Module 1: Data Science Foundations (1 week)

Participants will gain skills in data preparation, visualization, manipulation, transformation, working with databases, and handling varying data types. By the end of this module, they will have explored the Data Science lifecycle, understood the basics of databases, and learned about different data types and their treatment methods.

Module 2: Machine Learning Foundations (2 weeks)

This module focuses on algorithm design, working with supervised and unsupervised learning algorithms, and selecting the best algorithm for specific problems. Participants will gain insights into the mathematical formulations of popular algorithms and learn to build and train models for real-world scenarios.

Module 3: Statistical Model Validation & Testing (1 week)

Participants will cover evaluation criteria, statistical hypothesis testing, fair model comparison, and techniques for improving machine learning models. They will learn to avoid common evaluation pitfalls and ensure accurate, statistically sound comparisons.

Module 4: Neural Networks & Deep Learning for (1 week)

This module equips participants with knowledge of neural network architecture, backpropagation, Python’s Keras library, and techniques for debugging and improving deep learning models. Participants will also understand the mathematical foundations of neural networks and their significant impact on Machine Learning advancements.

Module 5: Deep Learning in Advanced Data Types (2 weeks)

Participants will focus on processing advanced data types such as images, text, and time-series data, exploring Convolutional and Recurrent Neural Networks for these applications. This module provides a strong foundation in fields like Computer Vision, Natural Language Processing (NLP), and Time-series analysis.

Module 6: Transformers & Large Language Models over (1 week)

This module dives into Transformers in NLP and their application in Computer Vision, fine-tuning large language models, and mastering prompt engineering. Participants will develop practical skills in working with Transformer architectures and LLMs, including understanding their self-attention mechanism and optimization techniques like LoRA and QLoRA.

Module 7: Machine Learning in Production (2 weeks)

Participants will explore best practices in machine learning development, deployment, and scaling models for real-world use. They will learn how to maintain and monitor deployed models, overcome deployment challenges, and leverage cloud-based ML solutions to implement scalable machine learning systems.

Module 1: Data Analytics Foundations (2 weeks)

Participants will gain proficiency in R programming, particularly using Jupyter Notebooks, and explore the data analytics process, including data collection, exploratory data analysis (EDA), and visualization. They will develop skills in conducting EDA using libraries like tidyverse and ggplot2. By the end of this module, participants will have a foundational understanding of how to manipulate, analyze, and visualize data using R.

Module 2: Data Wrangling with R (1 week)

This module focuses on assessing data quality, identifying data issues, and applying various data cleaning techniques using R. Participants will also learn how to transform data into formats suitable for analytics. These skills will prepare participants to handle real-world data challenges effectively.

Module 3: Statistical-Based Analysis (2 weeks)

Participants will gain a solid understanding of statistical concepts, including probability, distributions, the central limit theorem, confidence intervals, hypothesis testing, and A/B testing. They will also explore linear and logistic regression and their applications in data analysis, equipping them to perform statistical analyses confidently.

Module 4: Data Analytics with Excel (1 week)

In this module, participants will learn how to explore and prepare data using Microsoft Excel’s built-in features such as filtering, sorting, conditional formatting, and formulas. They will acquire the skills needed to reshape and clean data for analytics, gaining a strong foundation in using Excel for data analysis.

Module 5: Machine Learning Basics (1 week)

This module introduces participants to Artificial Intelligence and its applications, focusing on foundational machine learning algorithms. Participants will develop the skills to assess the performance of machine learning models using metrics such as accuracy, precision, and recall, preparing them to apply machine learning techniques to various tasks.

Module 6: Exploring Databases (2 weeks)

Participants will explore different types of databases and learn to distinguish between relational and non-relational databases. They will gain a basic understanding of SQL, including its usage for querying and manipulating databases, providing them with essential database management skills.

Module 7: Storytelling with Data (1 week)

Participants will revisit the data science lifecycle in the context of production, focusing on data collection, labeling, processing, analytics, model training, debugging, deployment, and monitoring. They will explore the challenges of real-world implementations and best practices for scaling solutions.

1) AI Foundations: What Marketers Need to Know

Focus: Understand what AI can do for marketing and turn raw signals into useful customer and competitor insights.

