The field of data science is one of the most widely discussed topics these days. Its multi-disciplinary nature has allowed it to be used in various fields and different industries. Simply put, data science is collecting, analyzing, visualizing, and storing huge amounts of data.
One of the fields that go hand in hand with data science is big data. Big data usually exists in unstructured formats and it is through data science that meaningful insights can be drawn from it. The internet has been a great source of data and provides data scientists with data that can be used to solve organizational problems.
Statistics, on the other hand, is a discipline that focuses on the study of data. Statistics has been applied in various fields to help to draw conclusions from the available data. At the same time, it provides the various methods with which data can be collected and analyzed for problem solving purposes. So how do these two disciplines compare?
Data science is largely centered on scientific computing. It entails machine learning, programming, and analytic processes that can be used to make informed decisions from big data. Data science applies the concepts of statistics and advanced mathematics to find the hidden useful information in big data. It is usually a wide field covering areas like computer science, software engineering, business models, and many others.
On the other hand, statistics is termed as the science of data. It helps data scientists estimate or measure a particular attribute. Statistics uses statistical functions and algorithms on data sets to find out values of a problem being looked into. Both statistics and data science have been crucial to big data engineers in solving organizational problems. To hire big data engineers, you can check out this website for some of the best experts in the field.
Data science usually focuses on huge amounts of data that can only be stored in cloud storage. Statistics can occasionally deal with big data, but that is unusual. Normally, statistics has been all about what we can learn from small data quantities.
One of the main focuses of statistics is quantifying uncertainty. The fact that statistics mainly deals with small data amounts explains why quantifying uncertainty is crucial. This helps statisticians to differentiate between a noise and a signal.
The types of problems reviewed
Data science mainly focuses on large databases within which predictions are made after analyzing the information contained in the databases. On the other hand, the problems that are studied by statistics are aimed at making conclusions from the whole world.
Statistics is all about making conclusion on what causes a particular phenomenon through quantifying uncertainty while data science specifically deals with a database or a predictive model.
Basis of information
The major focus of data science is to tackle problems related to data. It uses big data analysis to understand behaviors and predict patterns, all which are crucial for the performance of a business. Data science supports decision making.
In contrast, statistics’ major focus is to coming up with real world questions based on the available data. Here, data is presented in various ways ranging from tables to charts and graphs. At the same time, statistics offers information that can be used for decision making.