First, the frontier knowledge of big data and hadoop introduction

Get started with zero foundation, understand the historical background and development direction of big data, and master two installation configurations of hadoop.

Second, Hadoop has advanced deployment.

Proficient in hadoop cluster construction; The distributed file system HDFS based on Hadoop architecture is deeply analyzed.

Third, the Java Foundation.

Understand the basic idea of java programming, skillfully use eclipse for simple java programming, skillfully use jar files, understand the principles of database management systems such as mysql, and understand the program development process based on web.

Fourthly, MapReduce theory and actual combat.

Familiar with the working principle and application of mapreduce, familiar with the basic programming of MapReduce, and master the design and writing of projects based on MapReduce according to the goal of big data analysis.

V hadoop+Mahout big data analysis

Master the usage scenarios of big data analysis methods based on hadoop+mahout, and skillfully use mahout's mature algorithms to analyze big data in specific scenarios.

Sixth, Hbase theory and actual combat

Master the data storage and project actual combat of hbase, and master the installation, configuration and use scenarios of Spark and Hive.

Seven, Spark big data analysis

Spark and Hive install, configure and use scenarios, and skillfully use Spark's mature algorithms to analyze big data in specific scenarios.

Eight, big data learning comprehensive knowledge reserve

Statistics: multivariate statistical analysis, applied regression

Computer: R, python, SQL, data analysis, machine learning.

Matlab and mathematica also need to be mastered. The former has great advantages in practical engineering application and simulation analysis, while the latter is excellent in calculation function and mathematical model analysis. Mutual subsidies can complement each other.