Google Advanced Data Analytics

Question 1

A way to compare two versions of something to find out which version performs better

Answer 1

A/B testing

Question 2

(Refer to observed values)

Answer 2

Absolute values

Question 3

Refers to the proportion of data points that were correctly categorized

Answer 3

Accuracy

Question 4

A Tableau tool to help an audience interact with a visualization or dashboard by allowing control of selection

Answer 4

Action

Question 5

Refers to allowing team members, bosses, and other collaborative stakeholders to share their own points of view before offering responses

Answer 5

Active listening

Question 6

(Refer to adaptive boosting)

Answer 6

AdaBoost

Question 7

A boosting methodology where each consecutive base learner assigns greater weight to the observations incorrectly predicted by the preceding learner

Answer 7

Adaptive boosting

Question 8

The concept that if the events A and B are mutually exclusive, then the probability of A or B happening is the sum of the probabilities of A and B

Answer 8

Addition rule (for mutually exclusive events):

Question 9

A variation of R² that accounts for having multiple independent variables present in a linear regression model

Answer 9

Adjusted R²

Question 10

The metric used to calculate the distance between points/clusters

Answer 10

Affinity

Question 11

A pandas groupby method that allows the user to apply multiple calculations to groups of data

Answer 11

agg():

Question 12

A clustering methodology that works by first assigning every point to its own cluster, then progressively combining clusters based on intercluster distance

Answer 12

Agglomerative clustering

Question 13

Data from a significant number of users that has eliminated personal information

Answer 13

Aggregate information

Question 14

A set of instructions for solving a problem or accomplishing a task

Answer 14

Algorithm

Question 15

A process that allows the user to assign an alternate name—or alias—to something

Answer 15

Aliasing

Question 16

A group of statistical techniques that test the difference of means between three or more groups

Answer 16

Analysis of Variance (ANOVA)

Question 17

A data professional who supervises analytical strategy for an organization, often managing multiple groups

Answer 17

Analytics Team Manager

Question 18

Stage of the PACE workflow where the necessary data is acquired from primary and secondary sources and then cleaned, reorganized, and analyzed

Answer 18

Analyze stage

Question 19

A statistical technique that tests the difference of means between three or more groups while controlling for the effects of covariates, or variable(s) irrelevant to the test

Answer 19

ANCOVA (Analysis of Covariance)

Question 20

A method that adds an element to the end of a list

Answer 20

append():

Question 21

An ordered collection of items of a single data type

Answer 21

Array:

Question 22

A function for converting input to an array

Answer 22

Array():

Question 23

Information given to a function in its parentheses

Answer 23

Argument

Question 24

Refers to computer systems able to perform tasks that normally require human intelligence

Answer 24

Artificial intelligence (AI)

Question 25

The process of storing a value in a variable

Answer 25

Assignment

Question 26

A value associated with an object or class which is referenced by name using dot notation

Answer 26

Attribute

Question 27

Average: The distance between each cluster’s centroid and other clusters’ centroids

Answer 27

Average

Question 28

A stepwise variable selection process that begins with the full model, with all possible independent variables, and removes the independent variable that adds the least explanatory power to the model

Answer 28

Backward elimination

Question 29

A technique used by certain kinds of models that use ensembles of base learners to make predictions; refers to the combination of bootstrapping and aggregating

Answer 29

Bagging

Question 30

Each individual model that comprises an ensemble

Answer 30

Base learner

Question 31

(Refer to Bayes’ theorem)

Answer 31

Bayes’ rule

Question 32

An equation that can be used to calculate the probability of an outcome or class, given the values of predictor variables

Answer 32

Bayes’ theorem

Question 33

(Refer to Bayesian statistics)

Answer 33

Bayesian inference

Question 34

A powerful method for analyzing and interpreting data in modern data analytics; also referred to as Bayesian inference

Answer 34

Bayesian statistics

Question 35

The line that fits the data best by minimizing some loss function or error

Answer 35

Best fit line

Question 36

In data structuring, refers to organizing data results in groupings, categories, or variables that are misrepresentative of the whole dataset

Answer 36

Bias

Question 37

Balance between two model qualities, bias and variance, to minimize overall error for unobserved data

Answer 37

Bias-variance trade-off

Question 38

A segment of data that groups values into categories

Answer 38

Bin

Question 39

Grouping continuous values into a smaller number of categories, or intervals

Answer 39

Binning

Question 40

A discrete distribution that models the probability of events with only two possible outcomes: success or failure

Answer 40

Binomial distribution

Question 41

A technique that models the probability of an observation falling into one of two categories, based on one or more independent variables

Answer 41

Binomial logistic regression

Question 42

An assumption stating that there should be a linear relationship between each X variable and the logit of the probability that Y equals one

Answer 42

Binomial logistic regression linearity assumption

Question 43

Any model whose predictions cannot be precisely explained

Answer 43

Black-box model

Question 44

A data type that has only two possible values, usually true or false

Answer 44

Boolean

Question 45

A data type that has only two possible values, usually true or false

Answer 45

Boolean data

Question 46

A filtering technique that overlays a Boolean grid onto a dataframe in order to select only the values in the dataframe that align with the True values of the grid

Answer 46

Boolean masking

Question 47

A technique that that builds an ensemble of weak learners sequentially, with each consecutive learner trying to correct the errors of the one that preceded it

Answer 47

Boosting

Question 48

Refers to sampling with replacement

Answer 48

Bootstrapping

Question 49

A data visualization that depicts the locality, spread, and skew of groups of values within quartiles

Answer 49

Box plot

Question 50

The ability of a program to alter its execution sequence

Answer 50

Branching

Question 51

A keyword that lets a user escape a loop without triggering any ELSE statement that follows it in the loop

Answer 51

break

Question 52

(Refer to Business Intelligence Engineer)

Answer 52

Business Intelligence Analyst:

Question 53

A data professional who uses their knowledge of business trends and databases to organize information and make it accessible; also referred to as a Business Intelligence Analyst

Answer 53

Business Intelligence Engineer

Question 54

Data that is divided into a limited number of qualitative groups

Answer 54

Categorical data:

Question 55

Variables that contain a finite number of groups or categories

Answer 55

Categorical variables:

Question 56

Describes a cause-and-effect relationship where one variable directly causes the other to change in a particular way

Answer 56

Causation:

Question 57

The modular code input and output fields into which Jupyter Notebooks are partitioned

