BIG DATA ANALYTICS WORKSHOP FOR MANAGERS March, Endorsed by

Transcription

1 BIG DATA ANALYTICS WORKSHOP FOR MANAGERS March, 2019 Endorsed by

2 BIG DATA ANALYTICS WORKSHOP FOR MANAGERS topics include: INTRODUCTION VISION & STRATEGY ARCHITECTURE & DATA APPLICATION DATA SCIENCE TOOLS DATA EXTRACTION & INTERPRETATION STATISTICS AND ARTIFICIAL INTELLIGENCE MODELS DATA VISUALIZATION DATA SCIENCE PROJECT MANAGEMENT DATA DRIVEN DECISION MAKING SHIFTING FROM MODELS TO PRACTICAL USE CASE

3 PROGRAM OVERVIEW The Big Data Analy cs Workshop for Managers offered by RIT Dubai is intended for senior managers from every diverse fields, who want to incorporate Big Data and employ Data Science tools in developing and implemen ng new strategies in their organiza ons. DAY 01 3 Days Workshop WHY DO WE NEED BIG DATA AND WHAT WILL IT BRING TO OUR ORGANIZATION? WHAT KIND OF DATA DO WE NEED? INTRODUCTION What is Big Data? What is Data Science? The importance of Big Data in management and leadership. Challenges and opportuni es. The 5,3 and 10 Vs of Big Data. The main chapters of Data Science. Defining a strategy with Big Data: collabora on vs compe on. VISION & STRATEGY Aligning data and data science tools to the organiza on s needs. Introducing Big Data in the organiza on. Technology, personnel and culture changes. Use of tradi onal strategy tools when implemen ng Big Data: Change Management Strategies, VMOST (vision, mission, objec ves, strategy, tac cal), SWOT (strengths, weaknesses, opportuni es threats), PEST (poli cal, economic, social technology), SOAR (strengths, opportuni es, aspira ons, results), Porter s Five Forces (threat of new entrants, threat of subs tutes, bargaining power of customers, bargaining power of suppliers, industry rivalry). ARCHITECTURE & DATA What kind of data is available? Quan ta ve and qualita ve data. Structured data and unstructured data. Sta c and streaming data. Architectures for Data Scien sts. Data Base and Data Warehouse, Cloud Compu ng. APPLICATION Applica on domains for Big Data: Agriculture, business, consumer applica on and smart home, educa on, energy manage ment, engineering, environmental monitoring government, industry, Internet of Things (IoT), media, medical and health care, poli cs, privacy, public safety, science, smart ci es, etc.

4 DAY 02 WE HAVE THE DATA, WHAT CAN WE DO WITH IT? HOW DO WE GET THE INFORMATION WE NEED? DATA SCIENCE TOOLS Data storage and management Data cleaning Data mining Data analysis Data languages Data integra on DATA EXTRACTION & INTERPRETATION Methods for extrac ng informa on from the exis ng data Descrip ve and inferen al sta s cs, sta s cal tests Interpre ng data, ethics, presen ng and communica ng results STATISTICS AND ARTIFICIAL INTELLIGENCE MODELS Regression, me series, sta s cal modeling and fi ng Data analysis and predic ve analy cs Machine learning, ar ficial intelligence Applica ons of ar ficial intelligence in Machine vision Natural language processing Expert systems Gaming Self-teaching systems Intelligent robots DATA VISUALIZATION Data visual analysis and visualiza on tools DAY 03 WHAT ARE SOME REAL-LIFE APPLICATIONS OF BIG DATA IN MANAGEMENT SCIENCE? DATA SCIENCE PROJECT MANAGEMENT Important steps for making data-driven decisions Project management DATA DRIVEN DECISION MAKING Compe ve advantage with data-driven decisions and avoiding data-driven disasters Decision analysis and mul -criteria decision making Data visual analysis and visualiza on tools

5 SHIFTING FROM MODELS TO PRACTICAL USE CASE Op miza on problems. Linear programming Network models Transporta on Transshipment Assignment and maximum flow problems Transporta on Transshipment Assignment and maximum flow problems Applica ons of me series and forecas ng Applica ons of Markov processes PROGRESS IN YOUR CAREER DR. MIHAIL BARBOSU SUBJECT MATTER EXPERT Director of the Data and Predic ve Analy cs Center RIT New York Dr. Mihail Barbosu completed his Ph.D. in France at Paris 6 University and Paris Observatory. He is Professor in the School of Mathema cal Sciences and Director of the Data and Predic ve Analy cs Center at RIT. Previously he was Head of the School of Mathema cal Sciences at RIT and Chair of the Department of Mathema cs at State University of New York at Brockport. Dr. Barbosu s experience includes Mathema cal Modeling, Data and Predic ve Analy cs, Academic Management, Dynamical Systems and Space Dynamics.