Start with a blueprint

The full competency map for software engineering — languages and data structures, how computers and networks actually work, version control and testing, databases, systems design, and the practical application and interview preparation that hiring rests on. A starting point, not a promise: Kaidoro adapts it to what you already know.

What an AI engineer actually needs — Python and solid software engineering, how models behave, prompting and evaluation, retrieval and memory, agents and tool use, serving, cost and latency, and safety. Covers the ground from foundations to production, and Kaidoro will reshape it around your background.

The territory between data science and software engineering — linear algebra and probability, classical and deep learning, feature and data pipelines, training at scale, evaluation, deployment and monitoring. Broad by design, so you can see which parts you already have.

The competency map for quant development — C++ and performance, algorithms and data structures, mathematics and probability, operating systems, Linux and networking, market and instrument knowledge, and the interview preparation the field is known for. Firms differ enormously; this is the territory, not a checklist.

A research-led map — probability and stochastic processes, statistics and inference, time series, machine learning applied honestly to noisy data, backtesting and the many ways it lies, market microstructure, and how research is actually communicated. Mathematically serious by design.

Statistics and probability, Python and SQL, cleaning and exploring real data, modelling, experiment design and causal thinking, visualisation and communication, plus the practical application that makes it credible. Broader than a modelling course, and honest about how much of the job is not modelling.

Networking and operating system internals, cryptography, application and cloud security, offensive fundamentals used defensively, detection and incident response, plus the labs, certifications and legal boundaries that matter. Wide-ranging, because the field is.

One of the genuinely multidisciplinary maps — mathematics and dynamics, control, embedded systems and electronics, sensors and perception, motion planning, ROS and simulation, and building something that actually moves. Long by nature, and structured so you can see the dependencies.

Accounting and financial statements, valuation, three-statement and DCF modelling, M&A and LBO mechanics, industry and market awareness, plus Excel and PowerPoint fluency and the interview technical guides. Honest about how much the route depends on timing and process.

A long-horizon competency map — micro and macro theory, mathematics for economists, econometrics and causal inference, data and programming, applied fields, and the research and communication work economists actually do. Includes where formal study usually sits, without pretending it can be skipped.

Mapped around a profession that is genuinely exam-gated — probability and statistics, financial mathematics, actuarial models, risk, the exam sequence itself, plus the modelling, data and communication skills the day job needs. Realistic about the years involved.

Probability, expected value and mental arithmetic under time pressure, market microstructure and instruments, systematic strategy design, risk and position sizing, plus the games, puzzles and mock interviews the recruitment process is built around. Firms vary; this is the shared ground.

Accounting foundations, financial modelling and forecasting, valuation, corporate finance, Excel and data tooling, industry analysis, and the reporting and communication that makes analysis useful. A broad commercial map rather than a single certification path.

A genuinely long journey, mapped honestly — calculus and linear algebra, classical mechanics, electromagnetism, quantum mechanics, statistical physics, stellar and galactic astrophysics, cosmology, observational techniques, scientific computing and research practice. It shows the levels rather than compressing them into six months.

Cell and molecular biology, neuroanatomy, neurophysiology, cognitive and systems neuroscience, statistics and computational methods, laboratory and imaging techniques, and how neuroscience research is actually conducted and published. Multi-level and multi-year by nature.

Core psychological theory across cognitive, developmental, social and biological areas, research design, statistics and psychometrics, ethics, the replication crisis and open science, plus writing, reviewing and publishing. Built around producing evidence, not just reading about it.

Judgement and decision-making, behavioural economics, social and cognitive psychology, experimental design and field trials, causal inference and statistics, ethics, and applying it in policy or product. Honest about what the evidence does and does not support.

Exercise physiology, biomechanics, strength and conditioning, nutrition, sport and performance psychology, testing and monitoring, data analysis, injury and rehabilitation, and applied practice with real athletes. A science map rather than a personal training course.

The language, end to end — syntax and data types, functions and modules, the object model, comprehensions and iterators, errors, files and data, testing, the standard library, packaging and the idioms that make Python read like Python. A skill roadmap: it teaches Python, not a job title.

Core syntax and types, memory, pointers and ownership, functions and classes, the STL, modern C++ and RAII, templates and generic programming, error handling, concurrency, performance and build tooling. Genuinely a language roadmap, whatever you plan to use it for.

Linear algebra, probability and statistics, supervised and unsupervised learning, model evaluation and the ways it goes wrong, neural networks and deep learning, plus the practical work of getting from a dataset to a defensible result. Learning the subject, not preparing for a role.

Descriptive statistics and probability, distributions, sampling, estimation, hypothesis testing and its pitfalls, regression, experimental design, Bayesian thinking and practical analysis. A statistics education, not a data science career plan.

Language acquisition as it actually works — pronunciation and the sound system, high-frequency vocabulary, grammar in stages, listening, speaking, reading, writing, conversation practice and immersion, with clear milestones along the CEFR levels. Not a list of language-learning tips.

From requirements and scope through architecture, backend, model integration, conversation state, retrieval and memory, a usable interface, evaluation and testing, deployment and iteration. Focused on shipping the thing, not on becoming an AI engineer.

Scope and requirements, data modelling, API design, backend, frontend, authentication, testing, deployment and monitoring, then iteration. A build roadmap — the goal is a working app you can point people at.

Concept and scope, choosing an engine, prototyping the core loop, game systems, art and audio, level and content design, feel and polish, playtesting, performance and release. Structured around actually finishing, which is the part most game projects fail at.

Defining the strategy, market data, backtesting and the many ways backtests lie, execution and broker APIs, risk management and position sizing, monitoring and logging, paper trading and evaluation. Deliberately sceptical, and not financial advice.

How to investigate psychology intelligently — orientation, research foundations, the major schools, cognitive and behavioural psychology, neuroscience foundations, methodology, reading primary literature, evaluating evidence including the replication crisis, and synthesising it into a position you could defend.

Orientation in the field, probability and stochastic calculus foundations, asset pricing and derivatives, market microstructure, empirical asset pricing and factor research, the literature and its replication problems, backtesting scepticism, and forming your own research questions.

Orientation and hype-filtering, linear algebra and quantum mechanics foundations, qubits and circuits, the major algorithms, complexity, noise and error correction, the competing hardware platforms, and reading the primary literature well enough to tell a real advance from a press release.

Establish the format, sit a timed diagnostic, then work the weaknesses it exposes — data structures, algorithms and complexity, patterns, debugging under pressure, communication where the assessment is live, and repeated full mock attempts against the clock.

The format and what is actually being scored, a diagnostic mock case, structuring and issue trees, market sizing, quantitative work under pressure, business judgement, the fit and personal experience half, and repeated live practice with a partner.

The five subtests and their timing, a full diagnostic mock, then targeted work on verbal reasoning, decision making, quantitative reasoning, abstract reasoning and situational judgement, with mistake analysis and a mock schedule building to the test date.

The format and scoring, a diagnostic practice test, then logical reasoning question types, reading comprehension technique, argument structure and formal logic, timing strategy, mistake analysis by category, and a schedule of full timed tests up to the date.