Course Listing For Data Science Courses

This course is an introduction to the field of data science and the skills required to be a data scientist. The course explores the basics of data science including: vocabulary, common programming languages, data visualization, presentations, data analysis, the history of information, data ethics, and the data science process. Students should have a better understanding of how they generate data and how data science impacts them as a consumer of this information. Prior programming experience is not needed for this course.

As data becomes more abundant and essential in various industries, accuracy, timeliness, and data-driven decision-making are critical skills. Individuals with strong data management, visualization, and presentation skills are better equipped and positioned to contribute meaningfully to their organization. This course is tailored for those individuals seeking to apply essential business tools for data management and manipulation, as well as the presentation of that data to help make informed decisions. Through hands-on learning, participants will acquire the skills to succeed in data manipulation, analysis, and presentation using Excel and PowerPoint. Prerequisite: None

Data analytics enables professionals to examine large datasets using various techniques to interpret and understand data to make decisions and drive business solutions. This course provides participants with a comprehensive introduction to the field of data analytics. Participants will learn about the importance of data analytics and its role in business decision-making. Participants in this course will be introduced to data collection and preparation, risk identification and mitigation, and exploratory data analysis. Additionally, participants in this course will learn the ethical considerations and privacy concerns associated with data analytics, emphasizing best practices for data protection and anonymization. Through case studies and real-world examples, participants will gain a solid foundation in data analytics and its practical applications in the workplace. Prerequisite: None

Workplace storytelling is the art of using different techniques to share ideas and connect with various professional audiences. This course equips participants with the skills to transform data into engaging and narrative presentations. Participants will learn strategies for data transformation, narrative development, and visual representation. They will also discover techniques for simplifying and decluttering data, identifying compelling stories, and understanding and engaging the audience. This course emphasizes ethics, credibility, and authenticity in workplace storytelling and teaches how to communicate data-driven insights effectively in a professional and engaging manner. Prerequisite: None

This course introduces the architecture, hardware, and software utilized for data science projects. Fundamental terminology, definitions, and data architecture concepts will be covered. Students will explore case studies and examples to understand the opportunities and challenges that architectural decisions impose on data science.

This course prepares students for the methodologies and processes required to execute a data science project. Students will learn about the critical skills required for initiating and delivering a data science project with business value: research, project management, problem solving, decision making, requirements gathering, and data analysis. This course also prepares students for making a project operational and focuses on tasks required to deploy and automate projects.

The major focus of this course will be the relational, dimensional and NoSQL models. Topics include relational and dimensional modeling, business intelligence, NoSQL databases and their application, SQL, application development using databases and emerging trends. Students will prepare a small application using a commercial database management system.

In this course, students will use various techniques and tools to explore, visualize, and present data. Students will be exposed to R, Tableau, and PowerBI to perform initial analysis and view data. Students will use statistics and programming to ask and answer insightful questions regarding data, while also learning basic storytelling and presentation concepts. Students will learn innovative ways to communicate with different levels of leadership and stakeholders.

In order to fully analyze data, mathematical concepts need to be applied to data. This course focuses on the common statistics, algorithms, and models required for data mining and predictive analytics. Some of these concepts will include: Bayesian statistics, Bayesian models, calculus concepts to understand probability distributions, and basic linear algebra. Students will learn how to problem solve and identify the right methods to apply during their analyses. Prerequisite: MA 215 Applied Statistics

It is estimated that data scientists spend about 80% of their time finding and cleaning data. The data currently being produced is infinitely variable in its structure, presentation, and scale. This course prepares students for dealing with this infinite variety of data and how to interact with disparate sources of data. Students will be exposed to data structures and data management via Python, SQL, and other tools teaching them how to acquire, prepare, clean, and automate dataset creation. Prerequisite: CIS 245 Intro to Programming.

In this course, students will apply the concepts previously learned about statistics, algorithms, and models to interact with data for the purpose of predictive analytics. Predictive analytics has the capability to help organizations identify potential impacts to their business and to support business decisions. Concepts that will be covered include: bias/variance trade-off, over-fitting and model tuning, regression models – linear, nonlinear (SVMs, K-nearest neighbors), regression trees, classification models – logistic regression, random forest, dealing with unbalanced data, feature selection, and predictor importance. Prerequisite: DSC 350

Comments, chats, logs, etc., are rich with customer feedback and insights that if analyzed can drive business decisions and potentially reduce costs. The challenge is generating meaning and context when the data quality and type varies. This course focuses on text processing and interacting with unstructured data. Techniques for mining unstructured data such as text pre-processing, tokenization, corpus preparation, machine learning algorithms, N-gram language model, word and document vectors, and text classification will be covered in this course. Prerequisite: CIS 245 Intro to Programming.

