Insights from a data analysis course

Do you know which movies are more popular? What is the budget required produce the movies in each genre? What is the primary cast of a given movie? If you are curious about such questions, you can find the answers through data analysis. This is just one of the examples where you can ask questions about your favorite topic and seek answers through real-time data.

This image is snapshot of the Tableau viz (visualization) which I had been working on for a course assignment. I have been studying the Google Data Analytics certificate course provided by coursera.org since July and I am near the last few laps of this marathon. Throughout this course, I have learned to make sense of the data and transform it into stories like the one I illustrated above. You are free to visit my visualizations on?https://public.tableau.com/app/profile/janaki1149. These courses within the certificate program have really encouraged me to perform well, challenged me to deploy analytical skills to solve problems and has provided resources to satisfy my curiosity.

However, I have also undersood some important concepts along the way, which are the key takeaways for this post.

• Learning path – The initial courses are recommended to be studied in a?linear way, to understand the logic and build upon the concepts. But towards the end, it is more?iterative.
• The analysis and visualization is only as good as the cleanliness and integrity of the data, but the insights from data analysis derived depend solely on asking the right questions.
• You are the author of this data story and the responsibility of collecting data, preparing it, cleaning and analysing rests entirely upon you.
• Always ensure that the data you use is from primary source and generated reliably to ensure credibility.
• However good the dataset looks, it always need some preparation such as formatting, filtering, and sorting before you continue.?
• Each analysis tool has unique features and capabilities and hence it is also necessary to use all the required resources sometimes to arrive at a conclusion. (For example, for one of my exercises, I ran a query in BigQuery -a SQL program to clean a large dataset and analyze to generate a custom table. I exported the results to Google sheets, formatted the data, and generated pivot tables. I uploaded this spreadsheet to Tableau for further visualization.)
• It is possible that another analyst might derive more insights from the same dataset and present a different story. So consider it a great learning opportunity.

As many of you agree, data analysis has many advantages to offer in business, science, health and well-being, to name a few. There are also many programs, tools, and processes that fill the repertoire of a data analyst. Hence, it aids evidence-based decision-making in a complex and dynamic global setting.