Computer Systems Fundamentals — 48 hrs

This introductory course is designed to provide students not only a comprehensive grounding in fundamental knowledge of computers but also some of the latest advances in technology. More specifically, using both lectures and laboratory practices, the course would introduce students to essential computing concepts in hardware, software, data storage and manipulation, operating systems, programming, e-commerce, networking and the Internet. Students will gain hands-on experience with different operating systems including Windows, Linux, and Mac, application software packages including spreadsheets, word processing, and presentation, security software, email, the World Wide Web, and the Internet. Students will also learn techniques of refined searching, evaluating, and validating information on the Internet. Furthermore, the course will provide a very basic understanding of advanced and emerging computing technologies including artificial intelligence, machine learning, cloud computing, virtual and augmented reality, Internet of things, blockchain technology, quantum, fog, and edge computing, and 5G technology. Finally, the course would examine the social, legal, and ethical aspects of computing including privacy, data protection, intellectual property, health concerns, and accessibility.

Discrete Maths for Computing— 48 hrs

The main goal of this course is to provide the mathematical foundations for many computer science courses, with particular emphasis on sets of mathematical facts and how to apply them; more importantly, this course will teach students how to think logically and mathematically. The course will start with a discussion of mathematical logic that serves as the foundation for mathematical reasoning and constructing mathematical arguments. The course would further teach leaners how to work with discrete structures, which are the abstract mathematical structures used to represent discrete objects and relationships between these objects. These discrete structures include sets, permutations, relations, graphs, trees, and finite-state machines. Furthermore, students will learn how to write efficient algorithms for solving computational and real-world problems and how to illustrate the analysis of the complexity of algorithms, focusing on the time an algorithm takes to solve a problem. The course will also focus on combinatorial analysis, which is an important problem-solving skill to count or enumerate objects. Finally, graph theory would be introduced to provide an understanding of graph models and its applications in computing and how graph theory can be applied to solve computing and real-world problems.

Programming Languages— 48 hrs

This introductory course is the first of its kind in computer programming which is designed to introduce essential programming and scripting concepts to students. The main purpose of this course is to develop learners’ basic structured and web programming skills and prepare them for advanced programming and application development. Students will learn writing fundamental computer programs and scripts from scratch and will gradually practice major programming tools and techniques to implement them. More specifically, students will learn not only the design and implementation of the constructs of structured programming (variables, sequence, selection, iteration, functions, parameter passing, and arrays) in the context of a procedural development environment but also implement the same constructs in web programming scripting languages. Students will get familiarity with a number of languages including C++, Python, HTML, CSS, JavaScript, and PHP. The course will enable students to write small pieces of code and implement them in the aforementioned programming languages. Finally, the course will enable learners to develop small client-server applications in scripting languages.

Introduction to Statistics— 48 hrs

This introductory course in statistics provides students with the basic knowledge and skills required for data analysis. The course consists of three main parts. The first part introduces the methods of descriptive statistics that would help learners how to describe or summarize data in a meaningful way. This description can be done in learning two types of statistics i.e., measures of central tendency including mean, median, and mode and spread or dispersion including standard deviation and variance. This section also focuses on the assessment of variables and their relationships which would be discussed through the concepts of correlation and regression. The second part of the course would introduce the fundamental principles and axioms of probability. Topics include computing simple and conditional probabilities, probability distributions, and sampling distributions. This would help students in understanding the third part of the course i.e., inferential statistics. Here the basic techniques of inferential statistics including the estimation of parameters and testing of statistical hypotheses are introduced, which will enable students in making generalization about the population under discussion. Students will gain hands-on experience of doing statistical analysis of real-world problems using various software and tools including Python, R, Excel, and SPSS.

Database Fundamentals— 48 hrs

The main purpose of this course is to introduce essential concepts in databases and help students learn and apply foundational knowledge of SQL (Structured Query Language), with particular emphasis on designing and implementing a relational database in SQL. More specifically, this course will teach students how to create a relational data model and implement it in a current open-source relational database management system, e.g., MySQL. The course presents a thorough introduction to conceptual data modelling, entity-relationship diagram (ERD), and enhanced ERD constructs, primarily supertype/subtype relationships. Students will learn the process of converting a conceptual data model into a relational data model. The course will also introduce a conceptually sound and practically relevant introduction to normalization, emphasizing the importance of the use of functional dependencies and determinants as the basis for normalization. The course also aims at gaining hands-on experience and working knowledge of MySQL database management system. Through a series of hands-on labs and practical assignments, students will practice building and running SQL queries to create tables and insert, update, and delete data in a database. Finally, students will learn how to develop a small application to demonstrate communication between the front-end and back-end of information systems.

