You don’t need a technical background to begin. We’re building a place to learn Python, SQL, data science, and AI engineering—one clear explanation, small experiment, and useful project at a time.
In development. Courses and enrollment are not open yet.
Learn a thing.Build a thing.Show the thing.
Start where you are
Curiosity is a good prerequisite.
Maybe you’ve never written code. Maybe you’ve copied a tutorial and wondered what any of it meant. That’s a starting point.
DWKD Academy is being designed for people learning these skills for the first time. The plan starts with the basics, explains the unfamiliar words, and gives you something small to try before asking you to build something bigger.
01
Learn a thing.
Understand the idea, see an example, and predict what will happen.
02
Build a thing.
Change it, make a mistake, work out why, and try your own solution.
03
Show the thing.
Make something useful and explain how it works, what you learned, and where its limits are.
A small taste of the lesson experience
See the idea. Then make it click.
Code can look unfamiliar until you connect it to a question you already understand.
Take free delivery. An order qualifies when its total is at least $100. Python has a way to ask that exact question.
This is a concept preview with an illustrative Python example. Full coding lessons and saved practice are coming soon.
Python / ComparisonsConcept preview
Does this order qualify?
Turn a question into a comparison.
The symbol >= means greater than or equal to. Read order_total >= 100 as “Is the order total at least 100?”
order_total = 75
print(order_total >= 100)
$75Order total<$100Free delivery starts here
Python answers False. The order is below $100. A comparison answers a yes-or-no question; Python calls those answers True and False.
Change the total. Keep the rule.
Use the slider to explore the comparison. Watch what happens below, at, and above $100.
From $0 to $200. Use the arrow keys to change the total by $25.
order_total = 75
print(order_total >= 100)
Comparison result
$75 is below $100, so the comparison is False.
What happens right at the boundary?
An order is now exactly $100. The rule is still “at least $100.”
order_total = 100
print(order_total >= 100)
Show a hint
The rule includes orders greater than $100 and orders equal to $100. Which part of >= accounts for equality?
Explain the answer
The answer is True. The = in >= includes the boundary value. If you used > instead, an order of exactly $100 would not qualify.
You’ve just used a comparison, predicted a result, and checked an edge case. Small ideas like this are the building blocks of larger programs.
Planned learning paths
Choose your starting point. Build from there.
These standalone curricula are in development. Each is designed to welcome beginners; you won’t need to finish one before starting another.
Planned curriculum
Python: Zero to Hero
Start with your first program, then learn to work with data, organize your code, automate useful tasks, test your ideas, and build applications.
Work toward: a program you can build, debug, and explain yourself.
In development
Planned curriculum
SQL: Zero to Hero
Learn how tables fit together, ask questions of data, investigate surprising results, and design databases that support real work.
Work toward: a database investigation with answers you can support.
In development
Planned curriculum
Data Science: Beginner
Start by understanding a dataset. Learn the Python and SQL you need along the way, then clean, explore, visualize, and evaluate what the data can tell you.
Work toward: a reproducible investigation and a clear recommendation.
In development
The flagship path / In development
AI Engineering Bootcamp.
A planned journey from technical foundations to building, evaluating, and operating useful AI systems. Designed to begin without prior coding experience.
OnlineSelf-paced format
12 coursesFoundations through capstone
600 planned hoursA curriculum planning estimate
600 hours is our curriculum planning estimate. Courses, labs, and assessments are in development; completion time will vary.
Data Structures, Algorithms, and Object-Oriented Programming10 hours
Git, Testing, Debugging, and Packaging8 hours
HTTP, APIs, and Asynchronous Python10 hours
JavaScript, TypeScript, and Frontend Foundations6 hours
02Mathematics and Statistics for AI40 planned hours
Linear Algebra12 hours
Calculus and Numerical Optimization10 hours
Probability8 hours
Statistics and Experimental Design10 hours
03Data Analysis and Engineering40 planned hours
NumPy, pandas, and Data Visualization8 hours
SQL and PostgreSQL10 hours
Data Modeling and Processing Pipelines10 hours
Data Quality and Validation6 hours
Data Versioning, Lineage, and Governance6 hours
04Applied Machine Learning60 planned hours
Problem Framing and Baseline Models6 hours
Regression and Classification14 hours
Feature Engineering and Model Selection10 hours
Clustering and Anomaly Detection8 hours
Time-Series Forecasting and Recommendation Systems10 hours
Model Evaluation, Calibration, and Interpretability12 hours
05Deep Learning and Transformers60 planned hours
PyTorch, Tensors, and Automatic Differentiation8 hours
Neural Networks and Backpropagation10 hours
Training, Optimization, and Regularization10 hours
Convolutional Networks and Transfer Learning8 hours
Natural Language Processing, Tokenization, and Embeddings8 hours
Attention and Transformer Architectures16 hours
06LLM Application Engineering60 planned hours
Language-Model Capabilities and Model Selection6 hours
Model APIs and Structured Generation10 hours
Prompt Engineering and Context Engineering10 hours
Conversation State and Memory8 hours
Full-Stack AI Applications, Streaming, and Authentication14 hours
LLM Evaluation and Regression Testing12 hours
07Retrieval-Augmented Generation and Search50 planned hours
Document Parsing and Ingestion8 hours
Chunking, Embeddings, and Vector Databases10 hours
Keyword, Semantic, and Hybrid Search10 hours
Reranking and Query Transformation8 hours
Retrieval Evaluation and Citation Verification8 hours
Document Permissions, Refresh, and Deletion6 hours
08AI Agents and Workflow Automation50 planned hours
Tool Calling and Schema Validation8 hours
Workflow Patterns and State Machines8 hours
Agent Planning and Orchestration10 hours
Model Context Protocol and External Integrations8 hours
Durable Execution, Checkpointing, and Recovery8 hours
Agent Evaluation, Human Approvals, and Sandboxing8 hours
09Model Adaptation and Inference40 planned hours
Training-Dataset Curation and Annotation8 hours
Supervised Fine-Tuning10 hours
Parameter-Efficient Fine-Tuning, LoRA, and QLoRA8 hours
Preference Optimization and Model Distillation6 hours
Quantization, Batching, and Model Serving8 hours
10Multimodal AI Engineering40 planned hours
Vision-Language Models and Image Understanding10 hours
Document Intelligence, OCR, and Structured Extraction10 hours
Speech Recognition and Speech Synthesis8 hours
Real-Time Voice Applications8 hours
Diffusion Models and Image Generation4 hours
11Production AI Engineering60 planned hours
Docker and Cloud Deployment10 hours
Continuous Integration, Delivery, and Infrastructure as Code8 hours
MLOps, LLMOps, and Artifact Versioning8 hours
Tracing, Monitoring, and Drift Detection8 hours
AI Security, Privacy, and Threat Modeling12 hours
System Design, Scalability, and Reliability8 hours
Cost Engineering and Performance Optimization6 hours
12Capstone and Portfolio50 planned hours
Problem Discovery, Requirements, and Architecture6 hours
Data Preparation, Baselines, and Evaluation Planning6 hours
Full-Stack AI System Implementation20 hours
Security, Quality, and Performance Testing8 hours
Deployment, Documentation, and Technical Handoff6 hours
Portfolio Case Study and Final Assessment4 hours
The work should speak for itself
Build something you can explain.
The planned capstone brings the pieces together: define a useful problem, build an AI application, test its behavior, document its limits, and show the result. The aim is understanding you can demonstrate.
Before you begin
Good questions. Clear answers.
Do I need to know how to code?
No prior coding knowledge is planned as an entry requirement. The Academy is being built to explain the basics, introduce new ideas in order, and give you frequent opportunities to practice.
Do I have to finish Python before Data Science?
No. Data Science: Beginner is being designed as its own starting point. It will teach the Python and SQL needed for its projects. The standalone Python and SQL curricula are intended for deeper study.
Can I enroll or start the bootcamp today?
Not yet. This page introduces the planned Academy and provides a small concept preview. The bootcamp is not open for enrollment, and subscribing for updates does not enroll you or reserve a place.
Does 600 hours mean 600 hours are ready?
No. It is the planning allocation across the 12 AI Engineering courses. The full teaching, projects, assessments, and practice environments are still in development. Actual learning time will vary.
When will Academy launch, and what will it cost?
Academy is coming soon. A launch date and pricing have not been announced. We’ll share the release scope and access details before enrollment opens.