Answer 57

Cells:

Question 58

The idea that the sampling distribution of the mean approaches a normal distribution as the sample size increases

Answer 58

Central Limit Theorem:

Question 59

The center of a cluster determined by the mathematical mean of all the points in that cluster

Answer 59

Centroid:

Question 60

A hypothesis test that determines whether an observed categorical variable follows an expected distribution

Answer 60

Chi-squared (χ²) Goodness of Fit Test:

Question 61

A hypothesis test that determines whether or not two categorical variables are associated with each other

Answer 61

Chi-squared (χ²) Test for Independence:

Question 62

An executive-level data professional who is responsible for the consistency, accuracy, relevancy, interpretability, and reliability of the data a team provides

Answer 62

Chief Data Officer:

Question 63

A node that is pointed to from another node

Answer 63

Child node:

Question 64

When a dataset has a predictor variable that contains more instances of one outcome than another

Answer 64

Class imbalance:

Question 65

An object’s data type that bundles data and functionality together

Answer 65

Class:

Question 66

A type of probability based on formal reasoning about events with equally likely outcomes

Answer 66

Classical probability:

Question 67

The process of removing errors that might distort your data or make it less useful; one of the six practices of EDA

Answer 67

Cleaning:

Question 68

A probability sampling method that divides a population into clusters, randomly selects certain clusters, and includes all members from the chosen clusters in the sample

Answer 68

Cluster random sample:

Question 69

A technique used by recommendation systems to make comparisons based on who else liked the content

Answer 69

Collaborative filtering:

Question 70

A group of abnormal points, following similar patterns and isolated from the rest of the population

Answer 70

Collective outliers:

Question 71

An operator that compares two values and produces Boolean values (True/False)

Answer 71

Comparator:

Question 72

In statistics, refers to an event not occuring

Answer 72

Complement of an event:

Question 73

The maximum pairwise distance between clusters

Answer 73

Complete:

Question 74

A concept stating that the probability that event A does not occur is one minus the probability of A

Answer 74

Complement rule:

Question 75

Refers to defining attributes and methods at the instance level to have a more differentiated relationship between objects in the same class

Answer 75

Composition:

Question 76

The process of giving instructions to a computer to perform an action or set of actions

Answer 76

Computer programming:

Question 77

A pandas function that combines data either by adding it horizontally as new columns for existing rows or vertically as new rows for existing columns

Answer 77

concat():

Question 78

To link or join together

Answer 78

Concatenate:

Question 79

Refers to building longer strings out of smaller strings

Answer 79

Concatenation:

Question 80

Refers to the probability of an event occurring given that another event has already occurred

Answer 80

Conditional probability:

Question 80

A section of code that directs the execution of programs

Answer 80

Conditional statement:

Question 81

The area surrounding a line that describes the uncertainty around the predicted outcome at every value of X

Answer 81

Confidence band:

Question 82

A range of values that describes the uncertainty surrounding an estimate

Answer 82

Confidence interval:

Question 83

A measure that expresses the uncertainty of the estimation process

Answer 83

Confidence level:

Question 84

A graphical representation of how accurate a classifier is at predicting the labels for a categorical variable

Answer 84

Confusion matrix:

Question 85

Stage of the PACE workflow where data models and machine learning
algorithms are built, interpreted, and revised to uncover relationships within the data and help unlock insights from those relationships

Answer 85

Construct stage:

Question 86

A special method to add values to an instance in object creation

Answer 86

Constructor:

Question 87

A technique used by recommendation systems to make comparisons based on attributes of content

Answer 87

Content-based filtering:

Question 88

Normal data points under certain conditions but become anomalies under most other conditions

Answer 88

Contextual outliers:

Question 89

A variable that takes all the possible values in some range of numbers

Answer 89

Continuous random variable:

Question 90

A mathematical concept indicating that a measure or dimension has an infinite and uncountable number of outcomes

Answer 90

Continuous:

Question 91

Variables that can take on an infinite and uncountable set of values

Answer 91

Continuous variables:

Question 92

A non-probability sampling method that involves choosing members of a population that are easy to contact or reach

Answer 92

Convenience sample:

Question 93

Measures the way two variables tend to change together

Answer 93

Correlation:

Question 94

A process that uses different portions of the data to test and train a model on different iterations

Answer 94

Cross-validation:

Question 95

A plaintext file that uses commas to separate distinct values from one another; Stands for “comma-separated values”

Answer 95

CSV file:

Question 96

The business term that describes how many and at what rate customers stop using a product or service, or stop doing business with a company

Answer 96

Customer churn:

Question 97

The process of formatting data and removing unwanted material

Answer 97

Data cleaning:

Question 97

The process of protecting people’s private or sensitive data by eliminating PII

Answer 97

Data anonymization:

Question 98

A data professional who makes data accessible, ensures data ecosystems offer reliable results, and manages infrastructure for data across enterprises

Answer 98

Data engineer:

Question 99

Well-founded standards of right and wrong that dictate how data is collected, shared, and used

Answer 99

Data ethics:

Question 100

A process for ensuring the formal management of a company’s data assets

Answer 100

Data governance:

Question 101

Any individual who works with data and/or has data skills

Answer 101

Data professional:

Question 102

The discipline of making data useful

Answer 102

Data science:

Question 103

A data professional who works closely with analytics to provide meaningful insights that help improve current business operations

Answer 103

Data scientist:

Question 104

The location where data originates

Answer 104

Data source:

Question 105

The practices of an organization that ensures that data is accessible, usable, and safe

Answer 105

Data stewardship:

Question 106

An attribute that describes a piece of data based on its values, its programming language, or the operations it can perform

Answer 106

Data type:

Question 107

A collection of data values or objects that contain different data types

Answer 107

Data structure:

Question 108

A two-dimensional, labeled data structure with rows and columns

Answer 108

DataFrame:

Question 109

A graph, chart, diagram, or dashboard that is created as a representation of information

Answer 109

Data visualization:

Question 110

A file type used to store data, often in tables, indexes, or fields

Answer 110

Database (DB) file:

Question 111

A two-dimensional data-structure organized into rows and columns

Answer 111

Dataframe:

Question 112

A clustering methodology that searches data space for continuous regions of high density; stands for “density-based spatial clustering of applications with noise”

Answer 112

DBSCAN:

Question 113

Troubleshooting, or searching for errors in a script or program

Answer 113

Debugging:

Question 114

A node of the tree where decisions are made

Answer 114

Decision node:

Question 115

A flowchart-like structure that uses branching paths to predict the outcomes of events, or the probability of certain outcomes

Answer 115

Decision tree:

Question 116

The elimination or removal of matching data values in a dataset

Answer 116

Deduplication:

Question 117

A keyword that defines a function at the start of the function block

Answer 117

def:

Question 118

The concept that two events are dependent if one event changes the probability of the other event

Answer 118

Dependent events:

Question 119

The variable a given model estimates

Answer 119

Dependent variable (Y):

Question 120

A type of statistics that summarizes the main features of a dataset

Answer 120

Descriptive statistics:

Question 121

A function that returns the statistical summary of a dataframe or series, including mean, standard deviation, and minimum and maximum column values.