Python Programming— 48 hrs

This course is designed to introduce both essential and advanced programming concepts in Python language, with particular emphasis on data manipulation and analysis. The main purpose of this course is to develop not only learners’ programming skills in Python but also introduce them with necessary data science libraries and packages required for data analysis. Students will learn how to install and configure the Python language, Anaconda distribution, Jupyter Notebook, and PyCharm. More importantly, the course will cover different Python data structures including lists, tuples, dictionaries, sets, and data frames, user-defined functions and core statistical functions, object-oriented design and development, importing data in Python, data manipulation and visualization techniques. Students will get familiarity with a number of data science Python libraries and statistical programming packages. The course will help students build hands-on experience with important data science tasks including implementing techniques for preparing data for analysis, representation, and visualization in Python.

R Programming— 48 hrs

This course is designed to introduce essential programming and data analysis concepts in R language. The main purpose of this course is to develop not only learners’ basic programming skills in R but also introduce them with necessary data science libraries and packages required for data analysis. Students will learn how to install and configure R language, RStudio, an integrated development environment (IDE) for R, and other statistical programming software packages. More importantly, the course will teach how to use different data structures in R including data frames, lists, vectors, factors, and matrices, writing R functions and basic statistical functions, using control structures and loops, importing data in R and saving them, using data manipulation techniques, and how to perform data visualization. Students will get familiarity with a number of R packages and statistical programming libraries. The course will help students build hands-on experience with important data science tasks including implementing techniques for preparing data for analysis, representation, and visualization in R.

Foundations of Data Science— 48 hrs

This course is designed to introduce the fundamental concepts in data science and data analytics. More specifically, the main purpose of this course is to give the basic understanding of the anatomy of data, the methodology, tools, and techniques used in data science, and the open- source technologies used for processing big data. The course will begin with the discussion of evolution and promises of data science and data analytics, followed by an overview of practices and processes used by data scientists for the day-to-day operations. Students will gain an understanding of different kinds of data repositories including data warehouses, data marts, data lakes, and data pipelines. The course will cover further topics including, but not limited to, the DIKW (Data, Information, Knowledge, Wisdom) pyramid, the five V’s (volume, velocity, variety, veracity, value) of data and their associated issues, the data lifecycle and its management, and an overview of essential tools, techniques, and principles of data science. Furthermore, students will gain familiarity with a number of open-source big data processing technologies including Apache Hadoop, Apache Hive, and Apache Spark, to name a few. Finally, the legal, ethical, and social aspects and issues associated with data will be discussed.

Introduction to NoSQL Databases— 48 hrs

The main purpose of this course is to introduce essential concepts in non-relational and schemaless databases, with particular emphasis on designing and implementing NoSQL (Not Only SQL) databases. More specifically, this course will cover the four major categories of NoSQL data models including document-oriented, key-value, column-oriented, and graph datastores. The course begins with understanding the fundamental differences between relational and non-relational schemaless databases, followed by exploring the architecture of NoSQL databases. Students will learn the working knowledge of core concepts of NoSQL database and will be equipped with the skills to implement these concepts in NoSQL database management systems. The course will focus on gaining hands-on experience and practical learning of NoSQL databases. Students will get familiarity with various popular NoSQL databases including MongoDB, Cassandra, Redis, and Neo4J. Through a series of hands-on labs, students will practice building and running NoSQL CRUD (Create, Read, Update, Delete) queries. Finally, students will learn how to develop small projects using NoSQL databases and will also demonstrate the skills to evaluate the NoSQL tools and technologies.

