Career Pathways Catalog

This course introduces the fundamentals of software engineering through front-end web development with HTML, CSS, and JavaScript. You’ll learn how to build user interfaces, write clean and maintainable code, and solve logic-driven programming problems. Along the way, you’ll explore core programming concepts, common data structures, and essential developer tools such as GitHub. The course culminates in a project in which you’ll apply these skills to solve a real-world business problem, laying the groundwork for more advanced software engineering work.

This course deepens your front-end development skills by focusing on JavaScript as a core programming language for the web. You’ll learn modern JavaScript syntax and patterns, including asynchronous programming, while strengthening your understanding of data structures, functions, and testing. Through hands-on exercises and a command-line application project, you’ll practice writing interactive, user-focused programs and implementing clean logic. By the end of the course, you’ll be prepared to tackle increasingly complex front-end development challenges with confidence.

This course immerses you in front-end development by focusing on building interactive web pages with HTML, CSS, and JavaScript. You’ll work with core concepts such as asynchronous programming, DOM manipulation, and client–server communication to create responsive, user-focused interfaces. Throughout the course, you’ll integrate functionality and design, culminating in a project that brings together interactivity, structure, and visual presentation. By the end, you’ll be prepared to solve real-world business problems using industry-standard front-end practices.

In this course, you’ll be introduced to React, one of the most widely used libraries for building modern front-end applications. You’ll learn how React works alongside JavaScript to create reusable, composable UI components and manage application structure. The course focuses on understanding component-based architecture, working with props and state, and translating visual designs into functional interfaces. You’ll practice breaking down user interfaces into logical components and implementing them using industry-standard patterns. The course culminates in a project where you’ll build a set of React components based on a provided mock-up, establishing a strong foundation for building larger applications.

This course builds on your React foundation by focusing on assembling components into fully interactive front-end applications. You’ll work with more complex component hierarchies, manage shared state, and coordinate user
interactions across an application. The course emphasizes integrating logic, data flow, and UI behavior to create cohesive user experiences. You’ll also refine your approach to application structure and maintainability as projects grow in complexity. The course culminates in a comprehensive project where you’ll build a complete interactive application in React based on a provided mock-up, demonstrating your ability to move from components to a production-style front end.

This introductory Python course equips you with essential software engineering skills using one of the most versatile programming languages in the industry. You’ll learn fundamental programming concepts, including scripting, operators, loops, functions, core data structures, and working with libraries. The course culminates in a project where you’ll develop a Python script to analyze data, reinforcing both problem-solving and implementation skills. By the end of the course, you’ll be prepared to write efficient, readable code and tackle real-world software engineering challenges.

This course introduces object-oriented programming concepts in Python with a focus on backend software design. You’ll learn how to model real-world entities using classes and objects, and how to represent complex relationships such as one-to-many and many-to-many structures commonly found in database-backed applications. The course emphasizes writing organized, maintainable code that supports growing application complexity. It culminates in a hands-on project where you’ll build a command-line application that demonstrates object modeling and relationship management in practice.

In this course, you’ll learn how to build dynamic RESTful APIs using Python and a modern web framework. You’ll focus on core backend concepts such as request handling, data validation, serialization, and client–server communication. The course emphasizes designing APIs that are reliable, maintainable, and easy to consume by front-end applications. In the culminating project, you’ll create a REST API and connect it to a basic React client, giving you hands-on experience building and integrating full-stack systems.

In this course, you’ll learn how to work with relational databases using SQL and Flask-SQLAlchemy to build robust, database-backed APIs. You’ll practice writing queries to create, read, update, and manage data while modeling complex relationships between entities. The course emphasizes proper data modeling through normalization, constraints, and validations to ensure data integrity and security. It culminates in a project where you’ll build an API that seamlessly integrates with a relational database and handles real-world data structures.

This course focuses on building robust, secure backend systems
using Python and Flask. You’ll deepen your understanding of
client–server communication while implementing authentication
and authorization workflows using JWTs. The course emphasizes
designing APIs that support real users, protected resources, and
reliable data flow across a full-stack application. In the
culminating project, you’ll build a production-style API featuring
user authentication, authorization, and secure integration with a
front-end client, preparing you for real-world backend
development.

The Software Engineering Capstone is an immersive, project-driven experience where you bring together everything you’ve learned across the program to design and build real-world software systems. You’ll develop projects, each framed around a clearly defined business or user problem. You’ll be responsible for scoping requirements, designing system architecture, and implementing full-stack solutions using a React front end, a Flask backend, and a relational database. Throughout the capstone, you’ll focus on writing clean, maintainable code and making thoughtful technical decisions that reflect real engineering tradeoffs. By the end of the course, you’ll have a polished portfolio that demonstrates your ability to take complex ideas from concept to working, deployable applications.

