Title: Data Science Essentials — LearnCoachAssist

Q: What is data science?
A: An interdisciplinary field using statistics, programming, and domain knowledge to extract insights from data.

Q: What is the data science process?
A: Problem definition → data collection → cleaning → exploration → modeling → evaluation → deployment.

Q: What is CRISP-DM?
A: Cross-Industry Standard Process for Data Mining — a structured methodology.

Q: What is a dataset?
A: A collection of related data, typically organized in rows and columns.

Q: What is a feature?
A: A measurable property or characteristic used as input to a model.

Q: What is a target variable?
A: The output a model tries to predict.

Q: What is supervised learning?
A: Learning from labeled examples to predict outcomes.

Q: What is unsupervised learning?
A: Finding patterns in unlabeled data.

Q: What is reinforcement learning?
A: Learning by taking actions and receiving rewards/penalties.

Q: What is semi-supervised learning?
A: Learning from a mix of labeled and unlabeled data.

Q: What is regression?
A: Predicting a continuous numeric output.

Q: What is classification?
A: Predicting a categorical output.

Q: What is clustering?
A: Grouping similar data points without predefined labels.

Q: What is dimensionality reduction?
A: Reducing the number of features while preserving information.

Q: What is feature engineering?
A: Creating new features from raw data to improve model performance.

Q: What is feature selection?
A: Choosing the most relevant features for a model.

Q: What is data cleaning?
A: Detecting and correcting errors in data: missing values, duplicates, outliers.

Q: What is exploratory data analysis (EDA)?
A: Initial investigation of data to discover patterns, anomalies, hypotheses.

Q: What is missing data?
A: Values absent from a dataset, often coded as NaN or NULL.

Q: How can missing data be handled?
A: Drop rows/columns, impute with mean/median/mode, model-based imputation, or use models robust to missingness.

Q: What is an outlier?
A: A data point significantly different from others.

Q: How to detect outliers?
A: Z-scores, IQR, boxplots, isolation forests.

Q: What is normalization?
A: Scaling features to a common range, e.g., [0, 1].

Q: What is standardization?
A: Centering features to mean 0 with standard deviation 1.

Q: What is one-hot encoding?
A: Converting categorical variables to binary columns.

Q: What is label encoding?
A: Converting categorical labels to integer values.

Q: What is target encoding?
A: Replacing a category with the target mean for that category.

Q: What is the train-test split?
A: Dividing data into training and testing sets to evaluate models.

Q: What is cross-validation?
A: Splitting data into folds and rotating test sets to estimate model performance.

Q: What is k-fold cross-validation?
A: Cross-validation with k folds.

Q: What is stratified k-fold?
A: K-fold preserving class proportions in each fold.

Q: What is data leakage?
A: When information from outside the training data influences model training, inflating performance.

Q: What is overfitting?
A: A model that performs well on training data but poorly on new data.

Q: What is underfitting?
A: A model too simple to capture data patterns.

Q: What is the bias-variance tradeoff?
A: Balancing error from oversimplification (bias) and overcomplexity (variance).

Q: What is regularization?
A: Penalizing model complexity to prevent overfitting.

Q: What is L1 regularization (Lasso)?
A: Adding absolute values of coefficients to the loss; promotes sparsity.

Q: What is L2 regularization (Ridge)?
A: Adding squared coefficients to the loss; shrinks all coefficients.

Q: What is elastic net?
A: Combining L1 and L2 regularization.

Q: What is gradient descent?
A: An optimization algorithm that iteratively minimizes a loss function.

Q: What is the learning rate?
A: The step size for each gradient descent update.

Q: What is stochastic gradient descent (SGD)?
A: Gradient descent using single (or small batches of) examples.

Q: What is mini-batch gradient descent?
A: Gradient descent using small batches of examples.

Q: What is a loss function?
A: A function measuring how wrong a model's predictions are.

Q: What is mean squared error (MSE)?
A: Average of squared differences between predicted and actual values.

Q: What is mean absolute error (MAE)?
A: Average of absolute differences between predicted and actual values.