With the cost of data storage consistently decreasing, data volumes are increasing and organizations are no longer forced to only store the bare minimum data. This course examines the technology required to analyze and process Big Data. Topics include: Hadoop/MapReduce, Spark/RDD, Spark/Storm Streaming, TensorFlow, Keras/Deep Learning, Kubernetes, and Docker. Prerequisite: DSC 360 Data Mining. Recommend: DSC 350 Data Wrangling for Data Science.

Generative Artificial Intelligence (GAI) is arguably one of the most transformative developments in information technology history. With uses of GAI ranging from creating essays to generating entire videos, this technology affects every industry, directly or indirectly. This course prepares students for a life of GAI by giving a thorough introduction to the evolution leading to GAI, delving into how large language models (LLMs) can work with text, and how images can be created and manipulated using GAI. Students will also explore prompt engineering and retrieval augmented generation, which is how GAI is used and grounded in truth. Prerequisites: DSC 360 Data Mining: Text Analytics & Unstructured Data (Required), DSC 400 Big Data, Technology and Algorithms (Recommended)

In the final course of the Data Science program students have the opportunity to demonstrate their understanding of data science by completing a term project that takes them from idea/hypothesis to presentation. Students will gather data, prepare, clean, analyze, and present their analysis and recommendation. Students will finalize their data science portfolio based on work completed throughout the program. Students will also collaborate with each other to prepare for interviews. Prerequisite: Successful completion of all other required DSC courses.

This course introduces the possibilities, history, and ethics surrounding Data Science. Basics of data science are explored, including vocabulary, programming languages, big data frameworks, visualization, and statistics. Prior programming experience is not needed for this course.

This course introduces the Python programming language as a tool to clean, slice, and build tools to analyze an existing dataset. Basic principles of programming are explored as well as techniques for configuring a computer for data science work. Prerequisite: Recommend DSC 500

The R programming language and software environment is commonly used to explore all types of data. Using R, students perform statistical tests on the data. Report writing and presentation of data are introduced. Prerequisite: Recommend DSC 500

This course introduces complex techniques needed for profiling and exploring data. Students use programming and statistics-based inference to ask and answer insightful questions of data. Prerequisite: Recommend DSC 510 and DSC 520

Much like life, the data humans produce is infinitely variable in its structure, presentation, and scale. This course prepares students for this infinite variety of data. Students use Python, SQL, and other tools to acquire, prepare, clean, and automate dataset creation. Prerequisite: DSC 510 or equivalent and recommend DSC 530

Data can often contain patterns and anomalies that only emerge at large scale. In this course, you will import, clean, manipulate, visualize, analyze, and model structured and unstructured data to extract this information. Model building topics covered include text sentiment analysis, regression, classification, and neural networks. Furthermore, you will learn how to perform feature dimensionality reduction and tune model hyperparameters. The knowledge learned in this course culminates in a term project. Prerequisite: Recommend DSC 540

This course assembles topics covered in previous courses into an applied project. Students have the opportunity to find, clean, analyze, and report on a project they define. Advanced methods of analysis using Python and R allow students to delve deeper into their projects. Prerequisite: DSC 540 or equivalent and recommend DSC 550

Data scientists should be great storytellers, whether using visual, text, or other means. In this course, students explore the basic storytelling components of data science and apply them to different types of data for different types of clients and audiences. Presentation techniques, language use for different audiences, and visualization tools techniques are included. Prerequisite: Recommend DSC 630

This course covers the fundamentals of data infrastructure and how technologies fit together to form a process, or pipeline, to refine data into usable datasets. This course focuses on building a predictive modeling pipeline used by the various types of projects that are called, “big data.” Prerequisite: Recommend DSC 540

Generative Artificial Intelligence (GAI) is arguably one of the most transformative developments in information technology history. With uses of GAI ranging from creating essays to generating entire videos, this technology affects every industry, directly or indirectly. This course prepares students for GAI by showing how to apply it, delving into how large language models (LLMs) can work with text, and how images can be created and manipulated using GAI. Students will also explore prompt engineering, retrieval augmented generation, and model turning, which is how GAI is used, grounded in truth, and altered for specific uses. Finally, the course teaches students how to deploy custom LLMs, and how to architect and build applications around LLMs. Prerequisites: DSC 630 Predictive Analytic

In the final course of the Data Science program, students will conduct several data science projects from origin to presentation. Students will gather data, then prepare, clean, analyze, and present their analysis to an audience. Prerequisite: Completion of all other required DSC courses