Quiz
data analytics final
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Fragen in diesem Quiz (59)
1Which one is not part of OLAP?
- ASelect
- BRollup
- CDrill down
- Dslice & Dice
2Please provide three applications in data mining?
- AFraud Detection
- BDirect Marketing
- CEmail Classification
3The data is stored, retrieved & updated in ____________.
- AOLAP
- BSMTP
- COLTP
- DFTP
4The full form of OLAP is ....
- AOnline Advanced Processing
- BOffline Advanced Processing
- COnline Analytical Performance
- DOnline Analytical Processing
5 An ________ system is market-oriented and is used for data analysis by knowledge workers, including managers, executives, and analysts.
- AOLAP
- BOLTP
- CNone of the above
- DBoth of the above
6It is because of ________________ that enables OLAP to achieve great performance for a query.
- AComposition
- BDice
- CHybrid
- DAggregation
7What are the applications of OLAP?
- Abusiness reporting for sales
- BBudgeting
- CForecasting
- DAll of the above
8Which of the following is not an application of data mining?
- AFinancial fraud detection
- BSpam e-mail filtering
- CCost minimization
9List three of the terms that have been predecessors of analytics.
- Adata support systems (DSS)
- Boperations research (OR)
- Cexpert systems (ES)
10What are some common tasks performed in data preprocessing?
- AData consolidation
- Bdata cleaning
- Cdata transformation
- Ddata reduction
11What do drill down, roll up, and slice and dice mean?
[blank_start]Slice[blank_end]: a subset of a multidimensional array corresponding to a single value set for one (or more) of the dimensions not in the subset.
[blank_start]Dice[blank_end]: a slice on more than two dimensions of a data cube.
[blank_start]Drill Down/Up[blank_end]: specific OLAP technique whereby the user navigates among levels of data ranging from the most summarized (up) to the most detailed (down).
[blank_start]Roll-up[blank_end]: involves computing all the data relationships for one or more dimension- ASlice
- BDice
- CDrill Down/Up
- DRoll-up
12Data mining mistakes
- ASelecting the wrong problem for data mining
- BBeginning without the end in mind
- CNot leaving sufficient time for data acquisition, selection and preparation
- DLooking only at aggregated results and not individual records
13What is overfitting?
- AOverfitting is a modeling error which occurs when a function is too closely fit to a limited set of
data points. - BOverfitting occurs when a statistical model or machine learning algorithm captures the noise of the
data. - COverfitting is when data is lost
- DAll of them
- AOverfitting is a modeling error which occurs when a function is too closely fit to a limited set of
14What does overfitting do?
- ATriggers an event from feedback
- BVery well trained but makes inaccurate assumptions
- CUses input and outputs
- Dall of them
15In a feedforward neural network, which of the three nodes is responsible
for the calculations and has no interaction with the outside world?- Ainput layer
- Boutput layer
- Chidden layer
- Dall of them
16Every Artificial Neural Network need to have at least ..... layers?
- A2
- B3
- C1
- D4
17A neural network consisting of only linear activations is underfitting a
dataset. A student only has the time to change one feature of the network.
Which of the following statements is the best option for increasing the
accuracy of the model?- AAdd more layers to the network
- BTrain for longer
- CIntroduce non-linear activations
- DAdd more data
18Information flows from Input layer -> Hidden Layer-> Output layer
- AFeedforward Network
- BBackpropagation
- CNone of them
19 If we like to load data from a file which command we can use?
- ADELETE FROM tablename
- BINSERT INTO tablename VALUES ( Value 1, Value 2, ...)
- CUPDATE tablename SET values
- DLOAD DATA LOCAL INPATH '/home/hive/sample.txt' OVERWRITE INTO TABLE employee;
20In the text mining, stemming is the process of....
