The Data Engineering and Analytics with Co-op program was meticulously crafted to offer a comprehensive curriculum that seamlessly blends theoretical foundations with practical applications, ensuring students are well-equipped for dynamic careers in data engineering and analytics.
The program covers a broad spectrum of topics, including relational database systems, data structures, statistical analysis and machine learning, providing students with a deep understanding of foundational concepts and techniques. Additionally, students delve into specialized areas such as business analysis, system design and advanced statistical analysis, enhancing their analytical and problem-solving skills.
Moreover, the program exposes students to cutting-edge technologies and methodologies essential for modern data professionals. Students learn about data warehousing, enabling them to design and manage large-scale data repositories effectively. They also acquire skills in visual analysis for business intelligence, enabling them to create compelling data visualizations that drive informed decision-making. Furthermore, students gain practical experience with Microsoft SQL Server tools, including Integration Services (SSIS), Analysis Services (SSAS) and Reporting Services (SSRS), preparing them for diverse roles in data integration, analysis and reporting.
The program also covers key concepts in big data, cloud computing and data mining, providing students with the knowledge and skills needed to work with large and complex datasets efficiently. Additionally, students explore data security and privacy issues, ensuring they understand the importance of safeguarding data and comply with relevant regulations and best practices.
The Data Engineering and Analytics with Co-op program prepares students for careers in data engineering and analytics through a comprehensive curriculum covering key topics such as relational database systems, statistical analysis and machine learning. Students gain practical experience with technologies like data warehousing and visual analysis, ensuring they are well-equipped for diverse roles in the industry.
The co-op work experience component constitutes half of the program's entirety, totaling 900 hours. This segment offers you the chance to put into practice the skills you've recently acquired in a real-world setting within the industry, all while benefiting from a guaranteed paid work opportunity.
The Data Engineering and Analytics with Co-op program is reviewed and approved by the registrar of the Private Training Institutions Branch (PTIB) of the Ministry of Advanced Education, Skills & Training.
At CCTB, our mission is to equip students with top-tier training to distinguish themselves in today’s competitive job market. Our curriculum is meticulously designed to align with your career aspirations and is delivered using cutting-edge industry tools. Experience a dynamic learning environment that mirrors real-world business practices, ensuring your readiness for the professional realm.
Moreover, CCTB is dedicated to offering comprehensive career orientation, guidance and continual employment assistance. Throughout your educational journey, you'll gain practical skills tailored to industry demands and receive personalized support to secure employment in your chosen field. Our commitment extends beyond graduation, as we aim to provide ongoing support to facilitate your successful integration or re-entry into the workforce.
Studying in the Data Engineering and Analytics with Co-op program will provide you with the skills for many different roles. Some roles you could consider after you finish the program include:
NOCs: 21223
Standard
| Academic | 45 Weeks | 4 Terms |
| Co-op Placement | 42 Weeks | 3 Terms |
| Internship Break | 14 Weeks | 1 Term |
| Term Breaks | 21 Weeks | |
| *Reading Breaks | 11 Weeks | |
| Graduation | 133 Weeks | 8 Terms |
*Reading breaks are short academic breaks (1-2 weeks) built in each academic term.
In this course, students will immerse themselves in the organizational structures of software development, acquiring knowledge of the various business units that form the backbone of enterprise-level software development projects and the roles they play, both technical and non-technical. This deep dive will highlight the criticality of collaboration and coordination among these units and roles, showcasing how their integration drives project objectives to fruition.
Additionally, the course will familiarize students with a spectrum of software development project models, shedding light on how projects are structured and initiated within each distinct model. Students will also delve into the crucial process of interpreting and analyzing business requirements, understanding how these analyses shape strategic objectives for a project.
Furthermore, students will attain a thorough understanding of software testing fundamentals, encompassing testing methodologies, strategies and techniques. This knowledge equips students to proficiently plan, execute and assess software tests, ensuring the quality and reliability of software products. Moreover, students will develop skills in issue tracking and reporting and will gain proficiency in test case and defect management tools.