Q: What is cross-entropy loss?
A: A loss function for classification measuring distance between probability distributions.

Q: What is accuracy?
A: The fraction of predictions that are correct.

Q: What is precision?
A: True positives / (true positives + false positives).

Q: What is recall?
A: True positives / (true positives + false negatives).

Q: What is the F1 score?
A: The harmonic mean of precision and recall.

Q: What is a confusion matrix?
A: A table showing predicted vs. actual classifications.

Q: What is ROC curve?
A: A plot of true positive rate vs. false positive rate at various thresholds.

Q: What is AUC?
A: Area Under the ROC Curve; measures classifier discrimination.

Q: What is precision-recall curve?
A: A plot of precision vs. recall at various thresholds, useful for imbalanced data.

Q: What is class imbalance?
A: When one class has many more examples than others.

Q: How to handle class imbalance?
A: Resampling (over/under), SMOTE, class weights, anomaly detection methods.

Q: What is SMOTE?
A: Synthetic Minority Over-sampling Technique — generates synthetic minority examples.

Q: What is linear regression?
A: A model predicting a continuous target as a linear combination of features.

Q: What is logistic regression?
A: A model predicting class probabilities using the logistic function.

Q: What is a decision tree?
A: A tree-structured model splitting data on features for predictions.

Q: What is information gain?
A: The reduction in entropy from a split, used in decision trees.

Q: What is entropy?
A: A measure of impurity or randomness in a dataset.

Q: What is Gini impurity?
A: A measure of impurity used in CART decision trees.

Q: What is a random forest?
A: An ensemble of decision trees with bagging.

Q: What is bagging?
A: Bootstrap Aggregating — training models on bootstrap samples and averaging.

Q: What is boosting?
A: Sequentially training models, each correcting errors of the previous.

Q: What is gradient boosting?
A: Boosting that fits new models to residuals using gradient descent.

Q: What is XGBoost?
A: Extreme Gradient Boosting — a highly efficient implementation.

Q: What is LightGBM?
A: A fast gradient boosting framework by Microsoft.

Q: What is CatBoost?
A: A gradient boosting library handling categorical features natively.

Q: What is k-nearest neighbors (KNN)?
A: A non-parametric algorithm classifying based on closest training examples.

Q: What is naive Bayes?
A: A probabilistic classifier assuming feature independence.

Q: What is SVM?
A: Support Vector Machine — finds optimal separating hyperplane.

Q: What is the kernel trick (SVM)?
A: Implicitly mapping data to higher dimensions to find linear separation.

Q: What is k-means clustering?
A: Partitioning data into k clusters by minimizing within-cluster variance.

Q: What is hierarchical clustering?
A: Building nested clusters via agglomerative or divisive approaches.

Q: What is DBSCAN?
A: Density-Based Spatial Clustering — finds clusters by density.

Q: What is the elbow method?
A: A heuristic to choose the number of clusters by plotting cost vs k.

Q: What is silhouette score?
A: A metric for cluster quality measuring cohesion and separation.

Q: What is PCA?
A: Principal Component Analysis — linear dimensionality reduction by variance.

Q: What is t-SNE?
A: t-distributed Stochastic Neighbor Embedding — nonlinear visualization technique.

Q: What is UMAP?
A: Uniform Manifold Approximation and Projection — fast, scalable dimensionality reduction.

Q: What is a neural network?
A: A model composed of layers of interconnected nodes (neurons).

Q: What is a perceptron?
A: A single-layer neural network unit performing linear classification.

Q: What is a multilayer perceptron (MLP)?
A: A feedforward neural network with one or more hidden layers.

Q: What is an activation function?
A: A function applied to a neuron's output, e.g., ReLU, sigmoid, tanh.

Q: What is ReLU?
A: Rectified Linear Unit — max(0, x); a common activation function.

Q: What is the sigmoid function?
A: 1 / (1 + e^-x); squashes output to (0, 1).

Q: What is softmax?
A: Converts logits to a probability distribution.

