This course provides practical foundation level training that enables immediate and effective participation in big data and other analytics projects. It includes an introduction to big data and the Data Analytics Lifecycle to address business challenges that leverage big data. The course provides grounding in basic and advanced analytic methods and an introduction to big data analytics technology and tools, including MapReduce and Hadoop. Labs offer opportunities for students to understand how these methods and tools may be applied to real-world business challenges as a practicing data scientist. The course takes an “Open”, or technology-neutral approach, and includes a final lab in which students address a big data analytics challenge by applying the concepts taught in the course in the context of the Data Analytics Lifecycle. This course prepares the student for the Associate - Data Science (DECA-DS) track.
This 5- day/40 hour course provides practical foundation level training that enables immediate and effective participation in big data and other analytics projects.
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Upon successful completion of this course, participants should be able to:
|Data Science and Big Data Analytics||Instructor Led||5 Days Course|
|Data Science and Big Data Analytics||Video Instructor Led Training - Stream||40 Hours|
To complete this course successfully and gain the maximum benefits from it, a student should have the following knowledge and skill sets:
Consider the above as a list of specific prerequisite (or refresher) training and reading to be completed prior to enrolling for or attending this course. Having this requisite background will help ensure a positive experience in the class, and enable students to build on their expertise to learn many of the more advanced tools and analytical methods taught in the course.
Specialist - Data Scientist, Advanced Analytics Course
This course builds on skills developed in the Data Science and Big Data Analytics course. The main focus areas cover Hadoop (including Pig, Hive, and HBase), Natural Language Processing, Social Network Analysis, Simulation, Random Forests, Multinomial Logistic Regression, and Data Visualization. Taking an “Open” or technology-neutral approach, this course utilizes several open-source tools to address big data challenges. This course prepare the students for the Specialist - Data Scientist, Advanced Analytics (DECS-DS) track.
Learn more about Specialist course