Part 1:DATA ANALYSIS TECHNIQUES

What is Data Analysis

Before we dive into the realm of data analysis techniques, let’s take a moment to define data analysis.

To be completely candid, there’s no one-size-fits-all answer to define data analysis, but I can provide a perspective that aligns with the role of a Data Analyst.

A Data Analyst, much like professionals in other fields, tackles problems and phenomena by harnessing the power of data. This process encompasses data collection, cleansing, analysis, and reporting.

Beyond the charts, graphs, numbers, and tables, the core objective of a Data Analyst is to unveil insights. Being a Data Analyst involves substantial critical thinking, and I often refer to it as being a “data detective,” someone who approaches data with a probing mindset.

It’s worth noting that the world of data expertise is vast and includes roles like Data Scientists, Data Engineers, Business Analysts, Research Scientists, UX Researchers, and more. While the roles of a Data Analyst may overlap with these other data experts, the principles I will be sharing can benefit those interested in various data-related fields.

Even if you don’t aspire to become a data expert, the knowledge shared here can empower you in your interaction with data, as we are all part of a data-driven world. If you’re not interested in data, you’re not interested in yourself.

Please feel free to contribute your thoughts in the comments section, and the link to the previous post will be available in the comments for reference.