Data Driven Management
This course focuses on the methods and approaches to effectively manage the analytical processes and use the results of these processes as the basis for making informed, evidence-based decisions. In addition, the ways of companies in using analytics as the basis for creating value are also covered.
The course focuses on the communications skills such as clear communication, strategic management communication, giving and receiving feedback, using influence and advocacy, presentation skills and career management skills. After completion of this course, students will be able to identify the types and purposes of various business documents; create messages using appropriate channels; understand the effect of technology, such as social media, on business communication; and identify ethical, cross-cultural, and multinational issues in business communication.
Customer Relationship Management
Customer Relationship Management (CRM) is a comprehensive set of processes and technologies for managing the relationships with potential and current customers and business partners across marketing, sales, and service areas regardless of the channel of distribution. This course focuses on the development and implementation of relationship marketing strategies via the use of CRM initiatives. Topics covered in the course include: relationship marketing; operational, analytical, and collaborative CRM; reasons for CRM implementation failure; and the role of CRM in marketing management.
This course explores the digital marketing environment from both a consumer and business perspective. The course provides an overview of various online business models and delves into digital advertising and social media marketing techniques and technologies.
This course provides an overview of key areas of customer analytics: descriptive analytics, predictive analytics, prescriptive analytics, and their application to real-world business practices. This course also provides an overview of the field of analytics so that students make informed business decisions.
Quantitative Decision Making
This course aims to introduce students to the use of quantitative tools in the decision-making process of an organization. Planning and operational problems in the manufacturing and services sectors are emphasized. Topics include forecasting, capacity planning, optimization, project scheduling, simulation and risk analysis, quality, inventory management, and waiting lines.
This course is designed to impact the way you think about transforming data into better decisions. Recent extraordinary improvements in data-collecting technologies have changed the way firms make informed and effective business decisions. This course focuses on how the data can be used to profitably match supply with demand in various business settings. In this course, students will learn how to model future demand uncertainties, how to predict the outcomes of policy choices and how to choose the best course of action in the face of risk.
Strategic Management and Business Simulation
The objective of this course is to introduce the key concepts, tools, and principles of competitive analysis and strategy formulation. The course is concerned with managerial decisions and actions that affect the performance and survival of business enterprises. It is focused on the information, analyses, organizational processes, skills, and business judgement managers use to devise strategies, position their businesses, define firm boundaries, and maximize long-term profits in the face of uncertainty and competition.
Research methods course will prepare students to design effective ethical investigations. This course is designed to expose the core ideas behind research methods. The major components of designing research will be addressed in this course. The students will design, conduct, and present an actual research project as part of this course.
Introduction to Data Science Tools
Introduction: What is Data Science? Big Data and Data Science, Skill sets needed for a data scientist: Introduction to R (Python) Programming, Advance Features in R (Python). Introduction to Visualization (Basic principles, ideas and tools), Advanced Visualization in both R (Python). Exploratory Data Analysis and the Data Science Process - Basic tools (Visualization: plots and graphs and summary statistics) of EDA Using R (or Python). Statistical Inference. Extracting Meaning From Data: Feature Generation, Feature Selection algorithms – Filters; Wrappers; Decision Trees; Random Forests, Example: Churn Analysis. Three Basic Machine Learning Algorithms - Linear Regression, Ethical Issues in Data Science (Privacy, Security, and Ethics).
Database Design and Management for Business Analytics
The characteristics of database approach & relations, techniques and methodologies of Database Management Systems; Entity Relationship approach to data modeling (ER Diagrams), the relational model of DBMS, relational mapping, normalization and Structured Query Language (SQL), the contributions of DBMS to an organization’s operations, control and planning activities.
Machine Learning and its Applications to Business
Introduction to Machine Learning, major applications, Mathematical background, marginal and conditional probability, Bayes theorem, Bayesian decision theory, Density estimation, Maximum Likelihood estimate, Bayesian Learning, Naïve Bayes Linear regression, Bias-variance dilemma, regularization, ridge regression and lasso Linear classifiers, Artificial neural networks, perceptron and multilayer perceptron, Assessment and comparison of classifier performance, Feature selection and extraction, Large margin classifiers, support vector machines, kernel methods, Decision trees and random forest, Unsupervised learning, clustering, Deep learning and big data.
Cloud Computing and its Applications
Cloud computing characteristics, layers. Business and technical aspects of cloud computing, Marketplace, Startup Engineering. Application Platform as a Service, Architecture, Standardization and Adoption Issues, Integration as a Service, Cloud data management, Enterprise Data and Cloud Interaction. Emerging cloud computing applications and future of Cloud.
Overview of the process of data visualization for analysis, reporting, and prediction purposes. Understanding data and data types. Reporting, Charting, mapping, dashboards, Infographics.
Data and Technology Management
Business and technical aspects of data and technology management. Data integration, framework and business intelligence. Information Technology Innovation, Strategy, (Open) Data Governance and Architecture. Data Collection, Preparation and Visualization. Big Data Management and Technology Challenges. Emerging data management applications. Smart cities. Term project.
The course covers the concepts and tools that a PhD/MSc student needs throughout the entire data science life cycle from asking the right kinds of questions to making inferences and publishing results. In the final project, students will apply the skills learned by building a data product using real-world data. There are two components to the real-world case studies. The first is a dataset of a data-producing complex system. The second is the introduction of the network theory as a tool to model and to filter information in these data-producing complex systems.