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Analytics and Information Management
Statistical and technical understanding are essential for the AIM professional. This course focuses on creating a solid foundation of technical skills, which will be applied in downstream courses. Topics will include programming concepts and logic, descriptive, predictive and prescriptive statistics, and data storage techniques.
Modern organizations use database management systems to store their critical business data. In this course, students learn to answer business questions using various data retrieval, manipulation and transformation tools. For instance, data retrieval for most data-driven business applications relies on Structured Query Language (SQL) - the international standard language for data manipulation and retrieval. To source the data required by analytics projects, students will learn to utilize SQL along with other data-related concepts and languages.
Data Analytics with Artificial Intelligence
Artificial Intelligence (AI) technologies are widely adopted and applied in various contexts. Applications of AI include financial services, cyber security, web search, targeted product recommendations, self-driving cars, personal assistants, robotic manufacturing, machine translation, and video games. Such applications use AI techniques to interpret information from a wide variety of sources to enable intelligent, goal-directed behavior. Companies are increasingly applying these AI technologies and techniques to uncover new business insights and assist managers with making better informed and timely decisions. This course will cover concepts and algorithms in artificial intelligence with an emphasis on machine learning. Fundamental concepts and base knowledge for understanding AI, such as basic search algorithms for problem solving, knowledge representation and reasoning, pattern recognition, fuzzy logic, and neural networks, will be introduced.
Behavioral Analytics with Visualization
E-commerce and social media have experienced rapid growth during the past decade. Billions of users have been generating and sharing a variety of social content including text, images, videos, and related metadata. Social media can be viewed as an indicator reflecting different aspects of the society, and organizations can combine this with their existing data to support organizational decision-making. In this course, students will learn how to analyze behavioral data and apply the analyses to answer business questions. Advanced regression techniques, network concepts, and social theories will be covered throughout the course.
Applications of Data Analytics
The 21st century has been called by many ‘The Century of Data.' The explosion of social media and the digitization of many aspects of social and economic activity have resulted in the creation of large amounts of data in a variety of formats. At the same time, computers are becoming more powerful, and storage is becoming less expensive. This combination of factors has resulted in the availability of much richer data. By applying advanced analytical techniques to these data, organizations hope to uncover hidden insights that will yield competitive advantages. The key objective of this course is to equip students with knowledge about how organizations are applying analytical techniques in various business situations, as well as the associated technical, conceptual and ethical challenges associated with the application of such techniques.