This course introduces learners to the Data Analysis tools in Microsoft Excel. More specifically, learners will focus on how to build and use single regression and multiple regression to solve business problems.

This course was created by some of the leading researchers on business model theory and social entrepreneurship in Europe in collaboration with both small and large companies across various sectors investing in sustainable business model innovation. This course provides an overview of sustainable business model theory and innovation and discusses business models as essential tools in transforming to more sustainable businesses. Throughout the course, we will use the theory of sustainable business models and sustainable business model innovation as a foundation to investigate how companies can implement more sustainable business practices. We will provide you with a rich opportunity to examine their efforts and consider strategies for your organization. We would greatly appreciate if you would also enlighten us with business examples from your countries to discover new ways of dealing with sustainability together.

This course introduces the necessary concepts and common techniques for analyzing data. The primary emphasis is on the process of data analysis, including data preparation, descriptive analytics, model training, and result interpretation. The process starts with removing distractions and anomalies, followed by discovering insights, formulating propositions, validating evidence, and finally building professional-grade solutions. Following the process properly, regularly, and transparently brings credibility and increases the impact of the results. This course will cover topics including Exploratory Data Analysis, Feature Screening, Segmentation, Association Rules, Nearest Neighbors, Clustering, Decision Tree, Linear Regression, Logistic Regression, and Performance Evaluation. Besides, this course will review statistical theory, matrix algebra, and computational techniques as necessary. This course prepares students ready for and capable of the data preparation and analysis process. Besides developing Python codes for carrying out the process, students will learn to tune the software tools for the most efficient implementation and optimal performance. At the end of this course, students will have built their inventory of data analysis codes and their confidence in advocating their propositions to the business stakeholders. Required Textbook: This course does not mandate any textbooks because the lecture notes are self-contained. Optional Materials: A Practitioner's Guide to Machine Learning (abbreviated PGML for Reading) Software Requirements: Python version 3.11 or above with the latest compatible versions of NumPy, SciPy, Pandas, Scikit-learn, and Statsmodels libraries.

This aims primarily at post-baccalaureate students interested in leadership theory and ethical leadership. The first part of this course introduces students to the classical literature in philosophical ethics, including consequentialist, regularian, deontological, and virtue theory approaches. This includes exploration of the ethical responsibilities leaders have toward themselves, corporations, the government, and the public. In the second part of the course, students apply decision- making frameworks, drawing from theories of leadership, and gain experience in decision-making surrounding ethical issues. Finally, students will consider the moral challenges that artificial intelligence poses to ethical leaders. This is one course in the Coursera specialization, Leadership: An Introduction. It examines the latest trends in leadership theory invoking several disciplines, including business, sociology, philosophy, history, and psychology. To complete this course successfully students should be able to analyze college-level readings and audio/visual presentations into understandable parts, including premises and conclusions; synthesize the results of the analysis into coherent and accurate summaries; and evaluate the results for accuracy and practical applicability. Upon successful completion of the course, students will be able to • Identify ethical principles for leadership practices • Compare theories of leadership in terms of their ethical strengths and weaknesses • Apply ethical principles for leadership practices • Compare and contrast industry standards and techniques of ethical leadership practices