- Acategorizing the block of text into sentence
- Breducing multiple words to their base or root
- Ctransforming the term-by-document matrix to a manageable size
- Dcreate new branches or stems of recorded paragraph
21How to prevent overfitting
- Across-validation
- Btrain with more data
- Cremove features
- Dearly stopping
22Applications of neural network
- AHandwriting recognition
- BStock exchange prediction
- CImage compression
- DTranslate text
- EFace and speech recognition
23Types of activation functions
[blank_start]Sigmoid[blank_end] function - used when the model is predicting probability
[blank_start]Threshold[blank_end] function - used when you don't want to worry about the uncertainty in the middle
[blank_start]Hyberbolic Tangent[blank_end] function - similar to the sigmoid function but has a range of -1 to 1.- ASigmoid
- BThreshold
- CHyberbolic Tangent
24Feed-forward Neural Network
This is the simplest form of ANN (artificial neural network); data
travels only in one direction (input to output)
Radial Basis Functions Neural Network
This model classifies the data point based on its distance from a
center point. The network looks for data points that are similar to each other and groups them. One of the applications for this is power restoration systems.- ARichtig
- BFalsch
25Kohonen Self-organizing Neural Network:
Vectors of random input are input to a discrete map comprised of neurons. Vectors are also called dimensions or planes. Applications include using it to recognize patterns in data like a medical analysis
Recurrent Neural Network:
In this type, the hidden layer saves its output to be used for future prediction. The output becomes part of its new input. Applications include text-to-speech conversion.
Convolution Neural Network:
In this type, the input features are taken in batches—as if they pass through a filter. This allows the network to remember an image in parts. Applications include signal and image processing, such as facial recognition.- ARichtig
- BFalsch
26What is a Multi-Layer Perceptron Model in Artificial Neural Networks?
A multilayer perceptron (MLP) is a neural network connecting multiple layers in a directed graph, which means that the paths connecting nodes in
layers only go one way.- ARichtig
- BFalsch
27Support vector machines were developed for binary classification problems, although extensions to the technique have been made to support multi-class classification and regression problems.
- ARichtig
- BFalsch
28Types of SVM (support vector machine):
Simple SVM:
used for linear regression and classification problems
Kernel SVM:
More flexibility for non-linear data because you can add more features to fit a hyperplane instead of a two-dimensional space.- ARichtig
- BFalsch
29Pros of Support Vector Machine:
- AEffective on datasets with multiple features, like financial or medical data.
- BEffective in cases where number of features is greater than the number of data points
- CUses a subset of training points in the decision function called support vectors which makes it memory efficient
30Cons of Support Vector Machine:
- AIf the number of features is a lot bigger than the number of data points, avoiding over-fitting when choosing kernel functions and regularization term is crucial
- BSVMs don't directly provide probability estimates. Those are calculated using an expensive five-fold cross-validation
- CWorks best on small sample sets because of its high training time
31Steps for machine learning project in python:
▪ Import the dataset
▪ Explore the data to figure out what they look like
▪ Pre-process the data
▪ Split the data into attributes and labels
▪ Divide the data into training and testing sets
▪ Train the SVM algorithm
▪ Make some predictions
▪ Evaluate the results of the algorithm- ARichtig
- BFalsch
32What is big data?
