Data Analysis
Badge earners understand predictive models that create business value from Big Data
solutions. Participants recognize the technology (databases, query languages, architectures)
and statistical techniques of Big Data Analytics. Badge earners have completed a hands-on
Predictive Analytics project.
Prerequisite: Basic understanding of statistics, databases, and data management
Program objectives:
- Determine the predictive modeling problem definition
- Understand statistical concepts and tools
- Work with relational databases
- Use the R language for prediction
- Examine how to store Big Data using NoSQL frameworks, Hadoop, and Hortonworks
- Process Big Data
- Work with Pig Latin and Hive query language
- Understand advanced analytics
Required course
Understands how the growth of smart devices and the huge data they generate, along with the significant increase in the ability of computers to digest and learn from data, has led to Predictive Analytics becoming a critical area of focus for most businesses. Explores the three dimensions of Predictive Analytics. Studies technology (databases, query languages, R language, architectures) and statistical techniques of Big Data Analytics. Learns the statistical techniques underpinning Predictive Analytics, such as regression and correlation. Applies Predictive Analytics to a real-life domain situation. Recommended Preparation: basic understanding of statistics, databases, and data management.
R Programming
R Programming badge earners comprehend how to use this open source language and software
environment for statistical computing and graphics. Participants have learned how
to define a predictive modeling problem to study and have learned about R commands
for data analysis. Badge earners understand how to program a real-time predictive
tool and how to apply an approach to different predictive problems as part of class
projects.
Prerequisite: Some understanding of data analysis
Program objectives:
- Define R programming
- Understand predictive problem definition used to study data in R programming
- Use R programming commands for data analysis, statistical validation and graphics
- Use R programming to study the predictive problem
- Convert R programming into a real-time predictive tool
- Apply different predictive problems as part of individual class projects
- Use R programming for visualization of data and story telling
Required course
Learn R programming, and become skilled in this open source language and software environment for statistical computing and graphics. R programs are used by statisticians and data miners to facilitate data analysis and visualization and help build predictive models. In this class, you will learn how to define a predictive modeling problem to study; learn R commands for data analysis, program a real-time predictive tool. and apply your approach to different predictive problems as part of class projects.