What AI is good at in marketing today: research, creative help, targeting, and reporting
Where your data lives: CRM, analytics, ad platforms, reviews, social listening, support tickets
Leveraging AI for competitor monitoring and trend analysis.
Turning comments and conversations into themes, pain points, and opportunities
Use approved tools only and avoid pulling or storing personal data without clear consent

2) Shaping Brand Identity with LLMs

Focus: Use AI to sharpen your value proposition, positioning, and message map in a brand-safe way.

What are Large Language models and how they work
Prompt engineering essentials for marketers: context, audience, tone, must-include and must-avoid
Message maps that link customer pains to claims, proof points, and clear calls to action
Claim integrity: require substantiation (references, data, or testimonials) before publishing
Adapting tone and voice for different segments and stages of the customer journey
Leverage AI to brainstorm product features, packaging, and user experience elements aligned with brand identity.

 

3) Content Strategies Reinvented with AI

Focus: Produce channel-ready copy and visuals that match your goals and brand voice.

SEO and web: topic ideas, briefs, outlines, and landing page sections that convert
Visuals: creative prompts for images or short videos, consistent style cues, reuse across formats
Email and ads: subject lines, body copy, hooks, and multiple versions to test
Social: calendars, post ideas, community replies, and simple guidance for creators or partners
Matching each asset to a channel, a goal, and a simple success metric
Brand safety pass: respect IP, avoid misleading claims, and label AI visuals if your policy requires

 

4) Metrics that Drive Change

Focus: Link business goals to the right metrics, use customer feedback to spot opportunities, and turn insights into ongoing improvements to campaigns and journeys.

Building a clear line from business goals to channel targets and daily KPIs
Choosing a sensible way to measure impact and avoid misleading results
Writing practical test plans with a hypothesis, a basic sample estimate, and success criteria
Fair testing: compare results across key segments to spot skewed outcomes before scaling
Simple scenario planning to see how budget changes affect reach, cost, and volume
Using AI to analyze customer feedback and performance data in real time.
Spotting churn/upsell signals and campaign optimization opportunities.
Transparency: using feedback responsibly, minimizing intrusive targeting.

 

5) Workflow Automation for Marketers

Focus: Speed up everyday tasks using the tools you already have, without technical setup.

Turning intake forms or templates into consistent briefs for creative and media
Using automation to schedule welcomes, follow-ups, and re-engagements
Keeping a tidy review and approval process so assets go live faster
Reducing manual copy-and-paste work with folders, templates, and checklists
Respect preferences: honor consent and frequency caps; log approvals for automated sends
Setting simple rules for who sees what, when, and how progress is tracked

 

6) Agents in the Loop

Focus: Use AI helpers to watch performance, spot issues, and suggest next actions you can approve.

What an “AI agent” means in marketing and when it is useful
Daily summaries that highlight wins, problems, and recommended adjustments
Evidence-first suggestions: require links to sources (dashboards, pages, tickets) for quick review
Proactive checks like broken links, price mismatches, or off-brand phrases
Guardrails so agents suggest changes but people make the final call; block high-risk actions from auto-execution
Keeping a short audit trail so you know what was suggested and why

1) AI Foundations for Healthcare Teams

Focus: What AI really does in healthcare and where it helps today.

AI essentials: what AI is (tools that learn patterns) and what it isn’t (a final decision-maker).
Short history: key milestones from early rule-based systems to today’s generative AI.
Why it matters now: better documentation, triage support, imaging assistance, scheduling, patient communication.
AI domains in healthcare
Benefits vs. limits: speed and consistency vs. hallucinations, bias, and stale information.

2) Healthcare Data & the ML Process 

Focus: Understand your data and how AI tools learn.

What data looks like: EHR fields, vitals and labs, imaging files, clinical notes, wearables, patient messages.
Data quality basics: complete and correct entries, consistent units, up-to-date meds/allergies, fewer free-text surprises.
ML lifecycle at a glance: collect → train → validate → test → monitor
What this means for practice: capture cleaner inputs, read outputs critically
Patient data privacy: PHI stays in approved systems; access control, minimal necessary use, simple logging

 

3) Prompting Essentials for Clinical Communication & Insights

Focus: Use Gen-AI safely for notes, patient explanations, and evidence summaries.

