BIG DATA TOOLS – PART 2
Année du cours : 1 année(s)
Etablissement : IÉSEG School of Management
Langue : English
Formation(s) dans laquelle/lesquelles le cours apparait :
Période : S2
– Participants should be familiar with the basic concepts of R (e.g., vectors, dataframes, functions, packages).
– Participants should be familiar with reading and writing SQL queries (e.g., select, group by, having).
– Participants should know the basic concepts of Business Analytics and Predictive Modeling.
At the end of the course, the student should be able to:
– understand the available technologies in the Big Data universe and use the correct technology for a given Big Data problem
– know the technologies for reading and writing Big Data (e.g., MapReduce, Hadoop, HDFS, Parquet)
– know Spark, its architecture and its APIs
– use Spark as a tool for descriptive and predictive analytics using Spark SQL, MLlib, Streaming, and GraphX
– solve and present an end-to-end solution to a Big Data problem in an intercultural team
These competencies and/or skills contribute to the following learning objectives
– 1.B Successfully collaborate within a intercultural team
– 3.A Breakdown complex organizational problems using the appropriate methodology
– 4.B Compose constructive personal feedback and guidance
– 4.C. Convey powerful messages using contemporary presentation techniques
– 5.B Construct expert knowledge from cutting-edge information
– 7.A Demonstrate an expertise on key concepts, techniques and trends in their professional field
Every day, 2.5 quintillion bytes (=2.5*10^18 bytes) of data are created. Every minute, more than 4.2 million posts are liked and 300 hours of videos are uploaded. This generated (Big) data is characterized by its volume, variety, velocity, and veracity, and requires a specific approach for reading, writing, transforming, and modeling. This course introduces the problem of Big Data, the Big Data universe, reading and writing Big Data, and the skills to work with these data. It uses Spark as a core processing engine for running descriptive and predictive analyses on Big Data.
