Business intelligence is a collection of tools, techniques and approaches which include data mining, data science, artificial intelligence, machine learning, neural networks, data visualization, deep learning and others that identify the sources of data, discern patterns, associations, clusters and relationships in the data to turn data into meaningful information. That information can be used to produce the answers to big questions, diagnose and solve difficult or impossible problems, and even predict the future. This course explores the various forms and architecture of business intelligence and how business intelligence and its associated technologies are used to help organizations make operational and strategic decisions. The course also introduces data mining, as it is used to support business intelligence through analyzing vast amounts of data to produce information and recommendations by application of association rules, K-Nearest Neighbor (KNN) analysis, clustering, and Market Basket Analysis.
By the end of this course, participants will be able to:
- Use data-based tools to make more accurate and timely decisions
- Understand the mechanics and architecture behind business intelligence, data mining and Big Data
- Utilize data mining techniques for predictive analysis, to assist in making decisions about the present and predicting future events
- Visualize data using business intelligence and data mining visualization methods and tools
Module 1: Incorporating Business Intelligence into Organizational Decision Making
- The challenge of decision making
- The challenge of asking and answering questions What is “business intelligence”?
- The business intelligence value proposition
- The evolution of business intelligence Business intelligence taxonomy
- Business intelligence management issues
Module 2: The Architecture of Business Intelligence Data Warehousing
- The data warehouse and its relationships
- Data architecture: fact table, stars, and snowflakes
- Building the data warehouse – Extraction, Transformation, Load (ETL)
- Data marts
Module 3: Extracting Intelligence from the Data
- Business intelligence: the front-end
- The concept of dimensions in data
- Online analytical processing
- Visualizing and working with data cube
Module 4: The Architecture of Data Mining
- Big Data: types and structure
- Business challenges of Big Data Identifying sources of data
- Transforming Big Data into value
- Technologies of Big Data: Hadoop, Pig, and Hive
- Clouds: public, private and hybrid
- Applying Big Data to business problems
Module 5: Create a Data Mine – A Step-By-Step Process
- Cross-Industry Standard Process for Data Mining (CRISP-DM)
- Use of data mining in business
- Data mining models
- Descriptive, predictive, and prescriptive models
Module 6: Employing the Models – Identifying Patterns and Anomalies
- Classification
- Association rules
- Clustering and cluster analysis
Module 7: Employing the Models – Predicting the Future
- Market Basket Analysis
- Time series
- Predictive analytics and regression
This training is ideal for:
- Manager
- Executives
- Data Scientists
- Data Analysts
- Business Analysts
- professionals working with data analytics or business intelligence, and anyone who needs to understand how to use data to make better decisions