What prompting is: state your role, the clinical context, and the exact task and format you want.
Clinical documentation: request structured outputs for HPI, assessment, plan, discharge notes, and referrals.
Patient communication: ask for plain language, appropriate reading level, and respectful, culturally aware wording.
Evidence summary: frame the question (PICO), ask for study type and key findings, include limitations and certainty.
Quick literature matrix: population, intervention, outcomes, key finding, caveats
Data analysis prompts: dataset/timeframe/units/normals; ask for QC (missing values, outliers, unit mix-ups) first
Descriptives & trends: counts/means/medians/ranges; time trends; simple cohorts (pre/post)
Charts to request: lines (vitals over time), bars (category counts), scatter (simple relationships)
Nursing workflows: SBAR, shift handoffs, care plan updates, patient instructions
PHI rules in plain language (HIPAA/GDPR, residency, approved vendors)
Why LLMs hallucinate (plain English): predictive text, gaps in training data, outdated knowledge cutoffs, and no live EHR access

 

4) Computer Vision in Clinical Practice

Focus: How AI assists radiology, pathology, dermatology, dentistry.

Core tasks: detection (find an abnormality), classification (type), segmentation (outline), localization and measurement.
Feature extraction: turns pixels into simple features (edges, shapes, sizes, densities, textures) that support the suggestion.
Workflow uses: triage and prioritization, second-reader checks, structured measurements for reports.
Limitations: false positives/negatives, image quality and artifacts, device or site differences, lack of clinical context.
Role notes: radiology (worklist triage), dentistry (caries/periapical), derm (lesion triage)

5) Automation in Care Operations 

Focus: Reduce manual admin work by automating repeatable tasks in existing systems.

Where to start: repetitive, rules-based, high-volume, low-risk workflows
Map the path: trigger → inputs → steps → output → owner
Low-lift automations: Set up appointment reminders and follow-ups that send the right message at the right time and log the outcome automatically.
Automate discharge check-ins and medication reminders to improve adherence and reduce missed follow-ups.
Reliability and safety: approvals where needed, clear error paths, retries, audit logs, PHI kept inside approved tools

 

6) AI Agents in Healthcare Workflows

Focus: Deploy AI helpers that plan multi-step tasks, monitor signals, and propose actions with human approval.

What agents add beyond automation: planning, tool use, memory, self-checks
AI agents vs LLMs: what is the difference and when to use each
Common use cases: inbox triage, lab/radiology follow-ups, care-gap monitoring, benefits checks
Guardrails: Keep humans in the loop by sending suggestions for approval before anything is scheduled, ordered, or communicated to patients.
Evidence discipline: link suggestions to EHR entries, labs, or imaging and note confidence; record approvals

1) AI Foundations for Lawyers 

Focus: Understand what AI can (and can’t) do in legal work and set up clean, confidential data practices.

Where AI helps today: research, drafting, review, summarization, client communications
Matter data basics: DMS versions, emails, transcripts, privilege tags, sources of truth
Confidentiality boundaries: what never goes into public tools; approved enterprise tools only
Success measures for pilots: quality, speed, cost, risk (define before/after)

2) Prompt Engineering for Legal Drafting & Research

Focus: Write prompts that produce reliable, on-jurisdiction, cite-ready outputs you can verify fast.

Prompt structure: role, facts, jurisdiction, date cutoff, tone, constraints
Retrieval-aware prompts: require sources (cases/statutes/regs/firm docs) and pin-cites/sections
Structured outputs: issues lists, tables, checklists, risk matrices, clean drafts
Research workflows with AI: framing issues, identifying keywords, and targeting authoritative sources.
Self-audit techniques: test assumptions, flag missing facts, and record confidence notes.
Bias & hallucination awareness: why models miscite or skew outputs — and how to catch it before it reaches a client.

3) Contracts, Due Diligence & Negotiation Support

Focus: Accelerate contract review/drafting with clear risk views and firm playbooks.

Clause extraction/compare; risk scoring and fallback suggestions
Red-flag scans across many docs (change-of-control, indemnity, assignment, non-compete)
Clause retrieval from DMS: pull prior negotiated language/playbook clauses and cite them
Generate partner-ready clean drafts from redlines, with a brief “sources used” note
Fact-check language: watch for fabricated clause summaries; compare against actual text before redlining

4) Litigation Evidence & Case Building

Focus: Turn documents and transcripts into a defensible storyline and preparation materials.

