9 courses with the subject DA, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
DA 201Introduction to Data Analytics3
Introduces the importance of data management, data analysis and data representation for organizations. Explains the science of examining raw data with the purpose of drawing conclusions about that data. Covers how organizations can successfully collect, organize, manipulate, use, prospect and present data. This course will familiarize students with the concept of data analytics and its applicability in a business environment.
DA 202Multivariate Statistics, Probability, Multiple Regression, Linear Regression3
Introduces various statistical methods for analyzing more than one outcome variable and understanding the relationships between variables. Topics include a variety of multivariate models such as MANOVA, discriminant functions, canonical correlation, and cluster analysis. Covers the laws of probability and numerous important discrete and continuous random variables.
Introduces concepts and practical implementation of database and data warehouse systems. Material covers the dominant relational model (Codd, 1970), Entity modelling, database queries using SQL. Data Warehouse(DW) elements include Dimensional modelling, different DW schemas and the use of OLAP Cubes. This is a broad overview.
DA 301Spreadsheet Data Analysis and Business Modelling3
An introduction to Data Analysis and Business Modelling via the medium of a spreadsheet tool (Microsoft Excel). This course covers the use of Excel to ask and answer important business questions. Material will cover Pivot Tables, Descriptive Statistics, trend curves, multiple regression, exponential smoothing, financial, statistical, and time functions, Monte Carlo simulations on stock prices and bidding models, basic probability and Bayes’ Theorem.
An introduction to data visualization and how it can be used to understand data and support decision making. Course will cover fundamental visualization concepts and introduce students to commercial visualization tools such as Tableau. Students will learn to clean, and import data. Charting, dates, table calculations and mapping. We’ll explore the best choices for charts, based on the type of data you are using. Course will cover different types of charts and how to choose the most appropriate ones.
DA 303Data Mining for Business Intelligence including Predictive Analytics3
Building on DA 203 (Database/Data Warehouse) this course will delve into Analytical techniques for finding hidden patterns in large data sets. Material will include classification, prediction, recommendation, data reduction, supervised learning and predictive analytics.
DA 401Machine Learning and Artificial Intelligence3
Machine learning is a subset of the field of Artificial Intelligence. This course will give an overview of the principles and applications of AI and will then focus on Machine Learning (ML) . ML is a tool used to build predictive models by means of eliciting patterns from large bodies of data. Such models can be used in areas such as price prediction, risk assessment and predicting customer behavior. This course will introduce commonly used ML languages such as R and Python. The course will cover the core concepts and techniques used in several machine learning approaches, with substantial practical applications from a business point of view.
DA 402Data Analytics Applications Development Part 13
Part of a two course capstone this course builds on the theoretical and practical underpinnings and requires students to build working Data Analytics applications to address client requirements. Students gain valuable experience with industry standard data analytics applications development tools. Course involves requirements analysis, design and first cut implementation.
DA 403Data Analytics Applications Development Part 23
The 2 nd part of a two course capstone. This course picks up where DA402 finishes with students incrementally implementing refinements, upgrades and (hopefully few) bug fixes to the applications developed in DA402.
