Everyone communicates, but are they doing it well? Communicative competence takes practice and self-awareness. By developing their communication skills, the student will improve their interpersonal ability, intercultural competence, and digital fluency to prepare the student for success in the workplace. In Communication Strategies, the student will learn through discovery and project-based activities to practice approaching situations critically and collaboratively. The strategies the student will gain in this course will be useful throughout their program and in their chosen industry.
This course reinforces the communication strategies practiced in COMM-1173 to provide students with the communication skills needed to apply for, stay in, and progress in the world of work. Students will further develop their interpersonal skills and digital fluency by seeking college, industry and community supports. Additionally, they will apply speaking, writing, and collaboration strategies to getting the job, adjusting to the job, and developing long-term career goals. Communication for the Workplace is a creative and active learning opportunity to set students apart in today’s competitive job market.
Students will build on the skills they practiced in Communication Strategies by focusing on the information technology sector. Students will develop their ability to think at a systems level by analyzing problems to come up with innovative solutions. Learners will collaborate to manage, analyze, and communicate information to various audiences across different channels. This collaboration will involve active listening, networking, and persuasion strategies in an information technology context.
This course is intended to serve as an introduction to programming concepts. Students will be introduced to high-level modeling and common numeral systems used by computer programmers. Boolean operations will be explored with importance placed on the student’s ability to analyze, interpret and re-write word problems as Boolean expressions. Students will explore other core concepts such as assignment, sequence, iteration, decision, modular abstraction, arrays, and strings.
This is a data-focused course to develop confidence with quick data handling, parsing, structuring, and manipulating datasets for various database types. By viewing, understanding, and normalizing datasets, students will produce Entity Relationship Diagrams (ERDs) and other visual data schemas. Students will learn basic Structured Query Language (SQL) and NoSQL (not only SQL) data types, key-value pairs, and document stores. Students will develop basic to advanced commands including complex JOINs, advanced mathematical and string functions, and full-text search indexing functions. Students will tune the performance and execution times of queries using common practices of indexing and de-normalization.
In this course, students will be introduced to the fields of Data Science and Machine Learning (DSML) and how they are used in real business applications. Students will get an introduction to the industry standard tools and technologies used in this field and learn definitions and meanings of common terms. They will analyze real case studies of how industry has applied the tools of DSML to improve their performance. By the end of this course, students will be able to contrast how DSML tools have impacted performance metrics in industry, compared to conventionally used methods.
This course is an introduction to some of the basic techniques and algorithms of bioinformatics through coding challenges in an industry standard programming language. Topics covered include locating ori-C in small genomes, finding regulatory motifs in small genomes, graph algorithms, and the genome reconstruction problem.
Learn the fundamentals of Python programming and data analytics. Starting with the fundamental building blocks, this course will focus on teaching Python programming fundamentals before moving to more comprehensive examples. The course will also introduce students to data science and machine learning as they are used in business applications. Using tools such as the Jupyter Notebook, NumPy, Pandas, Matplotlib and Seaborn, you will learn about the basics of interpreting and preparing data for analysis.
This course covers steps to manipulate and manage data from raw source formats to functional structures where it can be exploited more readily as a valuable information asset. Students will learn industry standard techniques to inspect and visualize data for statistical, aggregate, and design pattern characteristics, and then manipulate the data into suitable representations within relevant data genre models that include relational, document, and network databases. Students will also learn methods to maintain data security using encryption, anonymization, sanitization, roles access, and walled infrastructures. Furthermore, learners will acquire competencies in maintaining data integrity through versioning, backups, archiving, and restoration approaches at various stages of an established data pipeline.
Supervised machine learning is a subfield of machine learning where algorithms are trained on labelled data to classify items or predict outcomes. This course builds upon concepts to describe how supervised learning algorithms are constructed and coded. Students will use Python to develop the code for supervised learning algorithms including polynomial regression, support vector machines and decision trees; data will be used to train, validate and test these models for common use cases in business and data science.
In this course, students will create blueprints for data management systems, identify potential data sources (internal and external), and create a plan to integrate, centralize, protect and maintain information and data.
Artificial intelligence (AI) is the ability of computers to learn from data and make decisions by running code. In this course, you will learn the role of logic and probability in AI algorithms, and how statistical machine learning and neural networks are used. These tools will be applied in the completion of course projects where you will develop code for important AI use cases.
Deep learning is one of the most important recent advancements in machine learning, with an ever-growing list of applications that include finance, medicine, computer vision, and language processing. The course first introduces the perceptron as a fundamental building block before moving onto more complicated neural network architectures. Students learn how leading architectures are constructed from tools in linear algebra and how to develop, train and test these networks using code.
Unsupervised machine learning is a subfield of machine learning where models are trained to identify clusters and find relationships in unlabelled data. This course builds upon concepts from previous courses to describe how unsupervised learning algorithms work, as well as how they are constructed and coded. Students will use Python to develop the code for clustering models, Autoencoders and topic models; real data will be used to train, validate and test these models for common use cases in business and data science.
This course is an introduction to the exciting field of robotics and automation. Working with Robot Operating System (ROS2) locally and cloud services like AWS RoboMaker, students will gain experience with important concepts such as vision, motion control and processing sensor data. Students will learn how Robot Operating System interacts with and controls physical hardware.
An understanding of statistics is fundamental in the study of data science and machine learning. This course is designed to familiarize students with sampling methods and estimations, presenting and describing data, probabilities and hypothesis testing.