitions for practice. You can also work on personal projects or contribute to open-source projects on platforms like GitHub.
Explore Data Visualization for Data Analysis
After analyzing data, visualizing it is a challenge. Have you ever made a mind map? If not, I recommend looking up how to create one. Similarly, when you turn numeric data into visualizations, you can make decisions more easily from a data dashboard. When practicing with data, create a data dashboard after completing the analysis.
How Google Can Help You Practice Data Analysis
Did you know that Google often collaborates to perform data analysis? You can use real data from Google to practice data analysis. Google Data Search and Google Public Data provide data sources for practice.
You can connect data from these sources and perform analysis. When practicing, document your work and post it on Kaggle or GitHub.
Uploading your files to these platforms creates your portfolio. Creating a practice data portfolio can enhance your career opportunities.
Conclusion
The more you work with real data and create projects, the more experienced you will become in the world of data. In truth, the projects you create will strengthen your portfolio. Thank you, and stay updated with us.