Answer 121

Describe():

Question 122

A function used to create a dictionary

Answer 122

dict():

Question 123

A data structure that consists of a collection of key-value pairs

Answer 123

Dictionary:

Question 124

A function that finds the elements present in one set but not the other

Answer 124

difference():

Question 125

Qualitative data values used to categorize and group data to reveal details about it

Answer 125

Dimensions:

Question 126

The process data professionals use to familiarize themselves with the data so they can start conceptualizing how to use it; one of the six practices of EDA

Answer 126

Discovering:

Question 127

Features with a countable number of values between any two values

Answer 127

Discrete features:

Question 128

A variable that has a countable number of possible values

Answer 128

Discrete random variable:

Question 129

A mathematical concept indicating that a measure or dimension has a finite and countable number of outcomes

Answer 129

Discrete:

Question 130

A hyperparameter in agglomerative clustering models that determines the distance above which clusters will not be merged

Answer 130

distance_threshold:

Question 131

An in-depth guide that is written by the developers who created a package that features very specific information on various functions and features

Answer 131

Documentation:

Question 132

How to access the methods and attributes that belong to an instance of a class

Answer 132

Dot notation:

Question 133

A group of text that explains what a method or function does; also referred to as a “docstring”

Answer 133

Documentation string:

Question 134

The process of removing some observations from the majority class, making it so they make up a smaller percentage of the dataset than before

Answer 134

Downsampling:

Question 135

Variables with values of 0 or 1 that indicate the presence or absence of something

Answer 135

Dummy variables:

Question 136

A NumPy attribute used to check the data type of the contents of an array

Answer 136

dtype:

Question 137

Variables that can point to objects of any data type

Answer 137

Dynamic typing:

Question 138

A value the user inputs or the output of a program, an operation, or a function

Answer 138

Dynamic value:

Question 139

A branch of economics that uses statistics to analyze economic problems

Answer 139

Econometrics:

Question 140

A way of distributing computational tasks over a bunch of nearby processors (i.e., computers) that is good for speed and resiliency and does not depend on a single source of computational power

Answer 140

Edge computing:

Question 141

A reserved keyword that executes subsequent conditions when the previous conditions are not true

Answer 141

elif:

Question 142

A reserved keyword that executes when preceding conditions evaluate as False

Answer 142

else:

Question 143

A type of probability based on experimental or historical data

Answer 143

Empirical probability:

Question 144

A concept stating that the values on a normal curve are distributed in a regular pattern, based on their distance from the mean

Answer 144

Empirical rule:

Question 145

Refers to building multiple models and aggregating their predictions

Answer 145

Ensemble learning:

Question 146

In DBSCAN clustering models, a hyperparameter that determines the radius of a search area from any given point

Answer 146

eps (Epsilon):

Question 147

(Refer to ensemble learning)

Answer 147

Ensembling:

Question 148

A built-in function that iterates through a sequence and tracks each element and its place in the index

Answer 148

Enumerate():

Question 149

In a regression model, the natural noise assumed to be in a model

Answer 149

Errors:

Question 150

A character that changes the typical behavior of the characters that follow it

Answer 150

Escape character:

Question 151

Stage of the PACE workflow where a data professional will present findings with internal and external stakeholders, answer questions, consider different viewpoints, and make recommendations

Answer 151

Execute stage:

Question 152

(Refer to independent variable)

Answer 152

Explanatory variable:

Question 153

The process of converting a data type of an object to a required data type

Answer 153

Explicit conversion:

Question 154

The process of investigating, organizing, and analyzing datasets and summarizing their main characteristics, often by employing data wrangling and visualization methods; the six main practices of EDA are: discovering, structuring, cleaning, joining, validating, and presenting

Answer 154

Exploratory data analysis (EDA):

Question 155

Expression: A combination of numbers, symbols, or other variables that produce a result when evaluated

Answer 155

Expression:

Question 156

Quantifies the difference between the amount of variance that is left unexplained by a reduced model that is explained by the full model

Answer 156

Extra Sum of Squares F-test:

Question 157

The process of retrieving data out of data sources for further data processing

Answer 157

Extracting:

Question 158

A model’s ability to predict new values that fall outside of the range of values in the training data

Answer 158

Extrapolation:

Question 159

The harmonic mean of precision and recall

Answer 159

F1-Score:

Question 160

A test result that indicates something is present when it really is not

Answer 160

False positive:

Question 161

The process of using practical, statistical, and data science knowledge to select, transform, or extract characteristics, properties, and attributes from raw data

Answer 161

Feature engineering:

Question 162

A type of feature engineering that involves taking multiple features to create a new one that would improve the accuracy of the algorithm

Answer 162

Feature extraction:

Question 163

A type of feature engineering that involves selecting the features in the data that contribute the most to predicting the response variable

Answer 163

Feature selection:

Question 164

A type of feature engineering that involves modifying existing features in a way that improves accuracy when training the model

Answer 164

Feature transformation:

Question 165

The process of selecting a smaller part of a dataset based on specified values and using it for viewing or analysis

Answer 165

Filtering:

Question 166

Data that was gathered from inside your own organization

Answer 166

First-party data:

Question 167

A data type that represents numbers that contain decimals

Answer 167

Float:

Question 168

A piece of code that iterates over a sequence of values

Answer 168

For loop:

Question 169

A string method that formats and inserts specific substrings into designated places within a larger string

Answer 169

format():

Question 170

A stepwise variable selection process that begins with the null mode—with zero independent variables—and considers all possible variables to add; incorporates the independent variable that contributes the most explanatory power to the model

Answer 170

Forward selection:

