How is the demand for data science and big data technology professionals. According to the 2016 China Internet Hottest Positions Talent Report released by Linking, R&D engineers, product managers, human resources, marketing, operations and data analysis are the most demanded positions in China's Nursing Wannabe industry.
Currently there are 300,000 data talents in China, it is expected that in 2018, the demand for big data talents will have a substantial increase, high-end talents such as big data scientists gap between 140,000 and 190,000; know how to use big data to make decisions on the gap between analysts and managers to reach 1.5 million, the demand for data analysts is very strong right now, 2 years of experience can reach a monthly salary of 8K, 5 years of experience can reach 400,000 to 600,000 yuan.
What are the jobs available for big data technology majors. Emphasis on data has become more and more institutions, up to the Ministry of Defense, down to the Internet startups, financial institutions need to do innovation drive through big data projects, the need for data analysis or processing jobs are also a lot; common food manufacturing, retail e-commerce, medical manufacturing, traffic detection, etc. also need data analysis and processing, such as optimizing inventory, reducing costs, predicting demand, and so on. Talents are mainly divided into three main categories: big data system research and development, big data application development, big data analytics, popular positions are:
1. Big data system architect big data platform construction, system design, infrastructure. Skills: computer architecture, network architecture, programming paradigms, file systems, distributed parallel processing.
2. Big Data System Analyst is oriented to the actual industry field, using big data technology for data security life cycle management, analysis and application. Skills: artificial intelligence, machine learning, mathematical statistics, matrix calculation, optimization methods.
3. hadoop development engineer. Solve big data storage problems.
4. data analyst different industries, specializing in industry data collection, collation, analysis, and based on the data to make industry research, assessment and prediction professionals. By using tools in their work, they extract, analyze, and present data to realize the commercial significance of the data.
As a data analyst, you need to be proficient in at least one of the data analysis software such as SPSS, STATISTIC, Eviews, SAS, Big Data Magic Mirror, etc., and at least be able to develop databases with Acess, etc., and at least master a math software such as matalab, mathmatics for the construction of new models, and at least master a programming language. In short, a good data analyst should business, management, analysis, tools, design are not left behind.
5. Data mining engineers do data mining to discover the laws from the massive amount of data, which requires a certain amount of mathematical knowledge, the most basic such as linear algebra, higher algebra, convex optimization, probability theory, etc.. Frequently used languages include Python, Java, C or C++, I myself use Python or Java more. Sometimes use MapReduce to write programs, and then use Hadoop or Hyp to process the data, if you use Python will be combined with Spark.
6. Big Data Visualization EngineerWith the application of big data in people's work and daily life, big data visualization is also changing the way human beings read and understand information. From Baidu migration to Google Flu Trends, to Aliyun launching county economic visualization products, big data technology and big data visualization are the heroes behind the scenes.
Big Data Visualization Engineer Job Responsibilities: 1, based on the product business function, the design of the visualization program to meet the needs of the 2, based on the visualization of the different scenarios and performance requirements, select the appropriate visualization technology. 3, based on the program and technology selection to produce visualization samples. 4, with the visual designers to improve the visualization samples. 5, with the front-end developers will be the samples of the componentization.