What courses are studied in data science and big data technology

1. belongs to the cross-disciplinary: statistics, mathematics, computers as the three major supportive disciplines; biology, medicine, environmental science, economics, sociology, management as the application of expanding disciplines. In addition to learning data collection, analysis, processing software, learning mathematical modeling software and computer programming languages, etc., the knowledge structure is a two-specialty multi-purpose composite cross-border talent (professional knowledge, data thinking).

2. Basic courses: Mathematical Analysis, Advanced Algebra, General Physical Mathematics and Introduction to Information Science, Data Structures, Introduction to Data Science, Introduction to Programming, Programming Practice. Required courses: Discrete Mathematics, Probability and Statistics, Algorithm Analysis and Design, Data Computational Intelligence, Introduction to Database Systems, Fundamentals of Computer Systems, Parallel Architecture and Programming, Unstructured Big Data Analysis.

Data science and big data technology career prospects

Big data is known as the "new oil of the 21st century", is a national strategic asset, is the 21st century "diamond mine". The McKinsey Global Institute regards big data as "the next frontier of innovation, competition and productivity", and 2013 is known as the first year of big data. In just a few short years, big data has permeated every aspect of society.

Artificial Intelligence (AI) is an unstoppable development trend, and Big Data technology is an important support for AI. Big data science will become the core of leading artificial intelligence technology, Internet of Things applications, computer science, digital economy and business development.

Data science and big data technology professional enrollment advice

1, the current phenomenon of enterprise employment: a professional cluster corresponds to an industry hotspot. Big Data is a cross-discipline, taking a "composite" training route, the industry engaged in related functions of people with different professional backgrounds. Big data as a talent training direction in the exploration, if directly from the various professional talents in the selection of students to carry out master's degree education will be more suitable for some of the direct opening of the undergraduate stage of education is relatively immature.

2, talent training and industry development gap. Due to the syllabus update will not be too timely, big data talent seven years after graduation (undergraduate four years, master's degree three years), I'm afraid that what I learned lagged behind the development of the industry.

3, the typical competency characteristics of big data talent: good at doing demand analysis, writing code; good at communicating with people, like to explore the unknown; the need to deduce, analyze, and propose solutions based on the data, there is a data mindset; the need to continue to maintain the state of learning; the internal character of the ability to move and be quiet.

4, different levels of schooling institutions to open this specialty, the training mode will be different. For example, students in higher vocational colleges and universities due to the relatively weak foundation of mathematics, will be more inclined to the use of tools, such as data cleaning, data storage, and data visualization and other related tools; undergraduate colleges and universities will be inclined to the comprehensive coverage of basic knowledge related to big data teaching, in the postgraduate section will specialize in a technical field, such as data mining, data analysis, business intelligence, artificial intelligence and so on.

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