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Business Data Analytics:

Competing in the Age of Big Data

Introduction

With the explosion of Big Data (e.g., Google, Facebook, Netflix, Zillow, etc.), data analytics has become a very hot entity in tech, marketing, finance as well as other business discipline and people with data analytic skills are highly sought after. Business data analytics refers to the practice of extracting insights and making informed business decisions through data analytics. It involves using various analytical techniques, tools, and technologies to examine large volumes of data and uncover meaningful patterns, correlations, and trends.

Course Objective

In this course, we will take a holistic approach to understand the key factors involved, from data collection to analysis to prediction and insight.
 

  • Can you understand the basics of data and analytics?

  • Can you analyze data or to consume it effectively?

  • How are companies putting modern analytical technologies in place?

  • How are businesses using data in their decision-making?

We will learn widely used methods for describing and summarizing data, discover patterns and trends in data, as well as methods and techniques in analyzing and modelling real world data to solve business problems. Emphasis will be on merging technical skills with critical thinking to ensure that robust data science pipelines are being created for business benefit. We will study case studies on market leaders and innovative startups and explore how companies leverage massive amounts of data and sophisticated analytics to succeed in today's data-driven environments.

Course Outline

In this course, we will study foundational concepts in business analytics and see how they are applied in real world setting through analysis of case studies (the Harvard Business School approach). There are 5 sessions in this course:
 

Session 1: Introduction to Business Analytics
Session 2: Building Data Science into Your Business Strategy I
Session 3: Building Data Science into Your Business Strategy II
Session 4: How Artificial Intelligence and Big Data Are Changing Business
Session 5: Group Presentation by Participants

 

At the end of the course, you will be better prepared to apply analytics in business. Topics covered include - What Is Business Analytics: Descriptive, Predictive, and Prescriptive Analytics, Models in Business Analytics, etc; Gather the Right Information: Data Sources, Organization, and Structures, Fundamentals of A/B Testing, etc; Communicate Your Findings: Data handling, Data Visualization, How to Make Charts That Pop and Persuade, etc; Problem Solving with Analytics: how to Deploy Predictive Analytics, Developing a Big Data Strategy, What AI-Driven Decision Making Looks Like, etc.

Teaching Methodology

General: Because class attendance and success in class are positively correlated, students are expected to attend lectures punctually and to participate actively in class.
 

Pedagogy: In this age of internet and social media, the use of interactive tools to make learning a more active process will become the new norm in teaching. The course will use multimedia (e.g., YouTube clips), online tools (e.g., Wooclap) to make lectures more lively, engaging, and interactive. They are powerful visual and experiential stimuli that help to create a more immersive and value-added learning environment. Oftentimes, students become inspired to learn more after watching YouTube clips about real-life applications of course content. The course will use Wooclap, an interactive platform that allows real time polls/quizzes, to encourage active participation and collaboration, and enable real time feedback and data analytics to optimize future classes and learning outcomes.

About the Trainer

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Dr Yong Ee Hou is an Assistant Professor from the School of Physical and Mathematical Sciences at Nanyang Technological University. He received his BSc in Mathematics, BSc in Physics, MSc in Statistics from Stanford University, and PhD in Physics from Harvard University. His research focuses on using new and emerging state-of-the-art methods from mathematics, physics, artificial intelligence, machine learning, topological data analysis, and statistics to analyze big data of complex systems such as DNA, RNA, biological networks, social networks, financial markets, etc., in order to understand their behavior, pattern and interaction. His work has been published in top journals such as Science, PNAS, Physical Review Letters, etc.

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