Data Wrangling and Cleaning— 48 hrs

This course is designed to introduce the fundamental concepts in data wrangling, data cleaning, and data exploration, with particular emphasis on preparing raw data for analytics. Students will gain an understanding of the processes and steps involved in identifying, gathering, importing, and validating data from disparate data sources. More importantly, the course will provide hands-on working knowledge of the techniques, tools, software packages and libraries used for organizing, importing, wrangling, and cleaning data, along with their features, strengths, limitations, and applications. An introduction of different data types, file formats, sources of data, structured, semi-structured, and unstructured data, along with their strengths and limitations will be provided. Students will demonstrate the skills to perform the ETL (Extract, Transform, Load) process to extract, transform, and load data into repositories. Furthermore, the course will equip students with necessary skills to integrate data from disparate sources, e.g., databases, websites, sensors, flat files, and social media, into a unified view for understanding and further processing. Students will gain hands-on experience and skills using different software and tools such as Excel, OpenRefine, Google DataPrep, SQL, R, and Python, to perform data cleaning and making the data ready for analysis. Finally, students will learn how to check data integrity and biases in data and verify the results of cleaning data.

Data Mining— 48 hrs

This course is intended to introduce students to the fundamental concepts, methods, principles, tools, and implementation techniques of data mining, with particular emphasis on discovering patterns, trends, and hidden relationships within data. The course consists of three main parts. The first part focuses on pattern discovery and introduces simple data mining methods including association rules, market-basket analysis, frequent itemset analysis, Apriori algorithm, frequent pattern growth method, and decision tree induction algorithm. The second part gives an overview of advance pattern mining in multilevel, multidimensional space, constraint-based frequent pattern mining, and mining high-dimensional data and colossal patterns. The final part of the course focuses on web mining algorithms, social web mining to extract patterns and trends from social media data, and an overview of text mining algorithms. Topics include PageRank and web-link analysis, HITS (Hyperlink-Induced Topic Search) algorithm, mining path-traversal patterns, recommendation systems, text mining, and latent semantic analysis. Through a series of hands-on labs sessions and practical assignments, students will gain hands-on experience and skills to implement data mining, web mining, and text mining algorithms using different software and tools such as Python, R, Weka, and Jupyter notebook. Finally, in small group projects, students will apply different data mining methods to a real-world data science problem to extract useful and interesting patterns and trends from the data.

Fundamentals of ERP & Datawarehouse— 48 hrs

The main purpose of this course is twofold: i) to introduce essential concepts of enterprise resource planning (ERP) systems; and ii) to provide students the working knowledge of how to design a data warehouse and load in and query data from it. Hence, the course has two parts. In the first part, students will get an understanding of what ERP systems are, their role in automating and unifying business processes, and how ERP systems impact on organizations in terms of information management. Among the topics covered include installing and configuring SAP ERP/ECC and SAP S/4HANA, evolution of ERP architecture, features and working of ERP system, ERP selection and acquisition process, implementation lifecycle and deployment models of ERP, and evaluating implementation strategies of ERP systems. The second part of the course will introduce fundamental concepts, tools, and technologies of data warehouse. The course will cover topics including evolution of data warehouse and business intelligence (BI), processes, applications, benefits, and challenges of data warehouse, data cubes and data marts, OLAP and OLTP operations, development lifecycle, architecture, and major components of data warehouse, steps of ETL process, data integration, designing data warehouse, and techniques, tools, and implantation technologies of data warehouse. Students will learn how to build a data warehouse using MS SQL Server and Oracle Warehouse Builder. Through a series of hands-on labs and practical assignments, students will practice building and running queries in a data warehouse.

Introduction to Machine Learning— 48 hrs

This course is designed from the data science perspective to introduce fundamental concepts in machine learning, with a particular emphasis on learning and applying machine learning models to analyze data and make predictions. Students will gain the working knowledge of different machine learning models including supervised and unsupervised learning that learn from data. The course will cover topics including classification, support vector machines (SVM), K-nearest neighbour, random forest, linear regression, logistical regression, neural networks, clustering, Naïve Bayes, and principal component analysis (PCA). Students will learn how to use these models and solve complex real-world problems. Through a series of laboratory sessions, and practical exercises, students will gain hands-on experience of implementing these models on sample datasets using Python. The course will equip students with practical skills of various machine learning libraries and packages including PyTorch, Scikit- learn, TensorFlow, Keras, Theano, Pandas, SciPy, NumPy, and Spark MLlib. Finally, in small group projects, students will apply machine learning techniques using the aforementioned libraries to create learning models for a real-world data science problem and derive meaningful insights from the data.

