Glossary
Glossary of Terms for Introduction to Data Science with Python
A–C
Accuracy
A metric used to evaluate classification models, representing the proportion of correct predictions over the total number of predictions.
Example: In a spam email classifier, if the model correctly identifies 90 out of 100 emails, the accuracy is 90%.
Algorithm
A step-by-step procedure or set of rules designed to perform a specific task or solve a problem.
Example: Implementing the k-means clustering algorithm to group similar data points in an unsupervised learning task.
Anaconda
A free and open-source distribution of Python and R programming languages for scientific computing and data science.
Example: Using Anaconda to manage Python packages and environments for data analysis projects in the course.
Analytics
The process of examining datasets to draw conclusions about the information they contain, often using specialized software and statistical techniques.
Example: Performing customer behavior analytics using Pandas and Matplotlib to improve marketing strategies.
Anomaly Detection
The process of identifying unusual patterns or observations in data that do not conform to expected behavior.
Example: Detecting fraudulent credit card transactions using an isolation forest model.
API (Application Programming Interface)
A set of functions and protocols that allows software applications to communicate with each other.
Example: Using the OpenWeather API to import real-time temperature data into a Python program.
Application Layer
The highest level in a computing system that interacts directly with user-facing applications and services.
Example: A Python script running in Jupyter Notebook is executed at the application layer.
Area Under the Curve (AUC)
A performance metric for classification models that measures the ability to distinguish between classes, calculated from the ROC curve.
Example: A model with an AUC of 0.95 is better at distinguishing positive from negative cases than one with 0.70.
Array
A data structure that stores a collection of items, typically of the same type, at contiguous memory locations.
Example: Using NumPy arrays for fast vectorized mathematical computations.
Artificial Intelligence (AI)
The field of computer science focused on creating systems capable of performing tasks that normally require human intelligence.
Example: Building a neural network to recognize handwritten digits.
Association Rule Mining
A method for finding relationships between variables in large datasets.
Example: Identifying that customers who buy bread often also purchase butter.
Attribute
A property or characteristic of an object, often represented as a column in a dataset.
Example: In a dataset of houses, "number of bedrooms" is an attribute.
Autocorrelation
A measure of how related a variable is with a lagged version of itself over time.
Example: Detecting repeating seasonal patterns in monthly sales data.
Bagging (Bootstrap Aggregating)
A machine learning ensemble technique that combines predictions from multiple models trained on different subsets of the data.
Example: Using bagging with decision trees to create a random forest classifier.
Bar Chart
A visualization that uses rectangular bars to represent data values for different categories.
Example: Displaying average exam scores for different majors in a bar chart.
Baseline Model
A simple model used as a reference point for comparing the performance of more complex models.
Example: Using the mean value of the target variable as predictions to compare against a regression model.
Batch Processing
A method of processing large volumes of data in groups instead of in real time.
Example: Running a nightly script to clean and aggregate all transaction data from the previous day.
Bayes’ Theorem
A formula used to update probabilities based on new evidence.
Example: Updating the probability of having a disease after a positive test result.
Bias (in Machine Learning)
The error introduced when a model makes assumptions that oversimplify the problem, potentially leading to underfitting.
Example: Using a linear model for a highly non-linear dataset can introduce high bias.
Bias-Variance Tradeoff
The balance between underfitting (high bias) and overfitting (high variance) when building predictive models.
Example: Choosing a model complexity that minimizes both bias and variance.
Big Data
Extremely large datasets that require specialized storage and processing methods.
Example: Analyzing petabytes of social media data for sentiment analysis.
Bin
A grouping of continuous numerical values into intervals for analysis or visualization.
Example: Grouping ages into bins like 0–18, 19–35, and 36–60 for analysis.
Binary Classification
A classification problem with only two possible outcomes.
Example: Predicting whether an email is spam or not spam.
Binary Variable
A variable with only two possible values, often represented as 0 and 1.
Example: "Passed Exam" variable coded as 1 for pass and 0 for fail.
Box Plot
A graphical summary of data distribution showing median, quartiles, and potential outliers.
Example: Using a box plot to compare exam scores across different classes.
Categorical Data
Data that represents discrete groups or categories.
Example: "Car color" with values red, blue, or green.
Causal Relationship
A relationship where a change in one variable directly produces a change in another.
