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Data Science

Bachelor of Science Degree

Accessing, understanding and sharing data are key skills for business strategy today. Those who can do this well are highly sought professionals. In this program, you'll learn how to use Python to wrangle data and solve problems, data visualization using R, Tableau and PowerBI, data analysis and management, and much more.

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Courses in the major include:

This course provides an introduction to problem solving and computer programming using the language Python. Students will analyze problems, design and implement solutions and assess the results. Topics include fundamental programming constructs such as variables, expressions, functions, control structures and lists. Emphasis is placed on numerical and data analysis for informed decision making. Prerequisite: None
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.
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.
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.
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.
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 360 Data Mining. Recommend: DSC 400 Big Data, Technology and Algorithms
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 provides the theoretical basis and problem-solving experience needed to apply the techniques of descriptive and inferential statistics, to analyze quantitative data, and to improve decision making over a wide range of areas. Topics covered include descriptive statistics, linear regression, data gathering methodologies and probability, as well as confidence intervals and hypothesis testing for one and two samples. Use of technology in solving and interpreting statistical problems is emphasized. Prerequisite: MA 101 or placement via ALEKS Placement Assessment

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