The Post-Baccalaureate Diploma in Applied Data Science Program:
Students are required to take six applied data science courses covering mathematics and statistics concepts, and nine applied data science courses in computing science including two one-year project courses. Support courses include one communications course.
Semester 1: 15 credits
- ADSC 1000 Intro to Statistical Data Analysis
- ADSC 1010 Data Visualization and Manipulation through Scripting
- COMP 1110 Introduction to Computer Programming
- CMNS 1290 or CMNS 2290 (requires approval from Program Advisor)
- ADSC 1910 Introduction to Applied Data Science
Semester 2: 15 credits
- ADSC 2020 Regression for Applied Data Science
- ADSC 2030 Design for Data Science
- ADSC 2110 Introduction to Applied Data Science with Python
- ADSC 2610 Database Systems in Applied Data Science 1
- ADSC 2910 Applied Data Science Integrated Practice 1
Semester 3: 15 credits
- ADSC 3040 Simulations for Modeling, Optimizing & Analysis
- ADSC 3610 Database Systems in Applied Data Science 2
- ADSC 3710 Artificial Intelligence in Applied Data Science
- ADSC 3910 Applied Data Science Integrated Practice 2
- ADSC 3920 Applied Data Science Project 1
Semester 4: 15 credits
- ADSC 4050 Multivariate Statistics for Applied Data Science
- ADSC 4710 Machine Learning in Applied Data Science
- ADSC 4720 Data Mining in Applied Data Science
- ADSC 4910 Applied Data Science Integrated Practice 3
- ADSC 4920 Applied Data Science Project 2
ADSC 1000 Introduction of Statistical Data Analytics
Students are introduced to a survey of basic concepts of data analysis and statistics with a variety of applications in each concept. Students explore probability and how data collection impacts analysis. Students are introduced to some methods of inference including estimations and testing and their applications. Students are introduced to the basics of regression analysis. Emphasis is placed on computational approaches rather than classical approaches.
ADSC 1010 Data Visualization and Manipulation through Scripting
Students are introduced to methods of processing and conveying data summaries targeted to various audiences. Students learn scripting skills to manipulate data between various types and formats. Students also learn different methods of summaries, including visualizations, after processing in a variety of contexts.
COMP 1110 Introduction to Computer Programming
Students are introduced to the use of structured problem-solving methods, algorithms, and structured programming. Students use a high level programming language to learn how to design, develop, and document well-structured programs using software engineering principles. Students learn the workings of a computer as part of programming. In a laboratory setting, through critical thinking and investigation, students will iteratively design and build a variety of applications to reinforce learning and develop real-world competency in Computer Programming. This course is for students who plan to take further courses in Computing Science or to learn basic programming concepts. Prerequisite: English Studies 12 with a minimum grade of C.
CMNS 1290 Introduction to Professional Writing
Students study the theories and practice of professional organizational communication, learning the importance of effective communication to meeting goals, developing and maintaining relationships and the overall facilitation of work. Students develop skills in evaluating communication scenarios, designing communication strategies that meet goals and audience need, including requests, information sharing and persuasion. In addition, students learn to employ writing techniques and editorial skills relevant to professional communication contexts.
CMNS 2290 Technical Communication
Students study a variety of technical communications used to document professional activity, including proposals, technical and formal reports, policies and procedures, technical descriptions and definitions, and instructions. Students learn the importance of documentation and accountability as part of professional due diligence, applicable across many fields including journalism, business, government, public service, consulting and research institutes. Students develop skills in assessing communication needs in a scenario, identifying communication goals, audience needs and relevant media. Finally, students learn skills in research and synthesis to ensure professional engagement and presentation of research material.
ADSC 1910 Introduction of Applied Data Science
This course will introduce the learner to the basics of Data Science. Data Science refers to the techniques used to analyze data to enhance productivity and business gain. This course is a practical introduction to the tools that will be used in the Post-Baccalaureate diploma in Applied Data Science. In this course, students will apply the main tools used in Applied Data Science including the R programming language, Matplotlib for data visualizations, dplyr for data manipulation, tidyr for reshaping data, ggplot2 for visualization of data, and interactive visualization in R. Additional tools will include version control, markdown, git, GitHub, and RStudio. By the end of this course, students will be able to apply the knowledge from term one of the Post-Baccalaureate in Applied Data Science to tabulate data, clean it, manipulate it, and run basic inferential statistical analyses on it to draw meaningful information from data.
ADSC 2020 Regression for Applied Data Science
Students are introduced to applications of regression-based concepts. Students learn a variety of concepts related to the simple linear regression model including coefficient of determination and basic inferences. Students extend their understanding and application to other linear regressions such as multiple and logistic regressions. Students learn how to perform other variants of regression including time-series and nonparametric regression. Students learn various methods of diagnostics, types of fallacies, and other issues that can arise in regression.
ADSC 2030 Design for Data Science
Students build upon knowledge of regression in further applications, particularly in experimental design. Students learn how to create different kinds of samples with various properties, and how to analyze such samples after the data has been collected. Students learn how to frame these designs in a regression framework to build upon existing knowledge in new situations such as models involving blocking, factors, and hierarchies. Students learn how to perform the corresponding inferences, such as (multiple) analysis of (co)variance.