This course provides a comprehensive introduction to the Linux operating system, covering a wide array of topics essential for understanding its functionality and administration. Students will explore the underlying operating system architecture, gaining insights into command line interface navigation, device and filesystem management, networking fundamentals, common administration practices and bash shell scripting.
Additionally, students will delve into common server administration tasks, learning how to deploy, configure and maintain various enterprise services. These services include, but are not limited to, SSH for secure remote access, web servers such as Apache and NGINX, SQL servers like MySQL, application servers and version control systems.
This hands-on database course focuses on practical exercises to teach students the fundamentals of modeling and designing relational database schemas. Using enterprise data modeling and mapping tools, students will learn key concepts such as technical requirements analysis, relationship identification, entity mapping, data normalization and validation.
Moreover, students will gain proficiency in interacting with relational database systems through the SQL (Structured Query Language) programming language. By the end of the course, students will be equipped to perform intermediate-level database queries with confidence.
This course provides students with the foundational skills and knowledge necessary for designing, implementing and analyzing data structures. Students will learn the fundamental principles and techniques for creating efficient and effective data structures to organize and manage data. Topics covered include the basics of data structure design, such as arrays, linked lists, stacks, queues, trees and graphs. By the end of the course, students will have a foundational understanding of data structures and be able to apply this knowledge to solve real-world problems in fields such as computer science, data analysis and software development.
This course offers a comprehensive introduction to business analysis, equipping students with the foundational skills needed to analyze business requirements and integrate new features and functions into systems efficiently. Students will explore the fundamentals of Agile Methodologies, including specific implementations such as the SCRUM framework, to understand iterative and incremental development processes. Additionally, students will learn the basics of project management and object-oriented analysis and design using Unified Modeling Language (UML), along with graphic representation tools. By the end of the course, students will have a strong understanding of business analysis principles and practices, preparing them to apply these skills in various professional settings.
This course provides students with a thorough understanding of the core concepts and methodologies of statistics and probability. Students will learn to confidently manipulate various distributions of discrete and continuous random variables, calculate mean, variance and standard deviation for different distributions and draw inferences and conclusions based on collected data. Additionally, students will become familiar with confidence intervals for the population mean and proportion, as well as hypothesis testing. By the end of the course, students will have the skills and knowledge to apply statistical and probabilistic reasoning effectively in practical scenarios.
This course focuses on modeling and analyzing the relationship between variables, specifically two quantitative variables involving a single response variable and a single exploratory variable. Students will learn the basic principles of regression analysis and how to establish statistical inferences from a sample population using a simple linear regression model. Additionally, students will gain practical experience in applying Microsoft Excel for regression analysis, enhancing their ability to apply these concepts in real-world scenarios.
This course provides students with the skills to estimate critical parameters of the multiple regression model and interpret inferences about these parameters. Students will learn how to predict the value of the response (dependent) variable based on the values of the exploratory (independent) variables using the statistical model for multiple linear regression. Additionally, students will explore non-parametric statistical analysis, including the Wilcoxon test, as well as other predictive models.
The course also covers the use of the Python programming language, widely recognized for its applications in data mining, regression analysis, statistical tests, time-series analysis and other data analysis techniques. Through practical exercises and projects, students will gain proficiency in using Python for statistical analysis, preparing them for roles that require strong analytical and programming skills in various industries.
This course provides a comprehensive introduction to artificial intelligence (AI) and machine learning (ML), covering principles, methodologies and practical applications. Students will gain a deep understanding of AI, ML, deep learning and their applications in various fields. They will also learn the differences between supervised, unsupervised and reinforcement learning and how to practically apply various models and algorithms to manipulate data.
