Modern Data & AI Essentials
A one-day, high-level introduction to the concepts, technologies, processes and roles that make up the data-driven enterprise. Participants follow the data lifecycle from collection and preparation through analytics, visualization, machine learning and AI, then look at modern data architectures and how generative AI is changing the way organizations derive value from data. No programming or hands-on technical experience is required - the goal is genuine fluency in the conversation.
Course Overview
- One day - approximately 6.5 hours of instruction, demonstrations and discussion.
- Introductory: no programming or advanced technical experience required.
- Follows the data lifecycle end to end, from collection through analytics, machine learning and AI.
- Covers modern platforms and the roles involved, so participants know who does what and why.
Who Should Attend
- Business and data analysts.
- Project and program managers.
- IT professionals.
- Technical and business leaders, product managers and functional leaders.
- Professionals supporting data, analytics, AI or digital transformation initiatives.
- Individuals considering further training in data science, analytics or AI.
Prerequisites
- No programming or advanced technical experience is required.
- A basic understanding of how organizations use data is expected.
- General familiarity with business or IT processes is helpful.
What You'll Learn
By the end of this course, participants will be able to:
- explain how data science, analytics, business intelligence, machine learning and AI fit together
- describe the major stages of the data lifecycle
- recognize why data quality, preparation, governance, privacy and responsible use matter
- explain how analytics and visualization turn data into business insight
- explain fundamental machine learning and generative AI concepts, including LLMs and RAG
- describe modern data platforms and architecture concepts
- recognize common data and AI roles and how they work together
- identify the opportunities and the common failure modes in data and AI initiatives
Course Outline
- 1. The Modern Data-Driven Enterprise
- Data science, analytics, business intelligence, machine learning and AI.
- How the data landscape has evolved, from reporting to predictive and AI-driven decision-making.
- Common business use cases; demonstration and discussion.
- 2. Data Collection, Preparation and Quality
- Structured, semi-structured and unstructured data; collection and integration.
- Cleaning, preparation and quality; preparing data for analytics and AI.
- Data governance, privacy and responsible data use.
- 3. Analytics and Data Visualization
- Descriptive, diagnostic, predictive and prescriptive analytics.
- Basic statistical concepts used in analytics.
- Principles of effective visualization and the categories of BI tooling.
- 4. Machine Learning and AI Fundamentals
- What machine learning is and is not; supervised and unsupervised learning.
- Classification, regression, clustering and forecasting; training data, models and model limits.
- Traditional AI versus generative AI; large language models and Retrieval-Augmented Generation.
- Privacy, security, bias, hallucinations and responsible AI.
- 5. Modern Data Platforms, Tools and Roles
- Traditional databases and modern data environments; warehouses, lakes and lakehouses.
- Cloud-based, distributed and scalable data processing.
- Roles including Data Analyst, Data Engineer, Analytics Engineer, Data Scientist,
- AI/ML Engineer, Data Architect and Data Governance, and how they work together.
- 6. Putting Data and AI to Work
- Starting with the business problem, then identifying and preparing the right data.
- Building cross-functional teams and moving from proof of concept to production.
- Measuring business value; responsible AI and governance.
- Why data and AI initiatives commonly fail.
Next Steps
- Participants leave able to take part in decisions involving analytics, modern data environments,
- machine learning, generative AI and enterprise data strategy - and better placed to choose
- deeper training in data science, analytics or AI.
Related training topics
Justify your training
Use this sample request letter — copy it into an email to your manager and personalize the bracketed details to make the case for the time and budget.
Sample training request letter
Subject: Request for Data Analytics & Databases training from Applied Technology Academy
[Decision Maker Name],
I'm writing to request time and budget approval to complete Applied Technology Academy's course, Modern Data & AI Essentials. The information below outlines how this training benefits our organization, the tasks I'll be able to perform after completing it, and relevant cost and funding details.
Course Description
A one-day, high-level introduction to the concepts, technologies, processes and roles that make up the data-driven enterprise. Participants follow the data lifecycle from collection and preparation through analytics, visualization, machine learning and AI, then look at modern data architectures and how generative AI is changing the way organizations derive value from data. No programming or hands-on technical experience is required - the goal is genuine fluency in the conversation. Applied Technology Academy is an award-winning, SBA-certified woman-owned training provider (est. 2019) whose instructors are active practitioners. The course combines instructor-led training with practical exercises, real-world examples, and computer-based activities designed to reinforce job-relevant skills.
Course Objectives
Once I've completed the course, I'll be able to:
- By the end of this course, participants will be able to:
- explain how data science, analytics, business intelligence, machine learning and AI fit together
- describe the major stages of the data lifecycle
- recognize why data quality, preparation, governance, privacy and responsible use matter
- explain how analytics and visualization turn data into business insight
- explain fundamental machine learning and generative AI concepts, including LLMs and RAG
- describe modern data platforms and architecture concepts
- recognize common data and AI roles and how they work together
Expected Organizational Benefits
After completing this course, I will be better equipped to apply these skills directly to our projects, reduce our reliance on outside expertise, strengthen our team's capabilities, and share what I learn with colleagues.
Expected Cost & Funding
Course fee: [request an itemized quote at the link below]. Applied Technology Academy supports multiple funding paths that may reduce or cover this cost: GSA MAS purchasing and government purchase orders, military credentialing funding (Army CA, AF COOL, CG COOL), VA GI Bill and VR&E, ATA Flexible Spending, and student financing. Private team cohorts are available if colleagues should attend with me.
Conclusion
This training provides practical, hands-on experience I can apply immediately to strengthen our work in Data Analytics & Databases. Additional course information is available at https://appliedtechnologyacademy.com/modern-data-and-ai-essentials/.
Thank you for your consideration,
[Your Name]
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