The main purpose of this course is to introduce the big data processing frameworks, with particular emphasis on Apache Hadoop and Apache Spark. Students will learn how to install, configure, and administer Apache Hadoop, MapReduce, Hive, and Spark. The course will cover topics that include, but not limited to, the characteristics, architectures, components, applications, strengths, and limitations of Hadoop, MapReduce, Hive, and Spark, architecture and features of Hadoop Distributed File System (HDFS), Resilient Distributed Datasets (RDDs), Hadoop and Spark programming basics, DataFrames and datasets operations, Spark SQL, fundamentals of parallel programming with Hadoop and Spark, and development and runtime environment options. Through a series of laboratory sessions and practical assignments, students will gain hands-on experience of basic programming, including parallel programming with Apache Hadoop and Spark. Finally, the course will discuss the Hadoop and Spark development and runtime environment options and will enable students how they can track their work using the Spark Applications UI (user interface).

Data Visualization— 48 hrs

This course is designed to introduce the fundamental concepts in data visualization to effectively communicate and present data findings. More specifically, the main purpose of this course is to equip students with the skills to present the results of data analytics in an interpretable, efficient, and effective manner. The course will begin with the discussion of principles, practices, and techniques involved with effective data visualizations. Through a series of hands-on labs’ sessions and worked examples, students will learn to demonstrate creating graphs piece by piece, starting with summaries of single variables and proceeding to more complex types of charts. The course will cover further topics including, but not limited to, creating graphs for continuous and categorical variables, layering information on graphics, creating multiple graphs, creating maps, grouping, summarizing, and transforming data for plotting, working with data findings of statistical models, refining graphs, and creating and publishing dashboard. Furthermore, students will gain hands-on experience with a number of software and tools including Python, R, Excel, Cognos, and Tableau to communicate their data findings effectively. Finally, students will gain familiarity with the power of storytelling with data that will enable them to create an engaging, informative, and compelling visualizations of their data and findings in a stimulating manner.

The main purpose of this course is to help students develop and enhance effective communication skills in English for successful interaction. More specifically, the course will focus on particular domains of communication in English including the role of listening, verbal and non-verbal communication, holding a formal conversation at interviews and meetings, writing professional emails, making effective presentations, and networking online. The course begins with the introduction of elements of communication and will move on to discuss communication style, barriers to communication, and the process and core principles of effective communication. Students will learn how to improve their listening skills, understand non-verbal cues in communication, write persuasive messages, and select the right medium of communication. The course will focus on building students’ speaking and writing skills and will enable them to clearly articulate their thoughts and ideas in writings and speaking including in-person, online, and telephonic conversations. Students will also learn how to write effective business emails to fulfil their professional needs. The course will further cover interviewing and presentation techniques to help students prepare for better interviews and design and deliver effective presentations. Finally, the course will cover online networking and will equip students with the necessary skills required for making meaningful online connections with other professionals.

This course is designed to help students plan, develop, and pursue effective career and employment strategies. More specifically, the course will focus on particular domains and stages of career development including devising career strategies for before, during, and after the job interview. The course is divided into three major parts. The first part of the course will focus on the before the interview stage. Students will learn about the techniques of choosing an appropriate career that suit them their interests, knowledge, and skills and also comply with the type of work they choose. Students will further learn how to create an effective professional resume from scratch or update the current one. The course will also cover creating a profile on LinkedIn and Indeed employment website, the tips and techniques of tapping the hidden job market, and assessment of online presence. Students will also learn how to write and adapt a professional cover letter. The second part of the course will focus on preparing students for the job interviews. Students will participate in video-recorded mock interviews that will enable them to assess their own interests, skills, competence, performance, personality, and values. The final part of the course will cover the after the interview stage and will discuss the follow-up, negotiations, weekly manager meetings, and the impact of contemporary issues on career choices and self-management. Finally, the course will discuss the BC Human Rights Code, BC Employment Standards Act, federal Employment Equity Act, and employees’ rights.