Big data learning: which books should be read for the introduction of zero basic big data?


Now there are many friends who are interested in big data industry, but they don’t know how to start. What books should you read as a beginner of zero basic big data? Today, I did some sorting as a reference, hoping to help those students who are interested in big data.

Big data learning: which books should be read for the introduction of zero basic big data?

1. Big Data Engineer

In Internet companies, there are extensive recruitment, platform business orientation, ETL and OLTP, etc., which are mainly based on Hadoop technology stack to process big data, and the algorithm requirements are not particularly high.

Classic book recommendation:Hadoop authority guide, hive programming guide, HBase authority guide, big data technology solution, big data challenge NoSQL, mahout practice

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2. Data Analyst:

In e-commerce, finance, telecommunications, consulting and other industries with industry data, we do business consulting, business intelligence, and produce analysis reports. The product managers of Internet companies are almost of the same type, with high statistical ability requirements, SPSS, SAS, R, SQL.

Classic book recommendation:Probability theory and mathematical statistics, statistics recommended by David Freedman edition, business modeling and data mining, introduction to data mining, SAS programming and data mining business case, Clementine data mining method and application, IBM SPSS statistics 19 statistical procedures company, etc.

3. Data mining engineer:

In the big data related industries such as the Internet, e-commerce, search, and social networking, machine learning algorithm implementation and analysis require high basic data structure algorithm and machine learning. Hadoop, spark technology stack, Java, python, C + +, Scala, shell.

Classic book recommendation:Data mining concept and technology, introduction to data mining, data mining practical machine learning technology; machine learning Tom Michael, introduction to machine learning, machine learning by Zhou Zhihua, machine learning practice, collective intelligent programming, ESL elements of statistical learning, ISL an introduction to sta Technical learning PRML pattern recognition and machine learning, introduction to database system, introduction to algorithm, web data mining, recommendation system, data visualization, thinking in Java, python core programming, thinking in C + +, etc.

Of course, another important step is to practice, practice and practice constantly, and combine the knowledge learned with the actual application scenarios. If you find it difficult to learn by yourself in the learning process, you may as well carry out systematic learning. Big data combines theory with practice, and is dedicated to the cultivation of big data talents.