Applies all previous data analytics knowledge into one project. Learners perform (ETL) extraction, transformation, and loading of data. Learners query and analyze data. Learners create reports and visuals. Learners communicate results and findings to stakeholders.
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.
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.
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.
Introduces learners to machine learning using Python. Learners process data and construct visualizations. Learners write Python code to classify data. Learners use various Python libraries to perform both supervised and unsupervised learning. Learners use Python to perform natural language processing, build neural networks, and understand deep learning.
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.
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.
Introduces learners to the format and types of questions given in the math, chemistry, and anatomy & physiology portions of the HESI test. Sample questions and possible study materials will be discussed. Upon completion of the course, learners will have a greater understanding of the test. Additionally, course completion will assist in determining if learners feel ready to test, or would like to pursue additional study opportunities prior to testing.
Examines proven strategies designed to help learners achieve greater personal, academic, and professional success. Learners will apply personal responsibility thinking and behaviors; self- management, awareness, and motivation strategies; as well as interdependence skills to develop a proactive life plan.
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.
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.
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.
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.