Question 171

A body of reusable code for performing specific processes or tasks

Answer 171

Function:

Question 172

A function that returns an object (iterator) which can be iterated over (one value at a time)

Answer 172

Generator():

Question 173

Values that are completely different from the overall data group and have no association with any other outliers

Answer 173

Global outliers:

Question 174

A variable that can be accessed from anywhere in a program or script

Answer 174

Global variable:

Question 175

Model ensembles that use gradient boosting

Answer 175

Gradient boosting machines (GBMs):

Question 176

A boosting methodology where each base learner in the sequence is built to predict the residual errors of the model that preceded it

Answer 176

Gradient boosting:

Question 177

A tool to confirm that a model achieves its intended purpose by systematically checking every combination of hyperparameters to identify which set produces the best results, based on the selected metric

Answer 177

GridSearch:

Question 178

A pandas DataFrame method that groups rows of the dataframe together based on their values at one or more columns, which allows further analysis of the groups

Answer 178

groupby():

Question 179

Grouping: The process of aggregating individual observations of a variable into groups

Answer 179

Grouping:

Question 180

An event where programmers and data professionals come together and work on a project

Answer 180

Hackathon:

Question 181

A function that returns a preview of the column names and the first few rows of a dataset

Answer 181

Head():

Question 182

A type of data visualization that depicts the magnitude of an instance or set of values based on two colors

Answer 182

Heatmap:

Question 183

A Python help function used to display the documentation of modules, functions, classes, keywords, and more

Answer 183

Help():

Question 184

A data visualization that depicts an approximate representation of the distribution of values in a dataset

Answer 184

Histogram:

Question 185

A random sample of observed data that is not used to fit the model

Answer 185

Hold-out sample:

Question 186

An assumption of simple linear regression stating that the variation of the residuals (errors) is constant or similar across the model

Answer 186

Homoscedasticity assumption:

Question 187

Hyperparameters: Parameters that can be set by the modeler before the model is trained

Answer 187

Hyperparameters:

Question 188

Refers to changing parameters that directly affect how the model trains, before the learning process begins

Answer 188

Hyperparameter tuning:

Question 189

A theory or an explanation, based on evidence, that is not yet proven true

Answer 189

Hypothesis:

Question 190

A statistical procedure that uses sample data to evaluate an assumption about a population parameter

Answer 190

Hypothesis testing:

Question 191

A reserved keyword that sets up a condition in Python

Answer 191

if:

Question 192

A type of notation in pandas that indicates when the user wants to select by integer-location-based position

Answer 192

iloc[]:

Question 193

The concept that a data structure or element’s values can never be altered or updated

Answer 193

Immutability:

Question 194

A data type in which the values can never be altered or updated

Answer 194

Immutable data type:

Question 195

The process Python uses to automatically convert one data type to another without user involvement

Answer 195

Implicit conversion:

Question 196

A statement that uses the import keyword to load an external library, package, module, or function into the computing environment

Answer 196

Import statement:

Question 197

The concept that two events are independent if the occurrence of one event does not change the probability of the other event

Answer 197

Independent events:

Question 198

An assumption of simple linear regression stating that each observation in the dataset is independent

Answer 198

Independent observation assumption:

Question 199

The variable whose trends are associated with the dependent variable

Answer 199

Independent variable (X):

Question 200

A string method that outputs the index number of a character in a string

Answer 200

index():

Question 201

A way to refer to the individual items within an iterable by their relative position

Answer 201

Indexing:

Question 202

The sum of the squared distances between each observation and its nearest centroid

Answer 202

Inertia:

Question 203

Inferential statistics: A type of statistics that uses sample data to draw conclusions about a larger population

Answer 203

Inferential statistics:

Question 204

Gives the total number of entries, along with the data types—called Dtypes in pandas—of the individual entries

Answer 204

Info():

Question 205

Refers to letting a programmer build relationships between concepts and group them together to reduce code duplication

Answer 205

Inheritance:

Question 206

A way of combining data such that only the keys that are in both dataframes get included in the merge

Answer 206

Inner join:

Question 207

Input validation: The practice of thoroughly analyzing and double-checking to make sure data is complete, error-free, and high-quality

Answer 207

Input validation:

Question 208

Information entered into a program

Answer 208

Input:

Question 209

A Python function that can be used to ask a question in a message and store the answer in a variable

Answer 209

Input():

Question 210

A function that takes an index as the first parameter and an element as the second parameter, then inserts the element into a list at the given index

Answer 210

insert():

Question 211

A variable that is declared in a class outside of other methods or blocks

Answer 211

Instance variable:

Question 212

Refers to creating a copy of the class that inherits all class variables and methods

Answer 212

Instantiation:

Question 213

A standard integer data type, representing numbers somewhere between negative nine quintillion and positive nine quintillion

Answer 213

Int64:

Question 214

A data type used to represent whole numbers without fractions

Answer 214

Integer:

Question 215

A piece of software that has an interface to write, run, and test a piece of code

Answer 215

Integrated Development Environment (IDE):

Question 216

Represents how the relationship between two independent variables is associated with changes in the mean of the dependent variable

Answer 216

Interaction term:

Question 217

The y value of the point on the regression line where it intersects with the y-axis

Answer 217

Intercept (constant 𝐵0):

Question 218

Traits that focus on communicating and building relationships

Answer 218

Interpersonal skills:

Question 219

The distance between the first quartile (Q1) and the third quartile (Q3)

Answer 219

Interquartile range:

Question 220

intersection(): A function that finds the elements that two sets have in common

Answer 220

intersection():

Question 221

A sample statistic plus or minus the margin of error

Answer 221

Interval:

Question 222

A calculation that uses a range of values to estimate a population parameter

Answer 222

Interval estimate:

Question 223

A rule that checks objects and classes for ancestry

Answer 223

Is:

Question 224

A dictionary method to retrieve both the dictionary’s keys and values

Answer 224

items():

Question 225

An object that’s looped, or iterated, over

Answer 225

Iterable:

Question 226

The repeated execution of a set of statements, where one iteration is the single execution of a block of code

Answer 226

Iteration:

Question 227

The process of augmenting data by adding values from other datasets; one of the six practices of EDA

Answer 227

Joining:

Question 228

A data storage file that is saved in a JavaScript format

Answer 228

JSON file:

Question 229

An open-source web application for creating and sharing documents containing live code, mathematical formulas, visualizations, and text

