The Data Analyst is the one who plays the game with the large sets of data (aka) Big Data! The everyday lives of data analysts revolve around analysing an enormous amount of data available in various forms.
Data Analysts are responsible for providing relevant insights out of the interpreted data which in turn aids the organization in decision-making and solve complex business problems.
All data scientists are data analysts but not all data analysts are data scientists.
From the above quote, it is evident that data scientist stands a level up in certain cases. This is due to the fact that in addition to the data analysis, the data scientists perform the programming part. They create the data model with the help of data given by the data analysts.
Data scientists work on top algorithms such as regression, clustering, visualization, K-NN, statistics, time series etc.
When the data model created by the data scientists needs to be deployed as a product, the machine learning engineer enters the crease.
Machine learning engineers create the software product (website applications, mobile applications etc ) out of the given data model.
The life of a machine learning engineer can be synchronized to that of a data scientists as their roles overlaps depending upon the organization.
A wide range of tools are used in data science industry for various purposes such as statistical analysis, modelling and visualization.
Some of the most widely used data science tools are: