Information is the oil of the 21st century, and analytics is the combustion engine. – Peter Sondergaard
In data analysis, data by itself is without form and is void. The process of cleaning up data, transforming it, and redesigning it provides inestimable value to the data. In the recently concluded 1st semester class on data analysis, the true form of data and how it can be presented and represented were taught.
At the beginning of the class, it felt like a herculean task: understanding the formulas and steps to analyse data. However, changing one’s perspective on how data is viewed significantly changed the learning curve, making it an easy-to-understand subject. Data analysis is a crucial skill in today’s digital age, where vast amounts of information are generated daily. Without the ability to make sense of this data, it remains just raw numbers and figures. However, by learning the techniques and tools of data analysis, one can unlock its true potential and uncover valuable insights that can drive decision-making and lead to success in various industries.
One of the most important elements of data analysis is to first determine the purpose of the data and the type of result required. Data such as population census or a company’s sales over a period of 10 years could be presented, it is important to first identify and understand the required report or result before analysing the data. Once the purpose and desired result are identified, the next step is to gather the necessary data. This can involve collecting data from various sources, such as databases, surve
ys, or online sources. Once the data is gathered, it needs to be cleaned and organised to ensure accuracy and consistency. This includes removing any duplicate or irrelevant data, correcting any errors or inconsistencies, and structuring the data in a way that is understandable and easy to analyze. With the data cleaned and organised, various statistical techniques and tools can be applied to analyse and interpret the data. This can include methods such as regression analysis, hypothesis testing, or data visualization. By applying these techniques, patterns, trends, and relationships within the data can be identified, and insights can be extracted.
Furthermore, the art of analysing the data using the best analysis tool for the pre-set goal is next. In data analysis, visualisation is an important art. Visualisation provides a quick and easy to understand reporting format. The use of colourful charts and graphs to present a 10-year report makes engagement and presentation easier.
There are about three types of data analysis. Data can be predictive, prescriptive, or diagnostic. Predictive data analysis helps to give future projections, while prescriptive data analysis reviews data and provides a possible set of solutions to an identified problem. Diagnostic data analysis tries to identify why a certain occurrence happened and what can be done to repeat such success or prevent such failure.
“If we have data, let’s look at the data. If all we have are opinions, let’s go with mine” – Jim Barksdale
AND SO IT BEGINS