Q: What is backpropagation?
A: An algorithm computing gradients for neural network training.

Q: What is a convolutional neural network (CNN)?
A: A neural network for grid-like data (images), using convolutional layers.

Q: What is a recurrent neural network (RNN)?
A: A network for sequential data with hidden state passed between steps.

Q: What is LSTM?
A: Long Short-Term Memory — an RNN handling long dependencies.

Q: What is GRU?
A: Gated Recurrent Unit — a simpler variant of LSTM.

Q: What is a transformer?
A: An architecture using self-attention, foundation of modern NLP models.

Q: What is self-attention?
A: A mechanism letting each input position attend to all others.

Q: What is BERT?
A: Bidirectional Encoder Representations from Transformers — language model.

Q: What is GPT?
A: Generative Pretrained Transformer — autoregressive language model.

Q: What is transfer learning?
A: Reusing a model trained on one task for another related task.

Q: What is fine-tuning?
A: Continuing training of a pretrained model on task-specific data.

Q: What is data augmentation?
A: Generating new training examples by transforming existing ones.

Q: What is dropout?
A: A regularization technique randomly disabling neurons during training.

Q: What is batch normalization?
A: Normalizing inputs to a layer to stabilize and accelerate training.

Q: What is early stopping?
A: Halting training when validation performance stops improving.

Q: What is hyperparameter tuning?
A: Searching for the best model configuration.

Q: What is grid search?
A: Exhaustively trying combinations of hyperparameters.

Q: What is random search?
A: Sampling hyperparameter combinations randomly.

Q: What is Bayesian optimization?
A: Using probabilistic models to choose hyperparameters efficiently.

Q: What is a hyperparameter?
A: A configuration set before training (e.g., learning rate), not learned from data.

Q: What is a parameter (model)?
A: A value learned from data (e.g., weights).

Q: What is Python's role in data science?
A: The dominant language with libraries like NumPy, pandas, scikit-learn, TensorFlow.

Q: What is NumPy?
A: A Python library for numerical computing with N-dimensional arrays.

Q: What is pandas?
A: A Python library for data manipulation and analysis with DataFrames.

Q: What is scikit-learn?
A: A Python library for classical machine learning.

Q: What is matplotlib?
A: A Python plotting library.

Q: What is seaborn?
A: A statistical visualization library built on matplotlib.

Q: What is Plotly?
A: An interactive plotting library.

Q: What is Jupyter Notebook?
A: A web app for interactive code, visualizations, and narrative.

Q: What is R's role in data science?
A: A statistical programming language popular in academia and finance.

Q: What is SQL's role?
A: Querying and manipulating relational databases.

Q: What is a join (SQL)?
A: Combining rows from multiple tables based on related columns.

Q: What is GROUP BY?
A: SQL clause grouping rows for aggregate functions.

Q: What is a window function?
A: SQL function performing calculations across related rows.

Q: What is ETL?
A: Extract, Transform, Load — moving and shaping data.

Q: What is ELT?
A: Extract, Load, Transform — modern variant doing transformation in the warehouse.

Q: What is a data warehouse?
A: A central repository for analytical data, optimized for read.

Q: What is a data lake?
A: A storage system for raw, unstructured, and structured data at scale.

Q: What is a data lakehouse?
A: A hybrid combining lake and warehouse paradigms (Delta, Iceberg).

Q: What is Apache Spark?
A: A distributed computing engine for large-scale data processing.

Q: What is Apache Kafka?
A: A distributed streaming platform for real-time data.

Q: What is Apache Airflow?
A: A workflow orchestration platform.

Q: What is dbt?
A: Data Build Tool — transforms data in warehouses with SQL and Jinja.

Q: What is Snowflake?
A: A cloud data warehouse with separated storage and compute.

Q: What is BigQuery?
A: Google's serverless data warehouse.

Q: What is Databricks?
A: A unified analytics platform built on Spark.

Q: What is feature store?
A: A centralized repo of curated, versioned features for ML.

