System Engineering for Data Science: End-to-End Practices to Gain Business Decision Insights for Mission Objectives
INCOSE San Diego Tutorial
Lecturer: Dr. James C Meng, UCSD SDSC Senior Fellow. (Dr. Meng’s resume)
Location: UCSD Extension University City Center, Rm 309, 6256 Greenwich Dr., San Diego, CA 92122
Cost: Up until May 6th in advance: Non-members: $50, Members: $30, Students: $15. May 7th-May 13th and at the door: Non-members: $60, Members: $40, Students: $20
This tutorial offers a practical step-by-step guide how to achieve business objectives leveraging data science basics. The end-to-end system engineering steps are described starting from organization’s business objectives, business processes, data governance, database management, data integration and analytics to gain decision-making business intelligence.
This four hours tutorial will comprise the following:
1 – What is Business Data Science? Why Bother? Expectation of Learning
2 – Define Business Objectives, Linking Business Missions with Performance Data; Business Process & System Engineering Requirements
3 – Business Data Integration, Business Data governance
4 – Business Data Standardization and Data Architecture for Interoperability
Data science, as a profession and as an academic discipline unto itself, is new, having been born in the first decade of the 21st century (Calvin Andrus 2012). The Data science is the study of the generalizable extraction of knowledge from data. While many fail in unleashing data’s potential, few end-to-end practical guides are available for the Enterprise leadership to learn what it takes to build business data insights without over-relying on tool vendors. Mckinsey Global Institute 2011 assessed that the U.S. needs 1.5 million data managers and executives and 140,000 data scientists over the current decade.
The purpose of this tutorial is to equip students with an overall understanding to manage their businesses analytics. Objective is not IT skill development, but critical thinking for solutions development for students. The focus of this tutorial is to develop students’ understanding of how large organizations gain insights from its own wealth of execution data. Students will develop insights into what, when and how business insights can be effectively gained from planning and execution data by using the most widely accepted automation tools developed by the industry leaders.
Data Science incorporates varying elements, including signal processing, mathematics, statistics, probability models, computer programming, engineering analysis, pattern recognition and learning and cloud computing with the goal of extracting meaning from data for businesses creating Business Insights. This tutorial will not address any of the above aspects; instead, we will focus on the end-to-end steps starting from business missions, data standardization, data governance, and data processing to visualize business insights.
* Continental breakfast and coffee will be provided.
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