Data Engineering and Analytics with Co-op

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.