Example: Increasing advertising budget directly increasing sales.
Central Limit Theorem (CLT)
A statistical theory stating that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases.
Example: Using the CLT to justify using normal-based confidence intervals.
Class (in Classification)
A label assigned to a group of similar data points in a classification task.
Example: "Cat" and "Dog" are two classes in an animal image classifier.
Classification
The process of predicting a categorical label for new observations based on training data.
Example: Using logistic regression to classify emails as spam or not spam.
Classification Report
A summary of a classification model’s performance, including precision, recall, and F1-score.
Example: Generating a classification report in scikit-learn after model training.
Clustering
An unsupervised learning method that groups data points into clusters based on similarity.
Example: Segmenting customers into groups based on purchasing habits.
Coefficient
A numerical value representing the strength and direction of the relationship between a predictor and the response variable in a regression model.
Example: In a linear regression, a coefficient of 2.5 for "hours studied" means each extra hour increases predicted score by 2.5 points.
Collinearity
A situation where two or more predictor variables are highly correlated, which can affect model stability.
Example: Including both height in inches and height in centimeters in a regression model creates collinearity.
Column
A vertical arrangement of data values in a table, representing a single attribute.
Example: In a Pandas DataFrame, each column holds one variable’s data.
Confusion Matrix
A table used to evaluate classification performance by showing counts of true positives, false positives, true negatives, and false negatives.
Example: A confusion matrix showing that a model misclassified 5 out of 100 test emails.
Correlation
A statistical measure that describes the strength and direction of a linear relationship between two variables.
Example: A correlation coefficient of 0.9 between height and weight indicates a strong positive relationship.
Correlation Coefficient
A numerical value between -1 and 1 that indicates the degree of linear association between two variables.
Example: A value of -0.75 shows a strong negative correlation.
Cross-Validation
A technique for assessing how well a model generalizes by dividing data into multiple training and testing subsets.
Example: Using 5-fold cross-validation to evaluate a regression model’s accuracy.
CSV (Comma-Separated Values)
A simple file format for storing tabular data where each line represents a row and values are separated by commas.
Example: Importing a CSV file of sales data into Pandas for analysis.
D–H
Data Cleaning
The process of detecting and correcting inaccurate, incomplete, or irrelevant data in a dataset.
Example: Removing duplicate rows and correcting misspelled city names in a dataset.
Data Exploration
The initial phase of data analysis where patterns, trends, and relationships are identified through visualization and statistics.
Example: Using Pandas and Matplotlib to examine sales trends by month.
Data Frame
A two-dimensional labeled data structure in Pandas that can hold different data types in columns.
Example: Loading a CSV file into a Pandas DataFrame for manipulation.
Data Mining
The process of discovering patterns, correlations, and insights from large datasets using statistical and computational techniques.
Example: Finding product purchase patterns from transaction logs.
Data Normalization
Adjusting values in a dataset to a common scale without distorting differences in ranges.
Example: Scaling features between 0 and 1 before training a machine learning model.
Data Preprocessing
Transforming raw data into a clean and usable format for analysis or modeling.
Example: Encoding categorical variables as integers before running a regression.
Data Science
An interdisciplinary field that combines statistics, programming, and domain knowledge to extract insights from data.
Example: Using Python to analyze and visualize health survey data.
Data Type
A classification specifying the kind of value a variable can hold, such as integer, float, or string.
Example: In Python, 3 is an integer, and 3.14 is a float.
Dataset
A structured collection of data, typically organized in rows and columns.
Example: The Iris dataset is commonly used for classification demonstrations.
Decision Tree
A model that makes decisions by splitting data into branches based on feature values.
Example: Predicting loan approval using a decision tree classifier.
Deep Learning
A subfield of machine learning that uses multi-layered neural networks to model complex patterns.
Example: Training a convolutional neural network to recognize handwritten digits.
Dependent Variable
The outcome variable being predicted or explained in a model.
Example: Predicting "house price" based on features like size and location.
Descriptive Statistics
Statistical methods for summarizing and describing data features.
Example: Calculating mean, median, and standard deviation for exam scores.
Dimension Reduction
Techniques for reducing the number of variables while preserving relevant information.
Example: Using Principal Component Analysis (PCA) to reduce feature count before clustering.