Hands-on exercises will enable students to apply supervised learning algorithms such as classification and regression trees, k-neighbors algorithm, Naïve Bayes algorithm, support vector machines (SVM), random forest and classification and regression ensembles. Additionally, students will learn to apply unsupervised learning algorithms such as k-means clustering, hierarchical clustering algorithms, Gaussian mixture models and hidden Markov models. By the end of the course, students will have a solid understanding of AI and ML concepts and be able to apply them effectively in real-world scenarios.
In this course, students will learn the design of data warehouses and the development of data integration workflows from diverse sources. They will gain hands-on experience in Extract, Transform, Load (ETL) principles and the complete data warehousing life cycle. Additionally, students will delve into dimensional modeling concepts, including star, snowflake and fact constellation schemas, along with online analytical processing (OLAP) servers. By the end of the program, students will be proficient in these technologies, ready to tackle real-world data engineering challenges.
In the data visualization course, students will explore the art and science of presenting data visually. They will learn to harness the power of Tableau, a leading software tool for data visualization, to create impactful visual representations. Through hands-on practice, students will master the fundamentals of data visualization, including customizing data views, creating dashboards and performing data operations like extraction, joining and blending.
They will also learn to manipulate and analyze data using Tableau, employing techniques such as sorting, filtering and graphical representation with charts like bar charts, pie charts, scatter plots, box plots, tree maps, histograms and more. By the end of the course, students will be adept at using Tableau as a tool to convey complex data insights clearly and effectively.
In this course, students will delve into the world of SQL Server services, including SQL Server Integration Services (SSIS), SQL Server Analysis Services (SSAS) and SQL Server Reporting Services (SSRS). They will gain hands-on experience in working with these services, learning how to create and maintain Analysis Services databases, extract data from various sources and build multidimensional databases. Additionally, students will tackle projects involving data migration, honing their skills in transferring data between systems even when there are changes in storage, database structure or applications. By the end of the course, students will be well-versed in leveraging SQL Server services to manage and manipulate data effectively.
In this course, students will explore key concepts in big data, data mining and cloud computing. They will dive into the fundamentals of data mining, learning various algorithms and techniques to perform data mining tasks effectively. Additionally, students will delve into cloud computing, gaining insights into its technologies, architectures and infrastructures. They will also explore different cloud computing models, services, management approaches, data storage solutions and applications. Furthermore, students will examine NoSQL databases, understanding their implementation and how they differ from traditional relational databases. By the end of the course, students will have a comprehensive understanding of these cutting-edge technologies and their applications in real-world scenarios.
In this course, students will focus on the security and privacy challenges associated with big data. They will explore topics such as internet security protocols, secure sockets layer (SSL), email security, website security and firewall security issues. Students will learn to identify and prevent data breaches, as well as how to safeguard personal data. Additionally, they will examine the unique security and privacy challenges that arise in the context of big data, particularly concerning cloud storage. By the end of the course, students will be equipped with the knowledge and skills needed to address security and privacy issues effectively in a big data environment.
This comprehensive course is designed to equip students with the necessary skills and tools to secure employment upon completing the program. Students will receive thorough training in various aspects of career readiness, including resume-crafting, portfolio development, job search strategies, interview preparation and salary negotiations. Additionally, the course offers comprehensive career development training, ensuring that students are well-prepared to enter the job market confidently and successfully.
Financial assistance may be available to eligible students under the StudentAid BC program. For more information please go directly to https://studentaidbc.ca/sabc-home-page
Language proficiency requirements are admission requirements and may not be waived by either the institution or the student.
Students must be in possession of one of the English Language Equivalencies as described in the CCTB Admissions Policy:
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We acknowledge that the territories on which CCTB and its campus are situated are the traditional, ancestral and unceded territories of the xʷməθkʷəy̓əm (Musqueam), Sḵwx̱wú7mesh (Squamish) and Sel̓íl̓witulh (Tsleil-Waututh) Nations. We thank them for having cared for this land since time immemorial, honour their graciousness to the students who seek knowledge here, and iterate our dedication to valuing the ongoing contributions of Indigenous peoples and communities.