Chronology framework: date/time, event, Source/Bates, element/claim, reliability score
Transcript analytics: admissions, inconsistencies, impeachment cites, follow-ups
eDiscovery triage (no code): issue tags, privilege screens, PII flags; chain-of-custody basics
Motion/brief prep kit: fact section scaffold, exhibit index, pin-cite discipline
Retrieval in practice: ensure chronology/briefs link back to exhibits and page:line quotes

5) Workflow Automation & Knowledge Operations 

Focus: Streamline matter workflows without coding and make knowledge easy to find and reuse.

Approvals, reminders, and filing rules to reduce manual handling and delays
Light automations with existing tools (no scripting): routing, notifications, checklists
Knowledge ops: naming/tags/folders so past memos/clauses are retrievable in seconds
Human check-points: require approvals where risk exists; one-click rollback for mistakes

6) AI Agents for Legal Workflows

Focus: Deploy agent assistants to handle multi-step legal tasks with human approval and full source evidence.

Agents vs. automations: planning steps, using tools, handing off for approval
Candidate workflows: intake → issues list → draft → cite-check → billing narrative; daily digest/alerts
Guardrails: thresholds, human-in-the-loop, blocked data, brand/voice limits
Source discipline: agent outputs include links/pin-cites and brief rationale
Operations: logs, ownership, KPIs (time saved, error rate, review time), pilot → scale

1) Foundations & Data in Finance

Focus: What AI/Gen-AI really means in finance and why clean data drives useful outputs.

Financial data landscape: transactions, P&L, balance sheet, cash flows, GL, CRM
Data quality: granularity, mapping, period alignment, currency/FX handling
Where AI adds value today: Close and reporting prep, variance explanations, anomaly and fraud cues, narrative drafts, scenario planning.
Privacy/compliance in practice: client data stays in approved/enterprise tools; minimal-access & auditability

2) Prompting for Finance 

Focus: Write prompts that yield accurate numbers, clear structures, and audit-ready outputs.

What are large language models and how they work
Finance tasks LLMs are good at
Prompt patterns: context, dataset scope, period, units, constraints, required formats (tables, bullet rationales)
Risk & credit prompts: risk matrices, PD/LGD reasoning, covenant checks, “unknown acceptable” to avoid made-up figures
Fraud cues via prompting: request rule-based red flags (dup invoices, round-number spikes, weekend activity) + rationale
Source discipline: ask for cell refs, query names, or calc steps so numbers are traceable

3) Financial Reporting and Budgeting with AI

Focus: Build faster reports, explain variances, and produce simple forecasts you can defend.

Variance analysis: drivers, mix/price/volume splits, waterfall-style narratives
Ratio analysis: liquidity, leverage, efficiency; trend commentary in plain English
Forecasting essentials: scenario prompts (base/upside/downside), assumption tables, sensitivity notes, time-series forecasting
Fraud & anomaly pass: outlier scan before sign-off; flag and park suspicious entries for review
Auditability: require a “numbers trail” (data source, calc, date) in every AI summary

4) Investment & Market Intelligence with AI

Focus: Research a space or ticker, synthesize signals, and spot opportunities (not advice).

Company and sector scan: earnings call takeaways, consensus themes, competitive moves
Screeners via prompting: filters for growth, margin, leverage, cash generation; watchlists
Thesis notes: catalysts, risks, valuation angles (qualitative), “what would change my mind”
Ethics/compliance: distinguish facts vs. model text; cite sources/dates; avoid undisclosed use of restricted info

5) AI-Powered Automation in Finance Ops

Focus: Remove swivel-chair work in reporting, reconciliations, and stakeholder updates.

Automate: data pulls, refreshes, schedule-based summaries, stakeholder digests
Reconciliations: exception queues, match suggestions, status updates to owners
Workflow controls: approvals, logs, failure paths, segregation of duties
Consent & comms: respect contact preferences and frequency; log who approved automated sends

6) AI Agents in Finance Workflows

 

Focus: Use agents for multi-step monitoring and recommendations with human approval.

What agents add beyond automation: planning, tool use, memory, self-checks
Use cases: transaction monitoring (fraud/anomalies), regulatory scanning, portfolio or KPI watch
Guardrails: blocked actions, thresholds, timeouts, role-based approvals; evidence links required
KPIs: suggestion acceptance rate, time saved, false-positive rate, audit completeness