Discrete Data
Data that can only take on specific, distinct values.
Example: Number of students in a classroom.
Distribution
The way values of a variable are spread or arranged.
Example: A bell-shaped distribution for students’ test scores.
Dummy Variable
A binary variable created to represent categorical data for modeling purposes.
Example: Encoding "Gender" as 0 for male and 1 for female.
EDA (Exploratory Data Analysis)
The process of visually and statistically summarizing a dataset to uncover patterns and insights.
Example: Plotting histograms and scatter plots to explore data before modeling.
Epoch
One complete pass through the entire training dataset in machine learning.
Example: Training a neural network for 50 epochs to achieve better accuracy.
Evaluation Metric
A standard of measurement used to assess model performance.
Example: Mean Squared Error (MSE) for regression models.
Evidence-Based Decision
A decision guided by analysis and data rather than intuition alone.
Example: Allocating marketing budget based on data-driven campaign performance.
Explanatory Variable
A variable used to predict or explain the dependent variable.
Example: In predicting crop yield, rainfall amount is an explanatory variable.
Exponential Smoothing
A forecasting method that applies decreasing weights to older observations.
Example: Forecasting monthly sales using exponential smoothing.
Feature
An individual measurable property of the data used in modeling.
Example: "Age" and "Income" as features in predicting spending habits.
Feature Engineering
The process of creating new features or transforming existing ones to improve model performance.
Example: Creating a "BMI" feature from height and weight columns.
Feature Scaling
Adjusting feature values to the same scale to ensure fair model training.
Example: Standardizing all features to have zero mean and unit variance.
Feature Selection
Choosing the most relevant features to improve model efficiency and accuracy.
Example: Removing features with low correlation to the target variable.
F1-Score
A metric that combines precision and recall into a single value.
Example: An F1-score of 0.85 indicates a balanced model performance.
False Negative
A prediction that incorrectly classifies a positive case as negative.
Example: A medical test failing to detect a disease when it is present.
False Positive
A prediction that incorrectly classifies a negative case as positive.
Example: A spam filter marking a legitimate email as spam.
Feature Matrix
A two-dimensional array containing feature values for all observations in a dataset.
Example: A NumPy array where each row is a sample and each column is a feature.
Forecasting
Predicting future values based on historical data.
Example: Using time series models to forecast next month’s sales.
Function (Python)
A reusable block of code that performs a specific task.
Example: Defining a function to calculate the mean of a list.
Gaussian Distribution
A symmetric, bell-shaped probability distribution characterized by mean and standard deviation.
Example: Heights of adult men following a Gaussian distribution.
Generalization
The ability of a model to perform well on new, unseen data.
Example: A model that predicts accurately on both training and test datasets generalizes well.
Gradient Descent
An optimization algorithm used to minimize a function by iteratively moving toward the lowest point.
Example: Training linear regression by adjusting weights with gradient descent.
Ground Truth
The actual, verified values used to evaluate predictions.
Example: Comparing model predictions to actual customer purchase data.
GroupBy (Pandas)
A Pandas method for splitting data into groups for aggregation or transformation.
Example: Grouping sales data by region and summing total revenue.
Heatmap
A graphical representation of data values using color shading.
Example: A correlation heatmap showing relationships between features.
Heuristic
A rule-of-thumb strategy for solving problems quickly, though not always optimally.
Example: Using a decision tree depth limit of 5 as a heuristic for speed.
Histogram
A plot that groups numerical data into intervals (bins) and shows their frequency.
Example: Plotting the distribution of exam scores in bins of 10 points.
Hyperparameter
A parameter set before training that controls model behavior.
Example: Setting the learning rate in gradient descent.
I–M
Imbalanced Dataset
A dataset where some classes have significantly more samples than others.
Example: A fraud detection dataset with 99% non-fraud and 1% fraud cases.
Imputation
The process of replacing missing data with substituted values.
Example: Filling missing ages with the mean age of the dataset.
Independent Variable
A variable used as an input to predict another variable.
Example: Study time as an independent variable in predicting test scores.
Index (Pandas)
Labels that identify rows in a Pandas DataFrame or Series.
Example: Setting the "date" column as the index for a time series.
Inferential Statistics
Statistical methods for making predictions or inferences about a population from a sample.
Example: Using a sample of voters to predict an election outcome.