Answer 229

Jupyter Notebook:

Question 230

An unsupervised partitioning algorithm used to organize unlabeled data into groups, or clusters

Answer 230

K-means:

Question 231

An underlying core program, like Python

Answer 231

Kernel:

Question 232

The shared points of reference between different dataframes

Answer 232

Keys:

Question 233

A dictionary method to retrieve only the dictionary’s keys

Answer 233

keys():

Question 234

A special word in a programming language that is reserved for a specific purpose and that can only be used for that purpose

Answer 234

Keyword:

Question 235

Data transformation technique where each category is assigned a unique number instead of a qualitative value

Answer 235

Label encoding:

Question 236

The nodes where a final prediction is made

Answer 236

Leaf node:

Question 237

In XGBoost, a hyperparameter that specifies how much weight is given to each consecutive tree’s prediction in the final ensemble

Answer 237

learning_rate:

Question 238

A way of combining data such that all of the keys in the left dataframe are included, even if they aren’t in the right dataframe

Answer 238

Left join:

Question 239

A function used to measure the length of strings

Answer 239

Len():

Question 240

A reusable collection of code; also referred to as a “package”

Answer 240

Library:

Question 241

The probability of observing the actual data, given some set of beta parameters

Answer 241

Likelihood:

Question 242

A collection of an infinite number of points extending in two opposite directions

Answer 242

Line:

Question 243

A technique that estimates the linear relationship between a continuous dependent variable and one or more independent variables

Answer 243

Linear regression:

Question 244

An assumption of simple linear regression stating that each predictor variable (Xi) is linearly related to the outcome variable (Y)

Answer 244

Linearity assumption:

Question 245

A nonlinear function that connects or links the dependent variable to the independent variables mathematically

Answer 245

Link function:

Question 246

The method used to determine which points/clusters to merge

Answer 246

Linkage:

Question 247

A data structure that helps store and manipulate an ordered collection of items

Answer 247

List:

Question 248

Formulaic creation of a new list based on the values in an existing list

Answer 248

List comprehension:

Question 249

The percentage of the population in a given age group that can read and write

Answer 249

Literacy rate:

Question 250

Notation that is used to select pandas rows and columns by name

Answer 250

loc[]:

Question 251

(Refer to logit)

Answer 251

Log-Odds function:

Question 252

An operator that connects multiple statements together and performs complex comparisons

Answer 252

Logical operator:

Question 253

A technique that models a categorical dependent variable (Y) based on one or more independent variables (X)

Answer 253

Logistic regression:

Question 254

The logarithm of the odds of a given probability

Answer 254

Logit:

Question 255

A block of code used to carry out iterations

Answer 255

Loop:

Question 256

A function that measures the distance between the observed values and the model’s estimated values

Answer 256

Loss function:

Question 257

When constructing an interval, the calculation of the sample means minus the margin of error

Answer 257

Lower limit:

Question 258

The use and development of algorithms and statistical models to teach computer systems to analyze and discover patterns in data

Answer 258

Machine learning:

Question 259

The average of the absolute difference between the predicted and actual values

Answer 259

MAE (Mean Absolute Error):

Question 260

Commands that are built into IPython to simplify common tasks

Answer 260

Magic commands:

Question 261

(Refer to magic commands)

Answer 261

Magics:

Question 262

An extension of ANCOVA and MANOVA that compares how two or more continuous outcome variables vary according to categorical independent variables, while controlling for covariates

Answer 262

MANCOVA (Multivariate Analysis of Covariance):

Question 263

An extension of ANOVA that compares how two or more continuous outcome variables vary according to categorical independent variables

Answer 263

MANOVA (Multivariate Analysis of Variance):

Question 264

The maximum expected difference between a population parameter and a sample estimate

Answer 264

Margin of error:

Question 265

A markup language that lets the user write formatted text in a coding environment or plain-text editor

Answer 265

Markdown:

Question 266

A library for creating static, animated, and interactive visualizations in Python

Answer 266

matplotlib:

Question 267

In tree-based models, a hyperparameter that controls how deep each base learner tree will grow

Answer 267

max_depth:

Question 268

In decision tree and random forest models, a hyperparameter that specifies the number of features that each tree randomly selects during training called “colsample_bytree” in XGBoost

Answer 268

max_features:

Question 269

A technique for estimating the beta parameters that maximizes the likelihood of the model producing the observed data

Answer 269

Maximum Likelihood Estimation (MLE):

Question 270

The average value in a dataset

Answer 270

Mean:

Question 271

A value that represents the center of a dataset

Answer 271

Measure of central tendency:

Question 272

A value that represents the spread of a dataset, or the amount of variation in data points

Answer 272

Measure of dispersion:

Question 273

A method by which the position of a value in relation to other values in a dataset is determined

Answer 273

Measure of position:

Question 274

Numeric values that can be aggregated or placed in calculations

Answer 274

Measures:

Question 275

The middle value in a dataset

Answer 275

Median:

Question 276

Someone who shares knowledge, skills, and experience to help another grow both professionally and personally

Answer 276

Mentor:

Question 277

A pandas function that joins two dataframes together; it only combines data by extending along axis one horizontally

Answer 277

merge():

Question 278

A method to combine two (or more) different dataframes along a specified starting column(s)

Answer 278

Merging:

Question 279

A function that belongs to a class and typically performs an action or operation

Answer 279

Method:

Question 280

In XGBoost models, a hyperparameter indicating that a tree will not split a node if it results in any child node with less weight than this value called “min_samples_leaf” in decision tree and random forest models

Answer 280

min_child_weight:

Question 281

Methods and criteria used to evaluate data

Answer 281

Metrics:

Question 282

In decision tree and random forest models, a hyperparameter that defines the minimum number of samples for a leaf node called “min_child_weight” in XGBoost

Answer 282

min_samples_leaf:

Question 282

In DBSCAN clustering models, a hyperparameter that specifies the number of samples in an ε-neighborhood for a point to be considered a core point (including itself)

Answer 282

min_samples:

Question 283

In decision tree and random forest models, a hyperparameter that defines the minimum number of samples that a node must have to split into more nodes

Answer 283

min_samples_split:

Question 284

A data value that is not stored for a variable in the observation of interest

Answer 284

Missing data:

Question 285

The most frequently occurring value in a dataset

Answer 285

Mode:

