About the Course
This course provides practical foundation level training that enables immediate and effective participation in big data and other analytics projects. It establishes a baseline of skills that can be further enhanced with additional training and real-world experience. The course provides an introduction to big data and a Data Analytics Lifecycle Process to address business challenges that leverage big data. It provides grounding in basic and advanced analytic methods and an introduction to big data analytics technology and tools, including MapReduce and Hadoop. The course has extensive labs throughout to provide practical opportunities to apply these methods and tools to real-world business challenges 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. The course prepares the student for the Proven™ Professional Data Scientist Associate EMCDSA) certification exam.
Topics Covered1. Introduction to Big Data Analytics
2. Data Analytics Lifecycle
3. Review of Basic Data Analytic Methods Using R
4. Advanced Analytics – Theory And Methods
5. Advanced Analytics - Technologies and Tools
6. The Endgame, or Putting it All Together
Who should attendManagers of business intelligence, analytics, big data professionals, data and database professionals adding big data analytics to their skills, recent college graduates and graduate students in related discipline looking to move into Data Science.
Pre-requisites*A strong quantitative background with a solid understanding of basic statistics, as would be found in a statistics 101 level course.
*Experience with a scripting language, such as Java, Perl, or Python (or R). Many of the lab examples taught in the course use R (actually RStudio), which is an open source statistical tool and programming language
*Experience with SQL
What you need to bringLaptop
Key TakeawaysAt the end of Data Science and Big Data Analytics training course, participants will be able to:
*Immediately participate and contribute as a Data Science Team Member on big data and other analytics projects by
*Deploy the Data Analytics Lifecycle to address big data analytics projects
*Reframe a business challenge as an analytics challenge
*Apply appropriate analytic techniques and tools to analyze big data, create statistical models, and identify insights that can lead to actionable results
*Select appropriate data visualizations to clearly communicate analytic insights to business sponsors and analytic audiences
*Use tools such as: R and RStudio, MapReduce/Hadoop, in-database analytics, Window and MADlib functions
*Explain how advanced analytics can be leveraged to create competitive advantage and how the data scientist role and skills differ from those of a traditional business intelligence analyst