In this repository, I store all the code presented in the analytical courses that I teach at Grand View University.
This course provides an introduction to data analytics. Students will learn how experts use predictive techniques and statistical reasoning to make data driven decisions in areas such as marketing, finance, sports, healthcare, genomics, environmental studies, etc. Students will be exposed to some of the special techniques and tools used in big data analysis, gaining hands-on experience analyzing real datasets, exploring different questions, and trying out common analytical tools such as RStudio and Jupyter notebooks.
The goal of this course is to introduce students to principles and techniques of representing data visually. Students will communicate data in a variety of ways using tableau and programming techniques to communicate an effective narrative.
In today's environment business, finance, and accounting professionals need to analyze an increasing volume of data in a meaningful way. This course covers the main quantitative analysis methods of finance. The emphasis is on rigorous and in-depth development of the key techniques and their application to practical problems in order to make sustainable strategic decisions. Good decisions depend on accurate and well-presented information drawn from both domestic and international sources and more importantly the ability to synthesize and draw conclusions from that data. This course will help individuals develop, interpret and analyze both internal and external financial information.
This course will focus on developing marketing strategies and resource allocation decisions driven by quantitative analysis. Marketing activities provide critical economic functions for the success of organizations. Companies of all sizes must develop effective marketing analysis to reach customers. The course will draw on and extend students' understanding of issues related to integrated marketing communications, pricing, digital marketing, and quantitative analysis.
Students will learn the analytical techniques that, when properly applied, can provide a competitive advantage to teams and players. Sports analytics helps facilitate decision-making both on and off the field. Students will learn how to apply methods and principles in a wide range of applications such as evaluating team and player performance; developing tactics and team strategies; improving sales and reducing expenses across an organization; and identifying opportunities to increase brand engagement.
This course provides a comprehensive overview of the algorithms and techniques in machine learning with an emphasis on their practical application to real problems. Covers key concepts in supervised and unsupervised machine learning. Topics include classification, tree-based methods, support vector machines, neural nets, clustering, principal component analysis, design of machine learning experiments, algorithms for prediction and inference, optimization, and evaluation.
This course focuses on predictive analytics to help decision makers evaluate possible outcomes, e.g. revenues, profits, market share, probability of making a sale, probability of losing a client, etc., based on historical data and learning models. Students will develop skills in predictive analytics that will allow them to develop and use advanced predictive analytics methods; develop expertise in the use of popular tools and software for predictive analytics; and learn how to develop predictive analytics questions and then identify and select the most appropriate predictive analytics methods and tools to answer the questions. Topics include: model tuning, hyper-parameter optimization, feature selection, resampling and cross-validation methods, and ensemble learning.
This capstone course provides an opportunity for students in the Business Analytics program to integrate and apply the analytics skills and knowledge learned in the previous courses to a large data analytics program. The Business Analytics Capstone Project gives you the opportunity to apply what you've learned about how to make data-driven decisions to a real business challenge. At the end of this Capstone, you'll be able to ask the right questions of the data, and know how to use data effectively to address business challenges of your own. You'll understand how cutting-edge businesses use data to optimize marketing, maximize revenue, make operations efficient, and make hiring and management decisions so that you can apply these strategies to your own company or business.