Business Applications of AI 10156122

Explores the use of AI in modern business environments, focusing on practical applications such as predictive analytics, customer relationship management, and automation. Students will analyze real-world case studies, assess the benefits and challenges of integrating AI into business operations, and explore AI-powered business tools and platforms.

Data Concepts 1 10156131

Data Concepts 2 10156132

Data Mining & Predicitve Analytics 2 10156111

Employs RapidMiner, R, and Orange software packages in order to explore text mining using classification algorithms. Uses k-nearest neighbor and decision trees to further explore classification on structured data. Lastly, learners evaluate time series data using forecasting algorithms. Learners demonstrate their findings via PowerPoint and short video presentations.

Data Mining & Predictive Analytics 1 10156109

Employs RapidMiner, R, and Orange software packages in order to facilitate exploration of clustering, association, and text mining algorithms. Learners import a variety of data and use the algorithms in the various software products to extract meaningful information. Learners demonstrate their findings via PowerPoint and short video presentations.

Data Mining & Predictive Analytics 3 10156115

Employs Python, Excel, R, and other GUI software to explore a variety of algorithms that fall under the umbrella of predictive analytics and data mining. Learners derive meaning from data using neural networks. Learners apply statistical models including linear and logistic regression. Lastly, learners evaluate data using Naïve Bayes and Bayesian Networks. Learners demonstrate their findings via PowerPoint and short video presentations.

Data Visualizations 1 10156113

Design charts, graphs, dashboards, and other visualizations with an understanding of color and chart type. Learners use the appropriate types of chart based on the data that is being presented and the audience that is viewing the presentation and also build charts for the purpose of exploratory data analysis.

Data Visualizations 2 10156117

Builds on Data Visualizations 1 and emphasizes choosing proper charts for quantitative and time-series analysis. Learners build effective dashboards and tell effective stories based on audience needs and analytical comfort. Learners contrast the ways in which data visualization can be used to tell truthful and untruthful stories.

Ethics and Implications of AI 10156123

Examines the ethical, legal, and societal implications of artificial intelligence (AI). Students will explore issues such as bias in algorithms, data privacy, and the broader impact of AI on employment and social structures and will utilize frameworks for ethical AI development and deployment, emphasizing responsible data-driven decision making.

Foundations of Data Engineering 10156124

Provides a comprehensive introduction to data engineering principles and foundational cloud computing for data. Students will learn to build and maintain scalable data pipelines, process and store large datasets, and implement cloud-based solutions for secure and efficient data management. With a focus on real-world applications, this course explores data ingestion, transformation, and storage strategies using AWS tools. By combining essential cloud computing skills with data engineering techniques, students will develop the expertise needed to support analytics, machine learning, and business intelligence workflows across diverse environments.

Introduction to AI 10156121

Introduces the fundamental concepts and history of artificial intelligence (AI), including machine learning, neural networks, large language models and natural language processing. Students will explore AI technologies, review their development over time, and study their impact on society. This foundation will provide students with the necessary framework to understand and work with AI in future courses and professional settings.

Introduction to Python 10156102

Introduces computer programming and terminology in the Python programming language. Special attention is paid to concepts essential to writing basic computer programs. These concepts include: Data Types, Expressions, Loops, File Interaction, Collections, and Functions. Additionally, several tools required to develop Python applications will be explored. Throughout the course learners will develop increasingly complex applications as new topics are introduced.

Introduction to R 10156114

Introduce learners to the foundational concepts of data programming in the open-source language R. Learners explore ways to visualize data, examine data structures, and model the relationship between variables using commands in R. Learners incorporate problem-solving and critical thinking skills as they write commands to support their work with data analysis.

Python for Data Analytics 10156103

Introduces topics and libraries related to data analytics in the Python programming language. Learners will explore reading, processing, and writing files in native Python. Then they will explore data analytics, processing and visualization using NumPy, Pandas, Matplotlib and Seaborn.