Field
Data Science
Analyze data, build models, and prepare for graduate study. Python, SQL, statistics, mathematics, machine learning, and data engineering. Roles include Data Analyst, Data Scientist, Machine Learning Engineer, Data Engineer.
SQL & DataQueries, joins, and database design0 of 6 cards masteredPython ScriptingScripts for automation: files, APIs, devices0 of 8 cards masteredCustomer-Facing SkillsDiscovery, delivery, and communication0 of 6 cards masteredTroubleshootingFault-finding drills across industrial, network and Linux0 of 15 cards masteredStatisticsWhat a number is allowed to claim: samples, averages, spread, and charts that mislead.0 of 4 cards masteredAIWhat artificial intelligence is, what it can do, and what it still is not.0 of 4 cards masteredAI fundamentalsHow these systems actually work: data, models, prediction, and the usual limits.0 of 4 cards masteredMachine learningHow a system learns from examples — and how that learning goes wrong.0 of 4 cards masteredLanguage modelsHow large language models produce text — and how to use that without mistaking it for a mind.0 of 4 cards masteredA · Python for dataFoundation lesson and study roadmap. Priority A (start first). variables, functions, NumPy, pandas0 of 2 cards masteredA · SQLFoundation lesson and study roadmap. Priority A (start first). joins, aggregation, window functions, query design0 of 2 cards masteredB · Data cleaning and explorationMaster’s prep study outline. Priority B (next). missing values, outliers, feature types0 of 1 cards masteredB · Data visualizationMaster’s prep study outline. Priority B (next). choosing charts, reading charts, pitfalls0 of 1 cards masteredB · Machine learning basicsMaster’s prep study outline. Priority B (next). supervised vs unsupervised, train/test split, overfitting, cross-validation0 of 1 cards masteredB · Core modelsMaster’s prep study outline. Priority B (next). linear and logistic regression, decision trees, random forests, k-means0 of 1 cards masteredB · Model evaluationMaster’s prep study outline. Priority B (next). accuracy, precision/recall, ROC/AUC, confusion matrix0 of 1 cards masteredC · Deep learning basicsMaster’s prep study outline. Priority C (later). neural networks, backpropagation, common architectures0 of 1 cards masteredC · Data engineeringMaster’s prep study outline. Priority C (later). ETL/ELT, data pipelines, warehouses vs lakes0 of 1 cards masteredC · Responsible data scienceMaster’s prep study outline. Priority C (later). privacy, bias, fairness, governance0 of 1 cards masteredStatistics & ProbabilityMath for Data Science · Study order 1. Measure uncertainty and decide whether a pattern is real or luck. What a p-value is and is not; Bayes by hand; CLT in one sentence; overfitting vs underfitting.0 of 5 cards masteredLinear AlgebraMath for Data Science · Study order 2. Data is tables of numbers (matrices); models transform them. Why PCA works (eigenvectors of the covariance matrix); similarity as a dot product / cosine; what rank means.0 of 4 cards masteredCalculus & OptimizationMath for Data Science · Study order 3. Models learn by reducing error; the gradient tells which direction. Explain gradient descent in plain words; what the learning rate does; why the chain rule matters for neural networks.0 of 4 cards masteredDiscrete Math & AlgorithmsMath for Data Science · Study order 4. Count possibilities and reason about efficiency. Big-O of common operations; basic counting; what entropy measures.0 of 4 cards mastered