Course Overview: 

14 Modules

This course introduces students to the core concepts, tools, and career pathways in information technology through a hands-on, lab-driven learning experience. The curriculum is organized into 18 modules that span four foundational computer science verticals—programming, data analytics, artificial intelligence (AI), and cybersecurity—so learners can explore how modern digital systems are built, analyzed, automated, and secured. Students will gain practical experience using common workflows (coding, data preparation, model experimentation, and security fundamentals) while developing professional skills such as documentation, troubleshooting, and ethical decision-making. 

Course Objectives: 

  • Explain core IT concepts including hardware, operating systems, networks, applications, and cloud services 
  • Write and debug basic programs using modern programming fundamentals (variables, conditionals, loops, functions) 
  • Collect, clean, and analyze data to produce basic visualizations and insights 
  • Describe AI concepts and experiment with simple machine learning workflows while understanding limitations and responsible use 
  • Apply cybersecurity fundamentals to identify common threats, improve account/device safety, and follow basic incident response steps 
  • Create clear documentation (README-style notes, tickets, and short reports) and communicate technical findings to non-technical audiences 

Module Breakdown: 

Modules 1–4: Vertical 1 — Programming Foundations (with EHR Context) 

  • Topics: What a computer program is; programming languages and the history of programming; programming for electronic health records (EHR) workflows; algorithms and problem solving; JavaScript fundamentals (variables, data types, syntax, strings, numbers, spread/rest). 
  • Activities: 
    • EHR scenario mapping: identify data fields, workflows, and validation rules 
    • Algorithm practice: translate a workflow into steps that create efficiency and automation 
    • Hands-on coding labs in JavaScript (variables, types, strings, numbers) 
    • Mini-lab: use spread/rest to simplify data handling in small examples 
  • Assessment: Lab check-offs; short quiz; short EHR workflow-to-algorithm write-up. 

Module 1: Programming for Electronic Health Records (EHR) + What is Programming? (Hour 1) 

  • What is a computer program? Inputs, processing, outputs, and stored data 
  • Programming languages: what they are and why there are many (compiled vs. interpreted—high level) 
  • History of computer programming (milestones overview) + programming slides/discussion 
  • EHR context: how software supports electronic health records (data fields, validation, privacy expectations—high level) 

Module 2: Introduction to Programming — Algorithms + Problem Solving (Hour 2) 

  • What is an algorithm? A set of steps to produce efficiency and automation (not just a recipe) 
  • Problem solving with algorithms: inputs/outputs, constraints, and edge cases 
  • Where algorithms show up: automation, search, recommendations, big data, and data mining (high level) 
  • Activity: write/present an algorithm for an EHR-related workflow (e.g., patient intake validation) 

Module 3: JavaScript Basics — Variables, Data Types, and Syntax (Hour 3) 

  • Introduction to variables and data types; basic JavaScript syntax 
  • Working with strings and numbers (concatenation, interpolation, basic math) 
  • Spread and rest operations: simplifying arrays/objects (conceptual + examples) 
  • Lab: create and manipulate simple variables to model small EHR-like data (names, dates, IDs—non-sensitive mock data) 

Module 4: Introduction to Data Analytics + AI Data Applications (Hour 4) 

  • Introduction to data: what it is, why it matters, and common types used in organizations 
  • Introduction to AI: what AI is (and isn’t) and how it relates to data analytics 
  • AI data applications: examples of how data powers predictions, automation, and decision support 
  • Activity: identify a data-driven question and what data would be needed to answer it 

Modules 5–6: Vertical 2 — Data Analytics (Sections 2–3) 

  • Topics: Collecting and organizing data; analyzing and visualizing data; introductory machine learning concepts; machine learning projects. 
  • Activities: Collect and organize a dataset; perform simple analysis; build charts; discuss how ML projects use data pipelines. 
  • Assessment: Lab check-offs; short quiz; mini analytics deliverable. 

Module 5: Data Analytics — Section 2 (Collecting and Organizing Data) (Hour 5) 

  • Collecting data: sources, basic formats, and simple quality checks 
  • Organizing data: rows/columns, tidy data concepts, naming conventions 
  • Creating a simple data dictionary (field name, type, description) 
  • Lab: organize a small dataset for analysis 
  • Where data comes from: apps, sensors, logs, forms, and APIs (conceptual) 
  • Data quality dimensions: completeness, accuracy, timeliness 
  • Lab: inspect a dataset and identify quality issues 

Module 6: Data Analytics — Section 3 (Analyzing and Visualizing Data) + Intro to Machine Learning (Hour 6) 

  • Analyzing data: summaries, grouping, basic comparisons (conceptual + practice) 
  • Visualizing data: selecting charts and telling the story with visuals 
  • Introduction to machine learning: how models learn patterns from data (high level) 
  • Machine learning projects: example project lifecycle and where analytics fits 

Modules 7–9: Vertical 3 — Artificial Intelligence (AI) (Sections 1–3) 