ADSC 2110 Introduction to Applied Data Science with Python
Students are introduced to the basics of the python programming environment as used in Applied Data Science. Students learn data manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Students explore the IPython and Jupyter, computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labelled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms.
ADSC 2610 Database Systems in Applied Data Science 1
Students are introduced to the basic ways that data can be obtained. Students learn how to obtain data in various formats from the web, from APIs, from databases, and from colleagues. Students explore the framework of the data life cycle, data loading, cleaning, and pre-processing.
ADSC 2910 Applied Data Science Integrated Practice 1
This course will introduce the learner to the tools necessary for Applied Data Science. Students learn the Python Applied Data Science and SQL tool sets necessary for Applied Data Science. By the end of this course, students will be able to apply the knowledge from term two of the Post Baccalaureate in Applied Data Science to obtain data from the Web and use the Python Applied Data Science Engine and SQL and R to interface to data sets and draw meaning from them.
ADSC 3040 Simulations for Modeling, Optimizing & Analysis
Students are introduced to the basic concepts of using the computer to analyze and optimize through modelling and simulation for decision making. Students explore creating appropriate objective functions for risk analysis and incorporating. Students learn various optimization techniques and approaches for simulation for modelling and their application to data analysis. Students are also introduced to graphs for network analysis.
ADSC 3610 Database Systems in Applied Data Science 2
Students learn the fundamentals of database design, modelling, systems, data storage, and the evolving world of data warehousing, governance and more. Students are introduced to big data, analytics, data quality, and data integration, up-to-date approach to database management. Students explore NoSQL, Data Integration, Data Quality, and Data Governance and Big Applied Data Science. Students learn fundamental concepts using real-world examples, query and code walkthroughs, including MySQL, MongoDB, Neo4j Cypher, and tree structure visualization.
ADSC 3710 Artificial Intelligence in Applied Data Science
Students are introduced to the principles of artificial intelligence (AI) through an exploration of its history, capabilities, technologies, framework, and its future. Students learn the implications of AI for business strategy, as well as the economic and societal issues it raises. Students develop a small-scale AI application.
ADSC 3910 Applied Data Science Integrated Practice 2
Students will be introduced to the tools necessary for developing applications using Artificial Intelligence (AI) tool set, and integrate this with Large Data Bases and Date Warehouses. By the end of this course, students will be able to apply the knowledge from term three of the Post-Baccalaureate in Applied Data Science using Artificial Intelligence and integrate it with Large Data bases.
ADSC 3920 Applied Data Science Project 1
This course is designed as the first phase of a capstone project in the Applied Data Science Post-Baccalaureate and includes the practical design and implementation of a supervised project in an area of specialization in Data Analytics. The students will, in a team environment: develop a project proposal, complete a data collection and/or experiment design, and develop a project implementation plan. A part of their learning experience will include working with an external client.
ADSC 4050 Multivariate Statistics for Applied Data Science
Students explore various multivariate statistical techniques to handle large datasets. Students learn various methods of dimension reduction and feature selection including PCA, CCA, SVD, and factor analysis. Students learn how to manipulate a variety of established learning algorithms such as k-Means clustering and hierarchical clustering. Students also learn some classic supervised techniques such as discriminant analysis and classification trees which extend to random forests. Students learn about boosting and bagging to improve prediction.
ADSC 4710 Machine Learning in Applied Data Science
Students are introduced to machine learning, focusing more on the techniques and methods than on the statistics behind these methods. Students learn core topics of machine learning, with a focus on applying existing tools and libraries of machine learning code to problems. Students explore practical considerations, such as preparation and manipulation of data, relevant theory and concepts key to understanding the capabilities and limitations of machine learning. Students are introduced to a number of the main machine learning methods such as preparation and manipulation of data, supervised (classification) and unsupervised (clustering) technique. Students learn to apply and write python code to carry out an analysis.
ADSC 4720 Data Mining in Applied Data Science
Students are introduced to the machine learning landscape, particularly neural nets. Students learn the use of Scikit-Learn to track an example machine-learning project end-to-end. Students explore several training models, including support vector machines, decision trees, random forests, and ensemble methods. Students are introduced to the TensorFlow library to build and train neural nets, neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning. Students learn techniques for training and scaling deep neural nets.
ADSC 4910 Applied Data Science Integrated Practice 3
This course will introduce the learner to the tools necessary for Applied Data Science. Students learn the tools necessary to do data mining on large data sets. By the end of this course, students will be able to apply the knowledge from term four of the Post-Baccalaureate in Applied Data Science to integrate machine learning, Artificial Intelligence and large data sets and draw meaning from them.
ADSC 4920 Applied Data Science Project 2
This course is designed as the second phase of a capstone project in the Applied Data Science Post-Baccalaureate and includes the practical design and implementation of a supervised project in an area of specialization in Data Science. The students will, in a team environment, complete the development of a “live project” and part of their learning experience will include working with an external client.