Information Gain
A measure of how much a feature improves classification by reducing uncertainty.
Example: A decision tree choosing a feature with the highest information gain for the first split.
Interpolation
Estimating unknown values within the range of known data points.
Example: Estimating missing temperature readings between two known days.
Iteration
A single pass through a loop or process in programming.
Example: Each pass through a for loop is an iteration.
Jupyter Notebook
An interactive environment for writing and running Python code, combining code, visualizations, and text.
Example: Running data analysis and visualizations in a Jupyter Notebook.
JSON (JavaScript Object Notation)
A lightweight data format for storing and exchanging structured data.
Example: Importing JSON-formatted API data into Pandas.
k-Fold Cross-Validation
A method of splitting data into k subsets for training and testing to evaluate model performance.
Example: Using 10-fold cross-validation to assess a regression model.
k-Means Clustering
An unsupervised learning algorithm that groups data into k clusters based on similarity.
Example: Segmenting customers into 4 clusters using purchase history.
Kernel (in Computing)
The central component of an operating system or computational environment.
Example: Restarting the Python kernel in Jupyter Notebook to clear variables.
Keyword Argument (Python)
An argument passed to a function by explicitly naming the parameter.
Example: Calling plot(x, y, color="blue") uses a keyword argument.
Kolmogorov-Smirnov Test
A statistical test to compare a sample distribution with a reference probability distribution.
Example: Checking if a dataset follows a normal distribution.
Label (in Machine Learning)
The output or target variable in supervised learning.
Example: "Species" in the Iris dataset is the label.
Label Encoding
Converting categorical values into numeric codes.
Example: Encoding "low", "medium", "high" as 0, 1, 2.
Lasso Regression
A linear regression method with L1 regularization that can reduce coefficients to zero.
Example: Using Lasso to perform feature selection in a high-dimensional dataset.
Learning Rate
A hyperparameter controlling how much to adjust model weights during training.
Example: Setting a learning rate of 0.01 for gradient descent.
Least Squares Method
A method for estimating regression coefficients by minimizing the sum of squared residuals.
Example: Fitting a line to data points using least squares.
Legend (in Visualization)
An element in a chart that explains the meaning of colors, symbols, or line styles.
Example: A legend identifying which line corresponds to each city in a temperature chart.
Library (Python)
A collection of modules that provide specific functionality.
Example: NumPy is a library for numerical computing.
Linear Regression
A method for modeling the relationship between a dependent variable and one or more independent variables using a linear equation.
Example: Predicting house prices from square footage.
Logistic Regression
A statistical model used for binary classification that predicts probabilities using a logistic function.
Example: Predicting whether a student passes an exam based on study hours.
Loss Function
A function that measures the error between predicted and actual values.
Example: Mean Squared Error is a loss function for regression.
Machine Learning
A subset of AI where models improve their performance by learning from data.
Example: Training a decision tree to classify loan applications.
MAE (Mean Absolute Error)
A regression metric measuring the average absolute difference between predicted and actual values.
Example: An MAE of 5 means predictions are off by 5 units on average.
Map Function (Python)
A function that applies another function to each item in an iterable.
Example: Using map(str.upper, words) to convert all words to uppercase.
Matplotlib
A Python library for creating static, animated, and interactive visualizations.
Example: Plotting a line chart of sales over time using Matplotlib.
Matrix
A two-dimensional array of numbers arranged in rows and columns.
Example: A 3×3 NumPy array representing a matrix.
Mean
The average value of a dataset, found by summing all values and dividing by the count.
Example: The mean of 2, 4, and 6 is 4.
Mean Squared Error (MSE)
A regression metric that measures the average squared difference between predicted and actual values.
Example: An MSE of 9 means the average squared error is 9 units.
Median
The middle value when data is ordered from smallest to largest.
Example: The median of 1, 3, 5 is 3.
MicroSimulation
An interactive simulation that runs in your browser that helps you learn a data science concept.
Example: You can visualize the least squares method of liner regression by changing the slope and intercept of a line through a series of points.
Min-Max Scaling
A feature scaling technique that rescales values to a given range, usually [0, 1].
Example: Converting exam scores from a 0–100 scale to 0–1.
Missing Data
Values not recorded in a dataset.
Example: A blank entry for "age" in a survey response.
