This class was created by Brainscape user Daphne Tan. Visit their profile to learn more about the creator.
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Daphne Tan
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Decks in this class (31)
Session 1
Big data 1997,
What characterizes big data,
Mapreduce
11
cards
Session 1.2
Data visualization,
Clustering,
Co occurrence grouping
8
cards
Session 2
Is webscraping legal,
Almost any data source can be use...,
Data mining is
16
cards
Session 2.1
Data structure volume velocity,
Cross sectional,
Transactional
16
cards
Session 3
We have a particular life insuran...,
How to choose at each step which ...,
The concept of information provid...
16
cards
Session 3.1
Data science tasks can be split i...,
Unsupervised methods,
Supervised methods
14
cards
Session 4
A fitting graph shows,
Fitting graphgenerally there will...,
Complexity is a measure of
22
cards
Session 4.1
Bias variance tradeoffwhen trying...,
Bias variance tradeoffwe can redu...,
Bagging or bootstrap aggregation ...
14
cards
Session 4.2
Higher number of folds means,
Having larger train sets leads to,
Having smaller test sets leads to
10
cards
Session 5
You decide to build a simple web ...,
Desired properties of a big data ...,
Batch layer
13
cards
Session 5.1
We decide to build a simple web a...,
Analytics server databasewhich pr...,
Analytics server databasehow to d...
16
cards
Session 5.2
Desired properties of a big data ...,
Computing arbitrary functions on ...,
The main idea of the lambda archi...
23
cards
Session 6
Why use clustering,
Clustering in the crisp dm cycle,
How to interpret clusters charact...
10
cards
Session 6.1
Bag of words,
Bag of n grams,
Term frequency tf
11
cards
Session 6.2
Clustering what is it,
Is clustering the same as classif...,
Distance measures
9
cards
Session 6.3
K means clustering,
K means clusteringprototype based...,
K means clustering how to
8
cards
Session 6.4
Why text mining,
A token term,
A document
16
cards
Session 7
Sensitive characteristics or prot...,
By defining some characteristics ...,
Formal non discrimination criteria
19
cards
Session 7.1
Deep learning consists in,
Hidden layers have fewer perceptrons,
Deep learning uses relatively old...
15
cards
Session 7.2
Uplift modeling is also known as,
Uplift modeling identifies,
Difference uplift vs predictive
7
cards
E1
Regression,
Classification,
Regression mathematical formula r...
9
cards
E2
Target variable,
Supervised segmentation,
The process of recursively segmen...
7
cards
E3
Confusion matrix,
True positives tp,
True negatives tn
7
cards
E4
Some of the challenges of creatin...,
Best approach to scaling problems,
Scaling using multiple databases
10
cards
E5
How distributed file systems work,
Hadoop mapreduce,
Map
7
cards
E6
What is clustering,
Is clustering the same as classif...,
Distance measures
10
cards
E7
What is text mining,
Cleaning and preprocessing text,
A token term
5
cards
E8
Underfitting,
Overfitting,
Bias
6
cards
E9
Data science tasks can be split i...,
Unsupervised methods,
Unsupervised methods
10
cards
E10
The main idea of the lambda archi...,
Batch layer,
Speed layer
9
cards
E11
Area under the roc curve,
Auc is useful when,
Typical business intelligence bi ...
3
cards
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bdma
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