What is Data Analytics?

Data analytics is the process of collecting, analyzing, and using data to identify patterns, trends, and insights that help organizations make decisions, develop strategies, manage operations, and forecast future outcomes. It brings together statistics, programming, artificial intelligence, analytics, and machine learning.

Why Study Data Analytics & Artificial Intelligence at Champlain?

Some schools bury this kind of program under computer science — Champlain lets it stand on its own. You'll learn to apply core tools like statistics, machine learning, and data platforms, while gaining a broad view of different fields within AI. Unlike a computer science degree, this program doesn't require you to code like a professional software engineer. It also doesn't require deep theoretical mastery. Instead, you'll build working competency across both math and computer science, so you're equipped to apply data analytics and AI wherever your interests take you. Plus, with our innovative curriculum, you'll pair your technical skills with a field you’re interested in, whether that's business, cybersecurity, or health, so you graduate with expertise in both that field and data science.

What Will I Learn While Studying Data Analytics & Artificial Intelligence?

By the time you graduate, you will… Write and debug code using the programming languages and tools common in data analytics and AI Collect, clean, and explore data to spot patterns, trends, and anomalies, and use them to inform decisions responsibly Build and test machine learning models for common tasks like classification, regression, and clustering Apply core statistics and math concepts to interpret data and evaluate how well your models are working Communicate your findings clearly through reports, visuals, and presentations to both technical and non-technical audiences Apply ethical judgment as a built-in habit, not a single unit — every course threads responsible, ethical AI use through the work itself

Curriculum for Data Analytics & Artificial Intelligence Degree

Champlain's Upside-Down Curriculum puts hands-on experience at the center of your data analytics and artificial intelligence education, so you graduate ready, not just qualified. Explore the data behind every decision, and the tools behind the tool.

Data Analytics and Artificial Intelligence at Champlain College prepares students to work confidently with data and AI tools in professional settings. The program starts with the fundamentals — programming, statistics, and data analytics — and builds into the technical depth to understand and adapt the frameworks and models that power industry-standard tools today.

Along the way, you will learn to collect, clean, and interpret data, and use it to support strategic insights and decisions, considering ethical implications — bias, privacy, and responsible use — in every aspect of your work. You’ll also have 30 flexible credits to pursue opportunities for internships, co-ops, and study abroad. By graduation, you’ll be ready for roles like data analyst, business intelligence analyst, or AI product specialist, with skills that transfer across industries and job functions.

Learning Outcomes for Data Analytics and Artificial Intelligence.

Formative Knowledge

FRM 114 Knowledge Making Through Science

There is a difference between what science is, its history and meaning, and what science does through experimental and applied practices. This interdisciplinary course invites students to examine how scientific knowledge is defined, constructed, and communicated. Students will critically analyze different examples of science in society to understand how science shapes and is shaped by culture, power, and people. By the end of the course, students will be equipped to evaluate the ways in which science reflects and transforms the world around us.

Prerequisites
Credits	3

FRM 124 Global Contexts of Technological Change

How does technological change impact people across the globe? What gets lost-or found-in translation? Through interdisciplinary analysis, this course examines technological change as a global phenomenon. We will examine how innovations from one perspective can be exploitative from another, investigating how technologies evolve and affect each other across different times and places. The course emphasizes ethical inquiry and considers how the economic, political, and cultural forces driving technological change affect people's lives differently depending on context, identity, and access. Students will leave with a deeper understanding of how science and technology shape and are shaped by the world.

Prerequisites
Credits	3

FRM 134 Engaging W/ Complexity: Systems Thinking

This course equips students with interdisciplinary tools and methods to analyze and respond to complex, real-world problems. Drawing on diverse frameworks and theories, students will explore how their own identities and experiences shape their understanding of contemporary issues. Emphasis is placed on synthesizing knowledge across fields to develop innovative, contextually relevant solutions. Students will communicate their insights effectively through a range of expressive modes tailored to varied audiences and purposes.