Embark on an immersive journey into the world of data analysis and visualization using Python. Throughout this course, you’ll learn essential statistical measures, explore different types of data, and master data analysis techniques using the pandas library. You’ll delve into data visualization with Python libraries such as Seaborn and Matplotlib, gaining insights into qualitative, quantitative, and multivariate data. The course culminates in a project, wherein you will apply your knowledge to perform a full exploratory data analysis process, demonstrate proficiency in descriptive data analysis and visualization using pandas. You’ll emerge from this course equipped with the skills to gather insights from data, perform data analysis, and communicate your findings through descriptive statistics.

This course will equip you with skills in structured query language (SQL), data engineering, database administration, and data analysis. Learn the essentials of mathematics, probability, and statistics for data science, as well as how to perform more advanced data analysis and cleaning with Python. You’ll start by getting familiar with SQL, learning how to connect to databases, and performing basic queries. As you progress, you’ll delve into topics such as filtering, ordering, and grouping data, as well as understanding table relations and implementing joins and subqueries. In the culminating project, you will demonstrate proficiency in SQL by applying queries on a database within a Python environment and reflecting on the database design and outputs.

This course dives into cloud computing’s cost-effective, scalable ecosystem for distributed data processing. Master technical components like PySpark to bridge Python, SQL, and Spark, to manipulate structured and semi-structured data. Leverage Numpy, Pandas, and PySpark libraries to pull in “big data”. You will craft data visualizations using the Matplotlib and Seaborn libraries. Finally, explore the cutting-edge of data analysis with generative AI and advanced dashboards, culminating in a project that brings big data to life through interactive visualizations.

In this course, you’ll perform statistical inference using Python, building the foundational theory and practical skills needed to analyze and interpret data. You’ll study core concepts such as probability distributions, confidence intervals, and hypothesis testing, then apply them to single proportions, means, and categorical data. From there, you’ll extend these techniques to comparisons across two or more groups and to multivariate datasets. The course culminates in a final project where you’ll select and apply appropriate inferential methods to draw meaningful conclusions from real-world data.

This course equips you with the skills to tackle real-world datasets using regression techniques. You’ll begin with linear regression, learning how to assess model assumptions and apply diagnostics to ensure validity. From there, you’ll move into multiple linear regression, evaluating and interpreting its predictive power across more complex datasets. You’ll explore advanced methods such as transformations, interaction terms, and model selection, while developing an intuition for the bias–variance tradeoff. The course also introduces regularization techniques, including Lasso and Ridge regression. In the culminating project, you’ll bring these concepts together by building, evaluating, and interpreting a robust multiple linear regression model.

In this course, you’ll learn the fundamentals of artificial intelligence and machine learning through a hands-on, model-driven approach. You’ll explore core concepts such as statistical learning theory and supervised learning, then dive into widely used models including logistic regression, decision trees, and support vector machines. Along the way, you’ll learn how to evaluate and compare models using performance metrics like ROC AUC. In the culminating project, you’ll apply the full data science pipeline to select, train, and deploy the most effective model for a real-world task.

In this course, you’ll apply machine learning concepts in practice using Scikit-Learn, working with both supervised and unsupervised models. You’ll explore distance metrics and their role in algorithms like k-Nearest Neighbors for classification, then dive into recommender systems using Singular Value Decomposition (SVD) across supervised and unsupervised use cases. You’ll also learn clustering techniques such as k-means and apply dimensionality reduction with Principal Component Analysis (PCA). The course culminates in a hands-on project where you’ll build and evaluate both a supervised (k-Nearest Neighbors) and an unsupervised (k-means) model, demonstrating your ability to solve real classification and clustering problems.

This course equips you with the skills to build advanced models across some of the most important domains in modern data science. You’ll explore natural language processing (NLP), learning techniques such as text vectorization and classification to extract meaning from unstructured text. You’ll then move into time series analysis, where you’ll manage, visualize, and model trends and temporal patterns in data. Finally, you’ll dive into the foundations of neural networks, gaining both theoretical understanding and hands-on experience implementing models with Keras. The course culminates in a comprehensive project where you’ll build three distinct models: a language model, a time series model, and a foundational neural network.

In this course, you’ll build on your neural network foundation to design and train more advanced models. You’ll learn normalization and regularization techniques to improve model stability and performance, then dive into Convolutional Neural Networks (CNNs) for image classification tasks. From there, you’ll explore Recurrent Neural Networks (RNNs) to model sequences and time-dependent data, before moving into modern transformer architectures, including BERT. The course culminates in a hands-on project where you’ll design and implement an advanced neural network application that integrates these techniques.

This course equips you with the skills to deploy, optimize, and maintain large language models in real-world environments. You’ll explore the open-source MLOps stack, learning how to manage the full machine learning lifecycle, from deployment and versioning to monitoring and iteration. The course emphasizes data-centric approaches to improving LLM performance through thoughtful data curation and preprocessing. You’ll also develop practical expertise in fine-tuning pre-trained models and applying prompt engineering to adapt LLMs for specific use cases. By the end of the course, you’ll be prepared to integrate and operate advanced AI systems in dynamic, production-oriented settings.