Question 286

Statements about the data that must be true in order to justify the use of a particular modeling technique

Answer 286

Model assumptions:

Question 287

The process of determining which model should be the final product and put into production

Answer 287

Model selection:

Question 288

The set of processes and activities intended to verify that models are performing as expected

Answer 288

Model validation:

Question 289

The ability to write code in separate components that work together and that can be reused for other programs

Answer 289

Modularity:

Question 290

A simple Python file containing a collection of functions and global variables

Answer 290

Module:

Question 291

An operator that returns the remainder when one number is divided by another

Answer 291

Modulo:

Question 292

A technique that estimates the relationship between one continuous dependent variable and two or more independent variables

Answer 292

Multiple linear regression:

Question 292

The average of the squared difference between the predicted and actual values

Answer 292

MSE (Mean Squared Error):

Question 293

(Refer to multiple linear regression)

Answer 293

Multiple regression:

Question 294

The concept that if the events A and B are independent, then the probability of both A and B happening is the probability of A multiplied by the probability of B

Answer 294

Multiplication rule (for independent events):

Question 295

The ability to change the internal state of a data structure

Answer 295

Mutability:

Question 295

The concept that two events are mutually exclusive if they cannot occur at the same time

Answer 295

Mutually exclusive:

Question 296

In K-means and agglomerative clustering models, a hyperparameter that specifies the number of clusters in the final model

Answer 296

n_clusters:

Question 297

The core data object of NumPy; also referred to as “ndarray”

Answer 297

N-dimensional array:

Question 298

In random forest and XGBoost models, a hyperparameter that specifies the number of trees your model will build in its ensemble

Answer 298

n_estimators:

Question 299

A supervised classification technique that is based on Bayes’s Theorem with an assumption of independence among predictors

Answer 299

Naive Bayes:

Question 300

Consistent guidelines that describe the content, creation date, and version of a file in its name

Answer 300

Naming conventions:

Question 301

Rules built into the syntax of a programming language

Answer 301

Naming restrictions:

Question 302

How null values are represented in pandas; stands for “not a number”

Answer 302

NaN:

Question 303

A NumPy attribute used to check the number of dimensions of an array

Answer 303

ndim:

Question 304

An inverse relationship between two variables, where when one variable increases, the other variable tends to decrease, and vice versa

Answer 304

Negative correlation:

Question 305

A loop inside of another loop

Answer 305

Nested loop:

Question 306

An assumption of simple linear regression stating that no two independent variables (Xi and Xj) can be highly correlated with each other

Answer 306

No multicollinearity assumption:

Question 306

A group organized for purposes other than generating profit; often aims to further a social cause or provide a benefit to the public

Answer 306

Nonprofit:

Question 307

A special data type in Python used to indicate that things are empty or that they return nothing

Answer 307

None:

Question 307

The total number of data entries for a data column that are not blank

Answer 307

Non-null count:

Question 307

A sampling method that is based on convenience or the personal preferences of the researcher, rather than random selection

Answer 307

Non-probability sampling:

Question 307

A programming system that is based around objects which can contain both data and code that manipulates that data

Answer 307

Object-oriented programming:

Question 307

Refers to when certain groups of people are less likely to provide responses

Answer 307

Nonresponse bias:

Question 307

An assumption of simple linear regression stating that the residuals are normally distributed

Answer 307

Normality assumption:

Question 307

A type of probability based on statistics, experiments, and mathematical measurements

Answer 307

Objective probability:

Question 307

A continuous probability distribution that is symmetrical on both sides of the mean and bell-shaped

Answer 307

Normal distribution:

Question 307

An essential library that contains multidimensional array and matrix data structures and functions to manipulate them

Answer 307

NumPy:

Question 307

A component category, usually associated with its respective class

Answer 307

Object type:

Question 307

A collection of data that consists of variables and methods or functions

Answer 307

Object:

Question 308

The existing sample of data, where each data point in the sample is represented by an observed value of the dependent variable and an observed value of the independent variable

Answer 308

Observed values:

Question 309

A data transformation technique that turns one categorical variable into several binary variables

Answer 309

One hot encoding:

Question 310

(Refer to dependent variable)

Answer 310

Outcome variable (Y):

Question 310

A type of statistical testing that compares the means of one continuous dependent variable based on three or more groups of one categorical variable

Answer 310

One-Way ANOVA:

Question 310

Data that is available to the public and free to use, with guidance on how to navigate the datasets and acknowledge the source

Answer 310

Open data:

Question 311

A common way to calculate linear regression coefficients

Answer 311

Ordinary least squares estimation (OLS):

Question 312

Observations that are an abnormal distance from other values or an overall pattern in a data population

Answer 312

Outliers:

Question 312

A way of combining data such that all of the keys from both dataframes get included in the merge

Answer 312

Outer join:

Question 313

A message stating what to do next

Answer 313

Output:

Question 314

When a model fits the observed or training data too specifically and is unable to generate suitable estimates for the general population

Answer 314

Overfitting:

Question 315

The probability of observing results as extreme as those observed when the null hypothesis is true

Answer 315

P-value:

Question 316

A workflow data professionals can use to remain focused on the end goal of any given dataset; stands for plan, analyze, construct, and execute

Answer 316

PACE:

Question 317

A fundamental unit of shareable code that others have developed for a specific purpose

Answer 317

Package:

Question 318

A powerful library built on top of NumPy that’s used to manipulate and analyze tabular data

Answer 318

pandas:

Question 319

A characteristic of a population

Answer 319

Parameter:

Question 320

The value below which a percentage of data falls

Answer 320

Percentile:

Question 321

Information that permits the identity of an individual to be inferred by either direct or indirect means

Answer 321

Personally identifiable information (PII):

Question 322

Stage of the PACE workflow where the scope of a project is defined and the informational needs of the organization are identified

Answer 322

Plan stage:

Question 323

A calculation that uses a single value to estimate a population parameter

Answer 323

Point estimate:

Question 324

A probability distribution that models the probability that a certain number of events will occur during a specific time period

Answer 324

Poisson distribution:

Question 325

A method that extracts an element from a list by removing it at a given index

Answer 325

pop():

Question 326

The phenomenon of more popular items being recommended too frequently

Answer 326

Popularity bias:

Question 326

Every possible element that a data professional is interested in measuring

Answer 326

Population:

Question 327

The percentage of individuals or elements in a population that share a certain characteristic

Answer 327

Population proportion:

Question 328

A relationship between two variables that tend to increase or decrease together.