Mode
The most frequently occurring value in a dataset.
Example: In 2, 3, 3, 4, 5, the mode is 3.
Example: Creating a scikit-learn pipeline to standardize data and train a model in a single workflow.
A mathematical representation of a process or system built from data to make predictions or decisions.
Example: A linear regression equation predicting house prices.
Model Complexity
The level of detail and number of parameters in a model.
Example: A deep neural network is more complex than a simple linear regression.
Model Evaluation
The process of assessing a model’s performance using metrics and validation techniques.
Example: Evaluating a classification model with precision and recall.
Model Overfitting
When a model learns noise in the training data, reducing performance on new data.
Example: A decision tree that memorizes training examples but fails on test data.
Model Underfitting
When a model is too simple to capture the underlying structure of the data.
Example: Using a straight line to fit highly curved data.
Module (Python)
A file containing Python definitions and functions.
Example: Importing the math module to use mathematical functions.
Multicollinearity
A situation where predictor variables in a regression model are highly correlated with each other.
Example: Including both temperature in Celsius and Fahrenheit in the same model.
Multiple Linear Regression
A linear regression model with two or more independent variables.
Example: Predicting house price using square footage, number of bedrooms, and location.
N–S
Naive Bayes
A probabilistic classification algorithm based on Bayes’ theorem with the assumption of feature independence.
Example: Classifying text documents into categories using Naive Bayes.
Natural Language Processing (NLP)
A field of AI focused on enabling computers to understand and process human language.
Example: Using NLP to analyze sentiment in customer reviews.
Neural Network
A computational model inspired by the human brain, consisting of interconnected nodes (neurons) organized in layers.
Example: A neural network classifying handwritten digits.
Noise (Data)
Random variations or irrelevant information in data that obscure patterns.
Example: Sensor measurement errors in temperature readings.
Nominal Data
Categorical data without an inherent order.
Example: Car brands such as Toyota, Ford, and Honda.
Normal Distribution
A symmetric, bell-shaped probability distribution characterized by mean and standard deviation.
Example: Adult human heights often approximate a normal distribution.
Normalization
The process of scaling numerical data to a standard range or format.
Example: Scaling all features to a 0–1 range before training.
Null Hypothesis
A default statistical assumption that there is no effect or relationship between variables.
Example: Testing whether two teaching methods result in the same average test score.
NumPy
A Python library for numerical computing, providing array objects and mathematical functions.
Example: Performing matrix multiplication using NumPy arrays.
Observation
A single record or row in a dataset.
Example: One student's exam scores in a class dataset.
One-Hot Encoding
A method of converting categorical variables into binary indicator variables.
Example: Encoding "red", "blue", "green" into three separate binary columns.
Open Source Software
Software with source code made freely available for modification and distribution.
Example: Pandas is open source.
Optimization
The process of adjusting model parameters to minimize or maximize an objective function.
Example: Using gradient descent to minimize prediction error.
Outlier
A data point significantly different from other observations.
Example: A monthly sales value 10 times higher than all others.
Overfitting
When a model fits training data too closely, capturing noise rather than general patterns.
Example: A polynomial regression curve that perfectly passes through all training points but fails on test data.
Overfitting Prevention
Techniques used to reduce overfitting, such as regularization, pruning, and cross-validation.
Example: Using Lasso regression to reduce unnecessary model complexity.
p-Value
A statistical measure of the probability of obtaining results at least as extreme as the observed results, assuming the null hypothesis is true.
Example: A p-value of 0.03 suggests statistical significance at the 5% level.
Pandas
A Python library for data manipulation and analysis with DataFrame and Series objects.
Example: Loading and cleaning a CSV dataset with Pandas.
Parameter (Model)
A value learned from data that defines how the model makes predictions.
Example: The slope in a linear regression equation.
Parameter Tuning
Adjusting model hyperparameters to optimize performance.
Example: Testing multiple learning rates to find the best one.
Parsing
Breaking down data or text into structured components for processing.
Example: Parsing JSON data from an API response.
Pearson Correlation Coefficient
A statistic measuring the linear relationship between two variables.
Example: A Pearson coefficient of 0.8 indicates a strong positive relationship.
Performance Metric
A quantitative measure used to evaluate the effectiveness of a model.
Example: Accuracy, precision, and recall are performance metrics for classification.