Q: What is MLOps?
A: Operationalizing machine learning: deployment, monitoring, retraining.

Q: What is model drift?
A: Degradation of model performance over time due to data changes.

Q: What is data drift?
A: Changes in the distribution of input data over time.

Q: What is concept drift?
A: Changes in the relationship between inputs and outputs.

Q: What is A/B testing?
A: Comparing two versions of a treatment to measure causal impact.

Q: What is hypothesis testing?
A: Statistical procedure to evaluate a claim about a population.

Q: What is a p-value?
A: Probability of observing data at least as extreme assuming null hypothesis is true.

Q: What is statistical significance?
A: When observed effects are unlikely to be due to chance under H0.

Q: What is correlation?
A: A statistical measure of linear association between variables.

Q: What is causation?
A: A directional relationship where one variable influences another.

Q: What is confounding?
A: A variable affecting both predictor and outcome, biasing the relationship.

Q: What is Simpson's paradox?
A: A trend that reverses when groups are aggregated.

Q: What is bootstrapping (statistics)?
A: Resampling with replacement to estimate sampling distributions.

Q: What is a confidence interval?
A: A range likely to contain the true parameter with stated confidence.

Q: What is a Bayesian approach?
A: Using prior beliefs updated with data via Bayes' theorem.

Q: What is a frequentist approach?
A: Interpreting probability as long-run frequency.

Q: What is a model's bias?
A: Systematic error from oversimplifying assumptions.

Q: What is a model's variance?
A: Error from sensitivity to small fluctuations in training data.

Q: What is model interpretability?
A: The degree to which a human can understand a model's decisions.

Q: What is SHAP?
A: SHapley Additive exPlanations — explains model predictions via game theory.

Q: What is LIME?
A: Local Interpretable Model-agnostic Explanations — explains individual predictions.

Q: What is permutation importance?
A: Importance assessed by shuffling a feature and measuring drop in performance.

Q: What is fairness in ML?
A: Ensuring models don't systematically disadvantage protected groups.

Q: What is bias in data?
A: Systematic skew in data leading to unfair models.

Q: What is demographic parity?
A: A fairness criterion: similar outcome rates across groups.

Q: What is equalized odds?
A: A fairness criterion: equal true positive and false positive rates across groups.

Q: What is differential privacy?
A: A formal guarantee that an individual's data doesn't significantly affect output.

Q: What is federated learning?
A: Training models on decentralized data without centralizing it.

Q: What is data versioning?
A: Tracking changes to datasets like code versioning.

Q: What is DVC?
A: Data Version Control — git-like tracking for data and models.

Q: What is MLflow?
A: An open-source platform for ML lifecycle management.

Q: What is a feature pipeline?
A: A reusable workflow to compute features for training and inference.

Q: What is online inference?
A: Serving predictions in real-time, often via API.

Q: What is batch inference?
A: Generating predictions on bulk data at scheduled intervals.

Q: What is model serving?
A: Deploying a model to make predictions available.

Q: What is TensorFlow Serving?
A: A model serving system optimized for TensorFlow models.

Q: What is Triton Inference Server?
A: NVIDIA's high-performance model serving framework.

Q: What is ONNX?
A: Open Neural Network Exchange — interchange format for models.

Q: What is data quality?
A: The fitness of data for its intended use.

Q: What are dimensions of data quality?
A: Accuracy, completeness, consistency, timeliness, validity, uniqueness.

Q: What is data governance?
A: Policies and processes for managing data assets.

Q: What is a data catalog?
A: A metadata inventory of data assets in an organization.

Q: What is data lineage?
A: Tracking the origin and transformations of data.

Q: What is a data dictionary?
A: A document defining data fields and their meanings.

Q: What is a schema?
A: The structure of a database or dataset, including columns and types.

Q: What is normalization (database)?
A: Organizing tables to reduce redundancy.

Q: What is denormalization?
A: Combining tables for query performance, accepting redundancy.

Q: What is OLAP?
A: Online Analytical Processing — multi-dimensional analytical queries.