Answer 328

Positive correlation:

Question 329

An ANOVA test that performs a pairwise comparison between all available groups while controlling for the error rate

Answer 329

Post hoc test:

Question 330

The probability of an event occurring after taking into consideration new information

Answer 330

Posterior probability:

Question 331

The proportion of positive predictions that were correct to all positive predictions

Answer 331

Precision:

Question 332

The estimated Y values for each X calculated by a model

Answer 332

Predicted values:

Question 333

(Refer to independent variable)

Answer 333

Predictor variable:

Question 334

The process of making a cleaned dataset available to others for analysis or further modeling; one of the six practices of EDA

Answer 334

Presenting:

Question 335

Refers to the probability of an event before new data is collected

Answer 335

Prior probability:

Question 336

The branch of mathematics that deals with measuring and quantifying uncertainty

Answer 336

Probability:

Question 337

A function that describes the likelihood of the possible outcomes of a random event

Answer 337

Probability distribution:

Question 338

A sampling method that uses random selection to generate a sample

Answer 338

Probability sampling:

Question 339

A series of instructions written so that a computer can perform a certain task, independent of any other application

Answer 339

Program:

Question 340

The words and symbols used to write instructions for computers to follow

Answer 340

Programming languages:

Question 341

A method of non-probability sampling that involves researchers selecting participants based on the purpose of their study

Answer 341

Purposive sample:

Question 341

Measures the proportion of variation in the dependent variable, Y, explained by the independent variable(s), X

Answer 341

R2 (The Coefficient of Determination):

Question 341

A general-purpose programming language

Answer 341

Python:

Question 341

A value that divides a dataset into four equal parts

Answer 341

Quartile:

Question 342

A visual that helps to define roles and responsibilities for individuals or teams to ensure work gets done efficiently; lists who is responsible, accountable, consulted, and informed for project tasks

Answer 342

RACI chart:

Question 342

A process whose outcome cannot be predicted with certainty

Answer 342

Random experiment:

Question 343

A starting point for generating random numbers

Answer 343

Random seed:

Question 343

An ensemble of decision trees trained on bootstrapped data with randomly selected features

Answer 343

Random forest:

Question 344

A Python function that returns a sequence of numbers starting from zero, increments by 1 by default, and stops before the given number

Answer 344

range():

Question 344

A variable that represents the values for the possible outcomes of a random event

Answer 344

Random variable:

Question 345

The difference between the largest and smallest value in a dataset

Answer 345

Range:

Question 346

Unsupervised learning techniques that use unlabeled data to offer relevant suggestions to users

Answer 346

Recommendation systems:

Question 346

The proportion of actual positives that were identified correctly to all actual positives

Answer 346

Recall:

Question 347

The process of restructuring code while maintaining its original functionality

Answer 347

Refactoring:

Question 348

A group of statistical techniques that use existing data to estimate the relationships between a single dependent variable and one or more independent variables

Answer 348

Regression analysis:

Question 349

The estimated betas in a regression model

Answer 349

Regression coefficient:

Question 350

A set of regression techniques that shrinks regression coefficient estimates towards zero, adding in bias, to reduce variance

Answer 350

Regularization:

Question 350

(Refer to regression analysis)

Answer 350

Regression models:

Question 351

A method that removes an element from a list

Answer 351

remove():

Question 352

A sample that accurately reflects the characteristics of a population

Answer 352

Representative sample:

Question 353

A NumPy method used to change the shape of an array

Answer 353

reshape():

Question 354

The difference between observed or actual values and the predicted values of the regression line

Answer 354

Residual:

Question 355

A reserved keyword in Python that makes a function produce new results which are saved for later use

Answer 355

return:

Question 355

The capability to define code once and using it many times without having to rewrite it

Answer 355

Reusability:

Question 356

(Refer to dependent variable)

Answer 356

Response variable:

Question 357

A way of combining data such that all the keys in the right dataframe are included—even if they aren’t in the left dataframe

Answer 357

Right join:

Question 358

The first node of the tree, where the first decision is made

Answer 358

Root node:

Question 359

A segment of a population that is representative of the entire population

Answer 359

Sample:

Question 360

The set of all possible values for a random variable

Answer 360

Sample space:

Question 361

The number of individuals or items chosen for a study or experiment

Answer 361

Sample size:

Question 362

The process of selecting a subset of data from a population

Answer 362

Sampling:

Question 363

A probability distribution of a sample statistic

Answer 363

Sampling distribution:

Question 363

Refers to when a sample is not representative of the population as a whole

Answer 363

Sampling bias:

Question 364

A list of all the items in a target population

Answer 364

Sampling frame:

Question 365

Refers to how much an estimate varies between samples

Answer 365

Sampling variability:

Question 366

Refers to when a population element can be selected more than one time

Answer 366

Sampling with replacement:

Question 367

Refers to when a population element can be selected only one time

Answer 367

Sampling without replacement:

Question 368

A series of scatterplots that show the relationships between pairs of variables

Answer 368

Scatterplot matrix:

Question 369

A collection of commands in a file designed to be executed like a program

Answer 369

Script:

Question 370

A visualization library based on matplotlib that provides a simpler interface for working with common plots and graphs

Answer 370

Seaborn:

Question 371

Data that was gathered outside your organization directly from the original source

Answer 371

Second-party data:

Question 372

A parameter passed to a method or attributes used to instantiate an object

Answer 372

Self:

Question 373

Code written in a way that is readable and makes its purpose clear

Answer 373

Self-documenting code:

Question 374

A one-dimensional labeled array capable of holding any data type

Answer 374

Series:

Question 374

Refers to the variables and objects that give meaning to Python code

Answer 374

Semantics:

Question 374

A positionally-ordered collection of items

Answer 374

Sequence:

Question 374

A function that takes an iterable as an argument and returns a new set object

Answer 374

Set():

Question 374

A data structure in Python that contains only unordered, non-interchangeable elements; a Tableau term for a custom field of data created from a larger dataset based on custom conditions

Answer 374

Set:

Question 374

A NumPy attribute used to check the shape of an array

Answer 374

shape:

Question 374

The mean of the silhouette coefficients of all the observations in a model

Answer 374

Silhouette score:

Question 375

A technique that estimates the linear relationship between one independent variable, X, and one continuous dependent variable, Y

Answer 375

Simple linear regression:

