BIG DATA ANALYTICSFree
About this course
|Duration : 16 Days|
Data Analytics (DA) is the process of investigating data to extract obvious or hidden information they contain. Commercial industries are increasingly utilizing data analytics technologies, techniques and tools to make information-aware or data driven business decisions.
The 16-day Enterprise Data Analysis course is a fast-paced practical introduction to the interdisciplinary field of data analytics, which is the study of how to use computer science, statistical methods and tests, coupled with a scientific mind-set to extract knowledge from data.
The objective of the course is to teach the participants how to think in a data-analytics way, conduct or approximate experiments on business activity using R, analyze data rigorously and communicate results well, and finally summarize achievements to be presented to decision makers. The course is aimed at people with no or little experience in computer science, but with grounding of undergraduate-level math.
Outside of the classroom, students are expected to spend 8-10 hours a week on homework and their final project.
Introduction to R
R is a powerful popular language and environment for Data Analytics and visualization purposes. As an open source environment, it is highly extensible, can be installed and ran on a wide variety of operating systems. This module provides the participants with a knowledge of R and its applications.
Data Analytics Applications & Statistics with R
In this module, we introduce the participants to common applications of Data Analytics followed by real-world examples. The section continues with the introduction of useful statistics functions/ methods and R packages that the participants can apply to different types of data.
SQL fundamentals with R
SQL is the most common language in database systems which create, alter, update and drop database elements. R provide packages that enable users to use SQL queries in R environment. In this section, RODBC package will be introduced to trainees and applied to run simple and complex SQL statements.
For the final project, the students will study a data-related problem in their professional field or in a related field that they are interested in. The student will acquire a real-world data set, store it in database, conduct different hypothesis tests and statistical analysis, and provide meaningful graphs to present extracted information.
The instructional team will help students scope out their project so that they choose a project that is feasible and that can display the skills that they acquired from the course.