Business intelligence and data warehousing

Overview

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Welcome to the specialization course Business Intelligence and Data Warehousing. This course will be completed on six weeks, it will be supported with videos and various documents that will allow you to learn in a very simple way how to identify, design and develop analytical information systems, such as Business Intelligence with a descriptive analysis on data warehouses. You will be able to understand the problem of integration and predictive analysis of high volume of unstructured data (big data) with data mining and the Hadoop framework. After completing this course, a learner will be able to ● Create a Star o Snowflake data model Diagram through the Multidimensional Design from analytical business requirements and OLTP system ● Create a physical database system ● Extract, Transform and load data to a data-warehouse. ● Program analytical queries with SQL using MySQL ● Predictive analysis with RapidMiner ● Load relational or unstructured data to Hortonworks HDFS ● Execute Map-Reduce jobs to query data on HDFS for analytical purposes Programming languages: For course 2 you will use the MYSQL language. Software to download: Rapidminer MYSQL Excel Hortonworks Hadoop framework In case you have a Mac / IOS operating system you will need to use a virtual Machine (VirtualBox, Vmware).

Syllabus

Introduction to Business Intelligence as Analytical System

In the first module named Introduction to Business Intelligence as Analytical System, we will learn how the steps of the process of datawarehousing to automate analytical processes that companies need for their business strategies. Let's start!

Designing a Data Warehouse

After completing this module, a learner will be able to identify the entire process of datawarehousing, which consist on OLAP design concepts and multidimensional modelling. The learner will be able to design and create a data warehouse from OLAP requirements.

The ETL process and Analytical queries with SQL

After completing this module, a learner will differentiate from structured and unstructured data and will be able to extract, transform and load data into a datawarehouse. The student will also be able to program and execute OLAP queries with SQL.

Predictive Analytics with Data mining

After completing this module, a learner will identify the main data mining tasks and some algorithms for classification, regression and clustering for predictive and descriptive analysis on business intelligence.

The problem of integration and analysis of unstructured data

After completing this module, a learner will learn the types of data according to structure and how to integrate, store and analyze unstructured data.

Big Data and Hadoop Framework

After completing this module, a learner will understand the problem of big data, a possible solution to the analysis of big data with the Hadoop ecosystem and under which conditions should be apply each element of this ecosystem.