10 essential statistical techniques that data scientists need to master

In today's rapid development of big data technology, data scientists have a huge impact in both academia and industry, so what is a data scientist? What skills do data scientists have?

What is a Data Scientist?

A data scientist is an engineer or expert (different from a statistician or analyst) who can use scientific methods and data mining tools to digitally reproduce and understand complex amounts of data, symbols, text, audio or other information, and find new data insights. The qualities that an excellent data scientist needs to have are: understand data collection, understand mathematical algorithms, understand mathematical software, understand statistical analysis, understand advanced analysis, understand market application, understand decision analysis, etc.

A data scientist is a combination of a technologist and a data analyst, compared to traditional data analysts, who typically use internal data to support leadership decision-making, to create products and processes with different characteristics by focusing on user-facing data to provide meaningful value-added services to customers.

Why study Statistics?

Statistics is a comprehensive science that uses means such as searching, sorting, analyzing, and describing data to infer the essence of the measured object and even the future of the object. It uses a great deal of expertise in mathematics and other disciplines, and it covers almost all fields of social and natural sciences.

For decades, statistics and computer science have been parallel to each other, creating a series of tools and algorithms created by each other. But until recently, people have begun to notice that what computer scientists call machine learning is just prediction in statistics. As a result, the two disciplines began to merge again.

Pure machine learning focuses on algorithms, while statistics have always been a strong shirt to praise "explainability". And as a data scientist, 80% of the time you need to explain to the client, team, or boss why A works and B doesn't. If you tell them, "My neural network is so powerful but I can't explain it," then no one will want to believe you.

All of all, learning statistics is very important for data scientists!

Traditional statistical methods for data mining include regression analysis, principal component analysis, and cluster analysis.

Non-machine statistical learning methods for data mining include fuzzy sets, rough sets, and support vector machines.

Data mining is the process of algorithmically searching for hidden information from large amounts of data. Data mining is often associated with computer science and is accomplished through many methods such as statistics, analytical processing, intelligence retrieval, machine learning, expert systems, and pattern recognition. Nowadays, people are eager to analyze massive amounts of data in depth, discover and extract hidden information in order to make better use of it, and it is precisely because of this need that data mining technology has come into being.

There are many legitimate uses for data mining, such as finding out the relationship between a drug and its *** in a database of patients. This relationship may not occur in 1,000 people, but pharmacology-related programs can use this method to reduce the number of patients who have adverse reactions to drugs and potentially save lives.

Regarding the study of data mining, we recommend the relevant courses of CDA data engineers, which take into account the horizontal ability to solve data mining process problems and the vertical ability to solve data mining algorithm problems. Students are required to have the thinking of starting from the root cause of data governance, explore business problems through digital working methods, and choose business process optimization tools or algorithm tools through proximate cause analysis and macro root cause analysis, rather than "problem tuning algorithm package". Click here to book a free trial lesson.

There are several statistical methods commonly used in data mining:

Traditional statistical methods include regression analysis, principal component analysis, cluster analysis, etc

Non-machine learning methods: fuzzy sets, rough sets, support vector machines.