  • Descriptive statistics: mean/median, spread, outliers (conceptual + practice) 
  • Choosing the right chart and avoiding misleading visuals 
  • Build a simple dashboard or slide with 2–3 key visuals 
  • Lab: explore a dataset and summarize key findings 

Module 7: Introduction to Artificial Intelligence — Section 1 (Hour 7) 

  • Welcome to AI: definitions and common misconceptions (what AI is/isn’t) 
  • How AI relates to data analytics (data in, predictions/automation out) 
  • Examples of AI applications (language, vision, recommendations, fraud detection) 
  • Activity: identify an AI use case and the data it would require 

Module 8: Introduction to Artificial Intelligence — Section 2 (Ethics, Bias, Responsible AI) (Hour 8) 

  • Ethics in AI: responsible use, transparency, and human oversight (high level) 
  • Bias in AI: where it comes from and how it can impact people and decisions 
  • Building responsible AI: basic mitigation ideas (data review, testing, monitoring—high level) 
  • Activity: review a short scenario and identify potential bias + mitigation steps 

Module 9: Introduction to Artificial Intelligence — Section 3 (Jupyter Notebook) (Hour 9) 

  • What is a Jupyter Notebook and why it’s used for data/AI exploration 
  • Notebook structure: cells, markdown, running code, and saving outputs 
  • Using notebooks to document experiments and results (reproducibility) 
  • Activity: open a sample notebook and run a simple analysis/model demo (guided) 

Modules 10–12: Vertical 4 — Cybersecurity (Sessions 1–3) 

  • Topics: What cybersecurity is; common threats; social engineering and phishing; passwords and multi-factor authentication; malware; privacy; cybersecurity careers and resources. 
  • Activities: “Digital Detective” threat hunt; “Spot the Phish” exercise; pattern recognition examples; short games (2 truths and a lie). 
  • Assessment: Participation; short quiz; reflection or brief incident write-up. 

Module 10: Introduction to Cybersecurity — Session 1 (Hour 10) 

  • Welcome to cybersecurity: what it is and why it matters 
  • Cybersecurity 101: threats, vulnerabilities, and basic defenses (high level) 
  • Common cybersecurity threats overview (malware, phishing, password attacks) 
  • Activity: “Digital Detective” — identify clues of a cyber incident in a scenario 

Module 11: Introduction to Cybersecurity — Session 2 (Social Engineering, Phishing, Passwords, MFA) (Hour 11) 

  • What is social engineering and why it works 
  • What is phishing? Common patterns and red flags 
  • Activity: “Spot the Phish” — analyze messages and explain the warning signs 
  • How hackers steal passwords; what multi-factor authentication (MFA) is and why it helps (plus optional video modules) 

Module 12: Introduction to Cybersecurity — Session 3 (Malware, Privacy, Careers) (Hour 12) 

  • What is malware? Common types and how infections happen 
  • Let’s hear the hacker’s point of view (discussion/video): motivations and tactics 
  • Data privacy and cybersecurity: protecting personal data and reducing exposure 
  • Cybersecurity careers + resources (including optional vendor/industry resources) and activity: “2 truths and a lie” 

Module 13: Practical Applications and IT Careers —(Industry Professional Discussion)  

  • Discussion with industry professional(s): roles, tools, daily work, and hiring expectations 
  • Career pathways: entry-level roles across programming, analytics, AI, and cybersecurity 
  • Portfolio guidance: what to include and how to present projects 
  • Q&A and next steps 

Module 14: IT Career Readiness Capstone  

  • Incident response basics: detect, contain, eradicate, recover (high level) 
  • Tabletop exercise: respond to a simulated security event 
  • Capstone: choose a vertical (programming/analytics/AI/security) and present a short project 
  • Career readiness: portfolios, resumes, and next-step certifications (overview) 

Additional Experiential Learning and Activities: 

To further enhance learning, the course may include opportunities for real-world exposure and professional insight into the four IT verticals: 

  • Guest Speakers: Software developers, data analysts, AI practitioners, and cybersecurity professionals discuss day-to-day work, tools, and pathways. 
  • Lab-Based Demonstrations: Live demos of coding workflows, data visualization, model experimentation, and basic security checks. 
  • Work-Based Learning (Optional): Job shadowing or virtual “day-in-the-life” sessions with an IT team, plus a short reflection log. 

These activities aim to broaden learner perspective, reinforce professional practices, and connect classroom skills to real-world IT work. 

Program Cost 

The Introduction to Information Technology course is offered at a fee of $3,000 per student. This all-inclusive cost covers the following: 

  • Tuition and Instructional Support: Instructor-led training, lab activities, and facilitated practice scenarios. 
  • Curriculum and Tooling Access: Course materials and any required app/tool access used during training. 
  • Experiential Learning Activities: Guest speaker honorariums and coordination for optional work-based learning are covered under the per participant fee. 

The per-student fee ensures a comprehensive educational experience that blends foundational concepts, hands-on lab skill-building, and exposure to real-world applications across programming, data analytics, AI, and cybersecurity.