Question 375

(Refer to learning_rate)

Answer 375

Shrinkage:

Question 376

A probability sampling method in which every member of a population is selected randomly and has an equal chance of being chosen

Answer 376

Simple random sample:

Question 377

The comparison of different models’ silhouette scores

Answer 377

Silhouette analysis:

Question 378

A probability sampling method in which every member of a population is selected randomly and has an equal chance of being chosen

Answer 378

Simple random sample:

Question 379

The minimum pairwise distance between clusters

Answer 379

Single:

Question 380

A method for breaking information down into smaller parts to facilitate efficient examination and analysis from different viewpoints

Answer 380

Slicing:

Question 381

The amount that y increases or decreases per one-unit increase of x

Answer 381

Slope:

Question 382

A method of non-probability sampling that involves researchers recruiting initial participants to be in a study and then asking them to recruit other people to participate in the study

Answer 382

Snowball sample:

Question 383

The process of arranging data into a meaningful order for analysis

Answer 383

Sorting:

Question 384

A statistic that calculates the typical distance of a data point from the mean of a dataset

Answer 384

Standard deviation:

Question 385

The standard deviation of a sample statistic

Answer 385

Standard error:

Question 386

The sample standard deviation divided by the square root of the sample size

Answer 386

Standard error of the mean:

Question 387

The square root of the sample proportion times one minus the sample proportion divided by the sample size

Answer 387

Standard error of the proportion:

Question 388

The process of putting different variables on the same scale

Answer 388

Standardization:

Question 389

A characteristic of a sample

Answer 389

Statistic:

Question 390

The study of the collection, analysis, and interpretation of data

Answer 390

Statistics:

Question 390

The claim that the results of a test or experiment are not explainable by chance alone

Answer 390

Statistical significance:

Question 391

A probability sampling method that divides a population into groups and randomly selects some members from each group to be in the sample

Answer 391

Stratified random sample:

Question 391

A Tableau term for a group of dashboards or worksheets assembled into a presentation

Answer 391

Story:

Question 392

The portion of a string that can contain more than one character; also referred to as a substring

Answer 392

String slice:

Question 393

A sequence of characters and punctuation that contains textual information

Answer 393

String:

Question 393

A machine learning model that is used to make predictions about unseen events

Answer 393

Supervised model:

Question 393

A programming string used in code in which characters exist as the value themselves, rather than as variables

Answer 393

String literal:

Question 394

The sum of the squared difference between each observed value and its associated predicted value

Answer 394

Sum of squared residuals (SSR):

Question 394

A type of probability based on personal feelings, experience, or judgment

Answer 394

Subjective probability:

Question 394

A category of machine learning that uses labeled datasets to train algorithms to classify or predict outcomes

Answer 394

Supervised machine learning:

Question 395

The process of taking raw data and organizing or transforming it to be more easily visualized, explained, or modeled; one of the six practices of EDA

Answer 395

Structuring:

Question 396

A method that summarizes data using a single number

Answer 396

Summary statistics:

Question 397

A function that finds elements from both sets that are mutually not present in the other

Answer 397

symmetric_difference():

Question 397

The structure of code words, symbols, placement, and punctuation

Answer 397

Syntax:

Question 398

A probability sampling method that puts every member of a population into an ordered sequence, chooses a random starting point in the sequence, and selects members for the sample at regular intervals

Answer 398

Systematic random sample:

Question 399

A business intelligence and analytics platform that helps people visualize, understand, and make decisions with data

Answer 399

Tableau:

Question 400

Data that is in the form of a table, with rows and columns

Answer 400

Tabular data:

Question 401

The complete set of elements that someone is interested in knowing more about

Answer 401

Target population:

Question 402

Data gathered outside your organization and aggregated

Answer 402

Third-party data:

Question 402

A NumPy method to convert arrays into lists

Answer 402

Tolist():

Question 403

A type of statistical testing that compares the means of one continuous dependent variable based on three or more groups of two categorical variables

Answer 403

Two-Way ANOVA:

Question 403

A type of supervised machine learning that performs classification and regression tasks

Answer 403

Tree-based learning:

Question 404

An immutable sequence that can contain elements of any data type

Answer 404

Tuple:

Question 405

A function used to identify the type of data in a list

Answer 405

type():

Question 406

A function that transforms input into tuples

Answer 406

tuple():

Question 407

Refers to when some members of a population are inadequately represented in a sample

Answer 407

Undercoverage bias:

Question 408

A machine learning model that is used to discover the natural structure of the data, finding relationships within unlabeled data

Answer 408

Unsupervised model:

Question 408

A function that finds all the elements from both sets

Answer 408

union():

Question 409

When constructing an interval, the calculation of the sample means plus the margin of error

Answer 409

Upper limit:

Question 409

A named container which stores values in a reserved location in the computer’s memory

Answer 409

Variable:

Question 410

The process of taking observations from the minority class and either adding copies of those observations to the dataset or generating new observations to add to the dataset

Answer 410

Upsampling:

Question 411

The process of verifying that the data is consistent and high quality; one of the six practices of EDA

Answer 411

Validating:

Question 412

A dictionary method to retrieve only the dictionary’s values

Answer 412

values():

Question 413

The process of determining which variables or features to include in a given model

Answer 413

Variable selection:

Question 414

Quantifies how correlated each independent variable is with all of the other independent variables

Answer 414

Variance inflation factors (VIF):

Question 414

Refers to model flexibility and complexity, so the model learns from existing data; the average of the squared difference of each data point from the mean

Answer 414

Variance:

Question 415

A process that enables operations to be performed on multiple components of a data object at the same time

Answer 415

Vectorization:

Question 416

A method of non-probability sampling that consists of members of a population who volunteer to participate in a study

Answer 416

Voluntary response sample:

Question 417

Merges two clusters whose merging will result in the lowest inertia

Answer 417

Ward:

Question 418

A model that performs slightly better than randomly guessing

Answer 418

Weak learner:

Question 419

A loop that instructs the computer to continuously execute the code based on the value of a condition

Answer 419

While loop:

Question 420

An optimized GBM package

Answer 420

XGBoost (extreme gradient boosting):

Question 421

Occurs when the dataset has no occurrences of a class label and some value of a predictor variable together

Answer 421

Zero Frequency problem:

Question 422

A measure of how many standard deviations below or above the population mean a data point is

Answer 422

Z-score: