Data Science

Data Science is the emerging field to acquire knowledge from data. Its goal is to extract the valuable information from data, and to transform or to manufacture the data product. The book focuses on both fundamental science knowledge (including math, statistics, etc.) and implementations (coding, tool, etc.).

The following are topics introduced in the book.

• Machine Learning : Machine learning is the field that to design a series of analyzing algorithm to make machine learn itself. In general, machine learning algorithms automatically acquire regularity from data, and attempt to classify, cluster or predict the unknown data based on the regularity.
• Deep Learning : Deep learning, also called reinforced machine learning or advanced neural network, is built on different neural network types and improves learning processes over several steps (levels) in developing images, versions, and speaks, etc fields.
• Data Mining : Data mining is the field that extracts the hidden features from different types of huge data.
• Statistics : The statistics is the objective science to summarize the whole data. It plays an important role in data science, and is used not only in machine learning but also in data mining. Furthermore, statistics promotes lots of emerging sub-fields, e.g. statistical learning, etc.
• Mathematics : Mathematics is the foundation of the science and is almost used in several engineering fields. Lots of mathematics subfields are important in data science, e.g. statistics, engineering mathematics (ODE), numerical analysis, etc.