Unit 3: Analysis of scientific data and information

Today, massive data analysis in chemical and biological life science is done automatically by computer. Through this module, you will understand how it is still important to understand how and under what circumstances the use of separate mathematical and statistical techniques and the importance of the results obtained. The main results of scientific experiments usually include data, the number of which varies greatly depending on the type of work being performed. Analysis of acquired data needs to be processed in a way to extract meaning

This unit is published in the Chemical and Biological Life Science Code, deepens the prior knowledge and understanding obtained from the study of scientific data and extends it to the level of industry and research. Beginning with the basic procedures of standard information and data expected from the scientific community, much of the content of units focuses on the use of mathematical and statistical methods in the appropriate environment. The processing of these techniques is practical rather than theoretical. It checks how the processing results are used based on the generated values ​​and related errors, and generates valid conclusions.

Let's define the terms. "Data" is a unit of information. For my purposes, they are input. Without context, analysis, and applications, the data is stupid. "Smart" is the output processing data. It is an answer to important value and power problems. In this article, we treat data and intelligence as unique assets. In the case of Tesla, the company's market share is much lower than that of Toyota, Ford, General Motors and other major automakers. To date, Tesla has 3 billion miles of data from vehicles equipped with autopilots operating on various roads and weather conditions around the world. More than 150,000 Tesla cars are in operation, sales and production increase, mileage grows rapidly

This unit is published in the Chemical and Biological Life Science Code, deepens the prior knowledge and understanding obtained from the study of scientific data and extends it to the level of industry and research. Beginning with the basic procedures of standard information and data expected from the scientific community, much of the content of units focuses on the use of mathematical and statistical methods in the appropriate environment. The processing of these techniques is practical rather than theoretical. It checks how the processing results are used based on the generated values ​​and related errors, and generates valid conclusions.