Prerequisites	Complete FRM-124
Credits	3

Data Analytics

MTH 180 Statistics, Introduction to

Students will develop a statistical toolkit for navigating an uncertain world. From summarizing data and understanding probability to testing hypotheses and modeling relationships between variables, students will gain practical skills for making evidence-based decisions. Through applying statistical methods to real community issues and challenges at various scales, students will learn to integrate quantitative reasoning with broader interdisciplinary perspectives, building the analytical and social awareness necessary for responsible engagement with complex problems in their personal, professional, and civic communities.

Prerequisites
Credits	3

—OR— MTH 280 Applied Statistics

Teaches the practical side of exploring, presenting, and analyzing data. Students will learn to effectively use a statistical software package to accomplish these tasks. After learning to graphically display data in meaningful ways, students will learn to analyze data, test claims, and make valid conclusions. These analyses will include hypothesis tests involving more than one sample, linear and multiple linear regressions, chi-squared tests of independence and goodness of fit, and ANOVA.

Prerequisites	MTH-180 or MTH-380
Credits	3

—OR— MTH 380 Probability and Statistics

This course will introduce students with a background in Calculus to the fields of probability and statistics. Topics include discrete and continuous probability, key terms and theorems, and different statistical distributions and models. Students will work with concepts by-hand and with technology in order to develop a greater understanding of the structure and applications of probability and statistics as a whole.

Prerequisites	Take MTH-230
Credits	3

DAT 210 Data Analytics

Students will analyze data using Python programming language to obtain insights from real-world data sets. Students will access data files containing both numerical and categorical data, which they will prepare for analysis by data preprocessing, data wrangling, and data aggregation. Students will conduct statistical analysis, text analysis, and date/time series analysis, summarize their findings in a meaningful manner, and use visualization to illustrate patterns and trends in the data.

Prerequisites	Complete MTH-180 and any CSI introductory programming course
Credits	3

DAT 230 Data Visualization

Data visualization-representing complex data in a visual format-is a way to communicate and explore patterns, trends, and relationships in a data set. In this course, students will examine a variety of data sets to create data visualizations and use them to explore, interpret, and present information. After transforming data sets into visual graphics, students will then create business reports and presentations designed to drive effective decision-making.

Prerequisites	Complete 15 credits
Credits	3

Computer Science

CSI 140 Introduction to Programming

Students learn the history and basics of computing, as well as the fundamentals of programming. General topics include: the history of computing, number systems and classical computing, Boolean logic, algorithm design and implementation, and modern computer organization. Programming topics include: memory and variables, mathematical operations, basic input and output, decision making, repetitions, functions, and arrays.

Prerequisites
Credits	3

CSI 240 Advanced Programming

This course builds on the content from Introduction to Programming. Students study the Object Oriented Programming (OOP) Paradigm with topics such as objects, classes, encapsulation, abstraction, modularity, inheritance, and polymorphism. Students examine and use structures such as arrays, structs, classes, and linked lists to model complex information. Pointers and dynamic memory allocation are covered, as well as principles such as overloading and overriding. Students work to solve problems by selecting implementation options from competing alternatives.

Prerequisites	Complete CSI-140 with a C or better
Credits	3

CSI 281 Data Structures & Algorithms

Students compare and contrast a variety of data structures. Students compare algorithms for tasks such as searching and sorting, while articulating efficiency in terms of time complexity. Students implement data structures and algorithms to support solution designs.

Prerequisites	CSI-240 with minimum grade of C or better.
Credits	3

Applied AI

CSI XXX Intro to AI / Rapid Prototyping

CSI 300 Database Management Systems

This course will introduce students to database design, Structured Query Language (SQL), normalization, and relational database theory. Traditional relational databases will be contrasted with NoSQL paradigm databases including document-oriented, key-value store, and graph. Students will gain hands-on experience writing database applications.

Prerequisites	Complete CSI-270 OR CSI-281, with a minimum grade of C.
Credits	3

DAT 330 Data Mining

This course introduces students to data mining and its various applications. Students will gain proficiency in supervised data mining techniques for building prediction models such as decision trees, random decision forests, bootstrapping, training and testing using multi-fold cross-validation and using entropy measures for weighting features. Students will also use unsupervised data mining techniques such as clustering and association analysis. Both methods are used for discovering patterns and associations in data.

Prerequisites	Complete DAT-210
Credits	3

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