Term used to describe data that is too large and complex to store in traditional databases- ARichtig
- BFalsch
335 V's of Big Data
- AVolume
- BVelocity
- CVariety
- DValue
- EVeracity
34Volume in Big data:
size of the data
data generated each second from sources- ARichtig
- BFalsch
35Velocity in Big data:
speed at which the data is:
generated, collected, analyzed- ARichtig
- BFalsch
36Variety in Big data:
Different types of data
structured, semi-structured, unstructured- ARichtig
- BFalsch
37Value in Big data:
Refers to the ability to turn data into useful business data- ARichtig
- BFalsch
38Veracity in Big data:
Trustworthiness of data in terms of quality and accuracy- ARichtig
- BFalsch
39Challenges of Big Data
- Astoring huge volume of data
- Bprocessing massive data
- Csecuring data
40Hadoop:
open-source framework for storing data and running applications on clusters,
Hadoop HDFS: solves the issue of storing rapidly increasing data
Hadoop map reduce: helps to process and analyze big data faster- ARichtig
- BFalsch
41Hadoop commands:
hadoop version - gets version
hadoop fs -put ... - copy file from local to hdfs
hadoop fs -get ... - move file from hdfs to local
hadoop fs -cat ... - display contents of file
hadoop fs -tail - presents last lines of hdfs file- ARichtig
- BFalsch
42Hadoop Hive:
data warehouse infrastructure tool to process structured data in hadoop. Resides on top of hadoop to summarize big data and makes querying/analzying easy- ARichtig
- BFalsch
43Hadoop hive is not:
a relational database
a design for OLTP
a language for real-time queries
Hadoop hive features:
stores schema in database and processed data into HDFS
designed for OLAP
provides SQL type language called HQL
familiar, fast, scalable and extensible- ARichtig
- BFalsch
44Text Analytics and Text Mining:
information retrieval
information extraction
data mining
web mining
Text analytics = information retrieval + text mining- ARichtig
- BFalsch
45Data mining vs text mining:
both seek useful patterns
both semi-automated processes
difference in nature of data:
- structured vs unstructured data
- structured data: in databases
- unstructured data: word documents, pdf files, text files, etc
To perform text mining - first impose data to structured data then mine the structured data- ARichtig
- BFalsch
46Natural language processing (NLP)
subfield of artificial intelligence and computational linguistics
studies the understanding of the natural human language- ARichtig
- BFalsch
47Challenges of NLP
- Apart-of-speech tagging
- Btext segmentation
- Cword sense disambiguation
- Dsyntax ambiguity
- Eimperfect or irregular input
48NLP task categories
- Aquestion answering
- Bautomatic summarization
- Cmachine translation
- Dspeech recognition
- EOCR
49text mining applications
- Amarketing applications
- Bsecurity applications
- Cmedicine and biology
- Dacademic applications
50TDM (term-by-document matrix) is a sparse matrix. How can we reduce the dimensionality of the TDM
- Amanual - domain expert goes through it
- Beliminate terms with very few occurrences in a few documents
- Ctransform the matrix using singular value decomposition
51Sentiment analysis:
belief, view, opinion...
applications:
voice of the customer,
voice of the market,
voice of the employee,
brand management- ARichtig
- BFalsch
52Sentiment analysis process:
step 1 - detection, comes right after the retrieval and preparation of the text documents, Fact vs Opinion
step 2 - N-P polarity classification, given an opinionated piece of text the goal is to classify the opinion as falling under one of two opposing sentiment polarities, Negative vs Positive
step 3 - Target identification, the goal of this step is to accurately identify the target of the expressed sentiment
step 4 - Collection and Aggregation, once the sentiments of all text data points in the document are identified and calculated, they are able to be aggregated- ARichtig
- BFalsch
53Challenges of processing web data
- Atoo big for effective data mining
- Btoo complex
- Ctoo dynamic
- Dnot specific to domain
54Web mining is the process of discovering intrinsic relationships from web data
- ARichtig
- BFalsch
55In Supervised learning, you train the machine using data which is well “labeled.” allows you to collect data or produce a data output from the previous experience
Unsupervised learning is a machine learning technique, where you do not need to supervise the model. helps you to finds all kind of unknown patterns in data- ARichtig
- BFalsch
56entropy:
a metric that measures the extent of uncertainty or randomness in a data set. If all the data is a subset belong to just one class, then there is no uncertainty or randomness in that data set and therefore the entropy is 0- ARichtig
- BFalsch
57k-fold cross validation:
A popular accuracy assessment technique for prediction models where the complete data set is randomly split into K mutually exclusive subsets of approximately equal size- ARichtig
- BFalsch
58nominal data
a type of data that contains measurements of simple codes assigned to objects as labels, which are not measurements- ARichtig
- BFalsch
59ordinal data
data that contains codes assigned to objects or events as labels that also represent the rank order among them- ARichtig
- BFalsch
Informationen
- Beschreibung
- Computer Science Quiz am data analytics final, erstellt von Duncan Levings am 01/11/2021.
- Autor
- Duncan Levings
- Sprache
- English
- Aufrufe
- 14
- Schlagwörter
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