Data is a new red diamond. When there is a conversation about data, many things need to be clarified about data analysis, data engineering, data science and artificial learning towards machine learning. For example, have you ever thought about the job titles of data analyst, data engineer, data science, data analytics and machine learning? And what are the differences between these titles? Of course, it will also show how scientific researchers and actuarial analysts relate to those titles. This article post will briefly describe above the designation titles, which will help to decide which career path you should start to work with data.
People who work with data are known by different titles. I have shown them in the following diagram:
Data Analysis means the data collection, organisation, and transformation of data to draw conclusions, make predictions and drive informed decision-making. And someone who collects, transforms and organises data to draw conclusions, make predictions, and drive informed decision-making is called a Data Analyst. A good data analyst must have to know the statistical methods and techniques. In this case, Data Analysis systematically applies logical and statistical techniques to describe, illustrate, condense, recap, and evaluate data.
Nowadays, every organisation needs data analysts. Hence, data analysis jobs became more demandable.
According to Google, there are six data analysis steps or phases that shows in the following figure 1.1:
