Index
A
B
- back-propagation / Basic structure
- bar charts
- basic Python, example
- Bayes formula
- Bayes theorem
- bi-modal
- bias/variance tradeoff
- bias variance tradeoff
- big data
- binary classifier / A bit deeper
- binomial random variable
- box plots
C
- Cartesian graph / Graphs
- causation
- central limit theorem
- centroid
- chi-square goodness of fit test
- chi-square test for association/independence
- classification
- classification tree
- cluster
- clustering
- coefficient of variation
- collectively exhaustive / Collectively exhaustive events
- collectively exhaustive events
- communication
- complementary events / Complementary events
- compound events
- conditional probability
- confidence
- confidence intervals
- confounding factor / Random sampling
- confusion matrix / A bit deeper
- continuous data
- continuous random variable
- correlation
- correlation coefficients
- cross validation error
- CSV (comma separated value) / Example – world alcohol consumption data
D
- data
- data, obtaining
- data exploration
- data mining
- data model
- data points
- data preprocessing
- data sampling
- data science
- data science, case studies
- data science Venn diagram
- decision trees
- Deep Neural Network Classifier (DNNClassifier)
- dimension reduction
- discrete data
- discrete random variables
- domain knowledge / Domain knowledge
- Domain Knowledge / The data science Venn diagram
- dot product
- dummy variables
E
- Empirical rule
- ensembling techniques / Ensembling techniques
- entity movement / Basic structure
- entropy
- error functions
- Euler's number / Logistic regression
- event
- exploration tips, for qualitative data
- exploratory data analysis (EDA)
- exponent
- extra-marital affairs case study
- extreme cases, bias/variance tradeoff
F
G
H
- histograms
- hypothesis test
- hypothesis test, for categorical values
I
- independent events
- intersection / Set theory
- interval level, of data
J
K
- k-fold cross validation
- K-means clustering
- K-Nearest Neighbors (KNN) algorithm
- K folds cross validation
- KPI (key performance indicator)
L
- labeled data / Supervised learning
- levels, data
- likelihood
- likert scale
- linear algebra
- linear regression
- line graphs
- logarithm
- logistic regression
- log odds
M
N
- Naïve Bayes classification
- neural networks
- nominal level, of data
- normalizing constant
- notation / Probability
- null hypothesis
- null model
- null set / Set theory
O
P
Q
- qualitative data
- qualitative data, versus quantitative data
- quantitative data
R
- random forests
- random sampling / Random sampling
- random variables
- ratio level, of data
- regression
- regression metrics
- regression tree
- reinforcement learning
- relative frequency
- relative length
S
- sample
- sample space
- sampling bias / Random sampling
- sampling distributions
- scalar
- scatter plot
- set / Set theory
- set theory / Set theory
- Silhouette Coefficient
- Simpson's paradox / Simpson's paradox
- slope
- spawner-recruit models
- square matrix
- standard deviation / Standard deviation
- standard normal distribution
- statistical model
- statistical modeling
- statistics
- statistics, measuring
- stock prices prediction based on social media case study
- structured data
- subset / Set theory
- Substantive Expertise / The data science Venn diagram
- summation
- sum of squared residuals
- supervised learning
T
U
V
- vector
- verbal communication
W
- why/how/what strategy, of presentation
Y
Z
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