Pipeline (Machine Learning)
A sequence of data processing and modeling steps applied consistently to training and test data.
Example: A pipeline for scaling features and then applying logistic regression.
Pivot Table
A table that summarizes data by grouping and aggregating values.
Example: Summarizing sales by region and month using a Pandas pivot table.
Polynomial Regression
A regression method where the relationship between variables is modeled as an nth-degree polynomial.
Example: Fitting a quadratic curve to non-linear data.
Population
The entire set of items or individuals of interest in a study.
Example: All registered voters in a country.
Precision
The proportion of positive predictions that are actually correct.
Example: A precision of 0.9 means 90% of predicted positives were correct.
Predictive Modeling
The process of creating models to forecast outcomes based on historical data.
Example: Using past sales data to predict next month’s revenue.
Predictor Variable
An independent variable used to forecast the dependent variable.
Example: Advertising spend as a predictor of sales.
Preprocessing
Preparing raw data for analysis or modeling through cleaning, transformation, and encoding.
Example: Removing missing values and scaling features.
Pre-Trained Model
A model that has been previously trained on a large dataset and can be fine-tuned for a new task.
Example: Using a pre-trained image classifier for a smaller dataset.
Probability
A measure of the likelihood that an event will occur, ranging from 0 to 1.
Example: A probability of 0.75 means a 75% chance of occurrence.
Probability Distribution
A function that describes the likelihood of all possible outcomes.
Example: A uniform distribution gives equal probability to all values.
PyTorch
An open-source machine learning library for deep learning and tensor computation.
Example: Building a neural network in PyTorch for image recognition.
Quantile
A cut point dividing data into equal-sized intervals.
Example: The median is the 0.5 quantile.
Quartile
One of three points that divide data into four equal groups.
Example: The first quartile is the 25th percentile.
Query (Database)
A request for specific information from a database.
Example: Using SQL to retrieve rows where sales exceed \$500.
R² (Coefficient of Determination)
A metric indicating the proportion of variance in the dependent variable explained by the model.
Example: An R² of 0.85 means 85% of the variation is explained by the model.
Random Forest
An ensemble learning method combining multiple decision trees for better performance.
Example: Classifying emails using a random forest model.
Recall
The proportion of actual positives correctly identified by the model.
Example: A recall of 0.8 means the model identified 80% of actual positives.
Recursive Feature Elimination (RFE)
A method for selecting features by recursively removing the least important ones.
Example: Using RFE to reduce feature count before training.
Regression
A statistical method for modeling relationships between variables to predict numerical outcomes.
Example: Predicting car prices from mileage and age.
Regularization
A technique for reducing model complexity to prevent overfitting by adding a penalty term to the loss function.
Example: L2 regularization in Ridge regression.
Residual
The difference between the observed value and the value predicted by a model.
Example: If actual sales are 200 and predicted sales are 180, the residual is 20.
Root Mean Squared Error (RMSE)
A regression metric measuring the square root of the average squared prediction error.
Example: An RMSE of 3 means predictions are off by 3 units on average.
Sample
A subset of a population used for analysis.
Example: Surveying 500 people from a city of 100,000 residents.
Sampling
Selecting a subset of data from a larger dataset or population.
Example: Randomly selecting 100 customers for a feedback survey.
Sampling Bias
A bias introduced when the sample is not representative of the population.
Example: Surveying only morning shoppers to estimate daily spending.
Scatter Plot
A graph showing data points on an x-y axis to visualize relationships between variables.
Example: Plotting height against weight to explore correlation.
Scikit-learn
A Python library for machine learning, providing tools for modeling, preprocessing, and evaluation.
Example: Building a decision tree classifier with Scikit-learn.
Scripting
Writing and executing code to automate tasks.
Example: Writing a Python script to download and clean data files.
Series (Pandas)
A one-dimensional labeled array in Pandas.
Example: A Series of student grades with names as labels.
Standard Deviation
A measure of the dispersion of values around the mean.
Example: A standard deviation of 2 means most values fall within ±2 units of the mean.
Standardization
Scaling features so they have zero mean and unit variance.
Example: Standardizing exam scores before analysis.
T–Z
Target Variable
The variable a model aims to predict.
Example: Predicting "exam score" as the target variable using study hours and sleep.
t-Statistic
A value used in hypothesis testing to determine if sample means differ significantly.
