Nice job explaing the differences between data analytics and statistics and the breakdown of the differences. I also found some interesting information regarding how data analytics has different uses in the following fields: In most cases, a scientist will examine a collection of pertinent facts using exact statistical techniques. Both statistics and data analysis are stated to have as their ultimate objective finding meaning in raw data. Still, every field approaches this aim differently. Data scientists process and view data using Python and Apache Spark. Though their research is more planned out and precise. Statistical study focuses on one group attribute. Data scientists must be able to use business applications and a query language if they are to do their duties. Statisticians must be math whizzes and knowledgeable about their field. Because of this, companies have a lot of data scientists in many spheres; yet, statistics are more difficult to discover. Data analytics finds great use in the following fields: e-commerce (to enhance sales and marketing) Healthcare (better treatment for patients, avoidance of illness, and optimum use of resources), identifying and halting hacking Banking (managing risks and adjusting) Leading in many data domains is this: The government makes most decisions