The Capstone is an immersive, project-driven experience where you bring together everything you’ve learned across the program to solve real-world problems. You’ll design and build projects framed around a clearly defined business or organizational challenge. You’ll be responsible for scoping the problem, selecting appropriate data, choosing modeling approaches, and communicating results in a way that aligns with real stakeholder needs. Across these projects, you’ll apply both supervised and unsupervised methods, including modern large language model workflows, to demonstrate technical depth and practical judgment. By the end of the Capstone, you’ll have a polished portfolio that showcases your ability to take ambiguous problems from concept to deployed solution.

This introductory cybersecurity course builds a strong technical foundation by covering computer hardware, basic networking concepts, and virtualization. You’ll explore core system components such as CPUs, memory, and storage, while gaining hands-on experience working in the Linux command line. The course emphasizes understanding how operating systems function and how built-in security features help protect systems. In the final assessment, you’ll apply these skills by locating hidden flags on both Windows and Linux virtual machines, reinforcing the fundamentals needed to secure IT systems against cyber threats.

This course focuses on securing and troubleshooting computer systems and networks across both Linux and Windows environments. You’ll build a working understanding of computer networking concepts, operating system behavior, and common connectivity issues. Through hands-on analysis, you’ll learn how systems communicate, where failures occur, and how misconfigurations can impact security. The final assessment challenges you to analyze a network diagram and virtual machines to identify and resolve connectivity and system-level issues.

This course focuses on strengthening and securing systems against real-world threats across on-premise and cloud environments. You’ll deepen your Linux skills with advanced commands and scripting techniques, explore Active Directory security, and examine common attack vectors along with effective mitigation strategies. The course also introduces cloud computing fundamentals, data protection methods, encryption, and privacy considerations. Throughout, you’ll apply system hardening principles and learn how to monitor system health and automate security tasks to maintain resilient, secure systems.

This course introduces the fundamentals of cyber threat intelligence (CTI) and its role in modern cybersecurity operations. You’ll explore how threat intelligence is collected, analyzed, and applied to real-world security incidents, including the use of frameworks such as MITRE ATT&CK. The course emphasizes turning raw security data into actionable insights, while also examining how Governance, Risk, and Compliance (GRC) considerations shape effective cybersecurity strategy. Through hands-on analysis, you’ll learn how organizations detect threats, assess risk, and respond in a structured, informed way.

This course focuses on detecting, investigating, and responding to security incidents using modern Security Information and Event Management (SIEM) tools. You’ll learn how to collect and analyze logs, correlate events, and apply threat intelligence to identify suspicious activity. The course also introduces threat hunting techniques and vulnerability management, emphasizing proactive approaches to uncovering hidden threats. In the culminating project, you’ll use SIEM tools and threat hunting methods to investigate and resolve a realistic cybersecurity incident.

This course focuses on designing and evaluating secure systems through the lens of security architecture and industry frameworks. You’ll examine the consequences of insecure design while learning how to assess system vulnerabilities across networks, applications, and cloud environments. Topics include secure network architecture, zero-trust principles, network segmentation, and cloud security considerations. The course culminates in a project where you’ll analyze an IT architecture to identify security weaknesses and recommend mitigations based on sound architectural principles.

This course focuses on responding to cybersecurity incidents using industry-standard incident response and digital forensics techniques. You’ll learn how to recognize and analyze malware, investigate compromised systems, and perform memory forensics across both Windows and Linux environments. The course also introduces Python-based approaches to securing and testing APIs, reinforcing how software systems intersect with security operations. In the culminating project, you’ll apply these skills to investigate, contain, and respond to a realistic cybersecurity incident using best practices from professional incident response workflows.

This course introduces the principles and practices of ethical hacking and penetration testing. You’ll learn how attackers identify and exploit vulnerabilities through techniques such as reconnaissance, scanning, enumeration, social engineering, and post-exploitation. The course emphasizes responsible testing methodologies and clear documentation of findings. You’ll gain hands-on experience with industry-standard tools, including Nmap, Burp Suite, and Metasploit, and apply your skills in a realistic penetration testing project that simulates real-world attack scenarios.

This course expands penetration testing into real-world application and platform security contexts. You’ll focus on web application security, examining common vulnerabilities, secure coding practices, and penetration testing techniques using industry-standard tools. The course also covers Active Directory attack methods and mitigations, credential dumping, and Operational Technology security concepts, while introducing emerging trends such as AI-driven attacks and quantum cryptography considerations. You’ll explore mobile device security, including vulnerability identification, configuration analysis, and script automation. The course culminates in a web application penetration testing project that brings these techniques together.

The Capstone is an immersive, project-driven experience where you integrate the technical, analytical, and strategic skills developed throughout the program. Over this course, you’ll complete projects, each framed around a real-world business or organizational security challenge. You’ll analyze network architectures, investigate risk and vulnerabilities, and design response strategies that reflect how security teams operate in practice. Throughout the capstone, you’ll focus on translating technical findings into clear processes, documentation, and reports suitable for both technical and non-technical stakeholders. By the end of the course, you’ll have a portfolio that demonstrates your ability to assess, respond to, and communicate cybersecurity risk in professional environments.