Q: What is OLTP?
A: Online Transaction Processing — high-volume, short transactions.

Q: What is a star schema?
A: A data warehouse design with fact and dimension tables.

Q: What is a snowflake schema?
A: A normalized variant of star schema.

Q: What is a fact table?
A: A table storing measurable events in a data warehouse.

Q: What is a dimension table?
A: A table providing descriptive context for facts.

Q: What is slowly changing dimension (SCD)?
A: Methods for tracking changes in dimension data over time.

Q: What is a Type 2 SCD?
A: Maintaining history by adding new rows for changes.

Q: What is incremental loading?
A: Loading only new or changed data.

Q: What is data partitioning?
A: Dividing data into smaller pieces for performance.

Q: What is sharding?
A: Distributing data across multiple databases.

Q: What is a primary key?
A: A unique identifier for each row in a table.

Q: What is a foreign key?
A: A column linking to a primary key in another table.

Q: What is data quality monitoring?
A: Continuously checking data for anomalies and issues.

Q: What is Great Expectations?
A: An open-source data validation framework.

Q: What is observability?
A: The ability to understand a system's internal state from external outputs.

Q: What is a dashboard?
A: A visualization of key metrics for monitoring or analysis.

Q: What is Tableau?
A: A widely used data visualization tool.

Q: What is Power BI?
A: Microsoft's business intelligence platform.

Q: What is Looker?
A: A BI platform with semantic modeling layer.

Q: What is Streamlit?
A: A Python library for quickly building data apps.

Q: What is Gradio?
A: A library for creating ML model demos as web apps.

Q: What is a recommendation system?
A: A model suggesting items users may like.

Q: What is collaborative filtering?
A: Recommendations based on user-user or item-item similarity.

Q: What is content-based filtering?
A: Recommendations based on item attributes.

Q: What is NLP?
A: Natural Language Processing — analyzing and generating text.

Q: What is tokenization?
A: Splitting text into smaller units (words, subwords, characters).

Q: What is stemming?
A: Reducing words to their root form (e.g., "running" → "run").

Q: What is lemmatization?
A: Reducing words to dictionary form considering context.

Q: What are stop words?
A: Common words filtered out (e.g., "the", "is").

Q: What is TF-IDF?
A: Term Frequency-Inverse Document Frequency — weights words by importance.

Q: What is a word embedding?
A: A dense vector representation of a word capturing semantics.

Q: What is Word2Vec?
A: An algorithm producing word embeddings from co-occurrence.

Q: What is GloVe?
A: Global Vectors for word representation, based on co-occurrence statistics.

Q: What is computer vision?
A: Enabling machines to interpret visual information.

Q: What is image classification?
A: Assigning labels to images.

Q: What is object detection?
A: Locating and classifying objects within images.

Q: What is image segmentation?
A: Partitioning images into meaningful regions.

Q: What is the Hadoop ecosystem?
A: A framework for distributed storage and processing of large datasets.

Q: What is HDFS?
A: Hadoop Distributed File System — fault-tolerant storage layer.

Q: What is MapReduce?
A: A programming model for processing large datasets in parallel.

Q: What is the curse of dimensionality?
A: Difficulties (sparsity, distance loss) when working in high-dimensional space.

Q: What is exploratory vs confirmatory analysis?
A: Exploratory generates hypotheses; confirmatory tests them.

Q: What is a data scientist vs data engineer?
A: Scientists analyze and model; engineers build pipelines and infrastructure.

Q: What is a data analyst?
A: A role focused on reporting and BI, often with SQL and visualization tools.

Q: What is a Kaggle competition?
A: A platform for data science contests using shared datasets.

Q: What is the importance of communicating results?
A: Insights only matter if stakeholders understand and can act on them.

Q: What is storytelling with data?
A: Crafting narratives around data to influence decisions.

Q: What is ethical AI?
A: Designing AI systems aligning with human values and avoiding harm.

Q: What is Goodhart's Law in ML?
A: "When a measure becomes a target, it ceases to be a good measure."