Example: Using a t-statistic to compare average weights of two groups.
Tensor
A multi-dimensional array used in deep learning frameworks like PyTorch.
Example: Representing a batch of images as a 4D tensor.
Test Data
A dataset used to evaluate model performance after training.
Example: Testing a spam filter on previously unseen emails.
Test-Train Split
The process of dividing data into separate training and testing subsets.
Example: Using 80% of data for training and 20% for testing.
Text Mining
Extracting useful information from text data through statistical and computational methods.
Example: Identifying frequent keywords in news articles.
Time Series
A sequence of data points collected over time intervals.
Example: Daily stock prices over a year.
Tokenization
Breaking text into smaller units such as words or phrases.
Example: Splitting "Data Science is fun" into ["Data", "Science", "is", "fun"].
Training Data
The dataset used to fit or train a model.
Example: Using past sales data to train a revenue prediction model.
Training Epoch
One complete pass of the training dataset through the model during learning.
Example: Training a neural network for 10 epochs.
Transformation
Applying a mathematical function to change data format or scale.
Example: Applying a logarithmic transformation to skewed data.
Tree-Based Models
Models that use decision trees or ensembles of trees for prediction.
Example: Random forest and gradient boosting are tree-based models.
Trend (Time Series)
A long-term movement or pattern in time series data.
Example: Increasing monthly sales over several years.
True Negative
A case where a model correctly predicts a negative outcome.
Example: A medical test correctly identifying a healthy patient.
True Positive
A case where a model correctly predicts a positive outcome.
Example: A cancer detection test correctly identifying a patient with cancer.
t-Test
A statistical test comparing the means of two groups.
Example: Testing whether two different diets result in different average weight loss.
Underfitting
When a model is too simple to capture the underlying data patterns.
Example: Using a straight line to predict a highly curved dataset.
Univariate Analysis
Analysis of a single variable to summarize and find patterns.
Example: Plotting a histogram of exam scores.
Unsupervised Learning
A machine learning approach where models find structure in data without labeled outcomes.
Example: Using k-means clustering to group customers.
Validation Data
A dataset used during training to tune model hyperparameters.
Example: Using validation data to choose the number of decision tree splits.
Value Counts (Pandas)
A method to count occurrences of each unique value in a Pandas Series.
Example: Counting how many times each grade appears in a class.
Variable
A storage location identified by a name that holds a value.
Example: In Python, age = 20 assigns 20 to the variable age.
Variance
A measure of the spread of data points around the mean.
Example: A variance of 4 means average squared deviations are 4 units².
Vectorization
Performing operations on entire arrays rather than using loops.
Example: Adding two NumPy arrays without explicit loops.
Visualization
The graphical representation of data to reveal patterns and insights.
Example: Using Matplotlib to plot a scatter diagram of height vs. weight.
Visual Studio Code
A free integrated development environment that is used widely by programmers and data science science professionals.
Visual Studio Code (VS-Code) also has many extensions for editing different languages and data types.
Other examples of AI-based IDEs include Cursor and Windsurf. The Cline extension is also useful.
Example: We use VS-Code to edit, run, debug and test our Python code.
Weighted Average
An average where each value contributes according to its assigned weight.
Example: Calculating a final grade with different weights for assignments and exams.
Weight (Model)
A parameter that determines the influence of a feature in a model’s prediction.
Example: A weight of 0.5 for "hours studied" in a regression model.
White Noise (Time Series)
A random sequence of values with no predictable pattern.
Example: Fluctuations in daily stock prices without a trend.
Wilcoxon Test
A non-parametric test for comparing paired samples.
Example: Comparing pre- and post-training test scores without assuming normality.
YAML (YAML Ain’t Markup Language)
A human-readable data serialization format.
Example: Storing configuration settings in a YAML file.
z-Score
A standardized score indicating how many standard deviations a value is from the mean.
Example: A z-score of 2 means the value is two standard deviations above the mean.
Zero-Inflated Data
Data with an excess number of zero values compared to a standard distribution.
Example: A dataset of monthly purchases where many customers have zero purchases.
Zero-Shot Learning
A machine learning method where a model makes predictions for classes it was not explicitly trained on.
Example: A text classifier identifying a new topic without prior examples.