As the modern adage goes,“Data is the new oil”. According to popular definition Business intelligence (BI) is a technology-driven process for analyzing data and presenting actionable information to help corporate executives, business managers and other end users make more informed business decisions. BI encompasses a wide variety of tools,applications and methodologies that enable organizations to collect data from internal systems and external sources, prepare it for analysis, develop and run queries against the data, and create reports, dashboards and data visualizations to make the analytical results available to corporate decision makers as well as operational workers.
In recent years, organizations have increasingly turned to advanced BI & Analytics solutions to manage workloads, maintain profitability and ensure competitiveness within their respective industries. Analytics is a broad area that utilizes statistical analysis, data mining and quantitative analysis to explore and analyze historical and current data. Global revenue in the business intelligence (BI) and analytics software market is forecast to reach $18.3 billion in 2017, an increase of 7.3 percent from 2016, according to the latest forecast from Gartner, Inc.
By the end of 2020, the market is forecast to grow to $22.8 billion. In recent times, this field has evolved from a technology topic to a management priority, creating an unprecedented demand for new management skills. This course provides potential IT managers with an understanding of the issues involved in designing, developing and implementing business intelligence applications. The course will focus on specific technologies including data warehousing, machine learning, business intelligence and analytics tools. Case studies describing organizational experiences with business intelligence will be discussed.
Another primary focus of the course is on the managerial issues relevant to these technologies. Hands-on workshops will be provided using cutting-edge BI and Analytics tools.
Upon completion of this course, students will be able to:
- Learn fundamental concepts, best industry practices and technologies that will help in implementing and delivering successful business intelligence and analytics solutions.
- Understand data warehousing concepts including data integration and the extraction, transformation, and load (ETL) processes, administration, and security issues.
- Apply data visualization principles to create effective data visualizations for communicating, monitoring, and data exploration.
- Contrast various data analytic tasks (classification, prediction, association rules, and cluster analysis) in terms of the type and structure of the data, purpose, expected output, underlying algorithms, and common business and public-sector applications.
- Translate a business problem into an analytic task, determine the needed data, build a model using WEKA, evaluate the model’s performance, and identify deployment concerns.
- Explain the value of each stage defined by the Cross Industry Standard Process for Data Mining (CRISP-DM) and learning how to use this framework to structure a data analytic problem.
- Study successful case studies of contemporary BI applications in various industries.