Business Intelligence an der Universität Erlangen-Nürnberg | Karteikarten & Zusammenfassungen

# Lernmaterialien für Business Intelligence an der Universität Erlangen-Nürnberg

Greife auf kostenlose Karteikarten, Zusammenfassungen, Übungsaufgaben und Altklausuren für deinen Business Intelligence Kurs an der Universität Erlangen-Nürnberg zu.

TESTE DEIN WISSEN

What are the different measure of the descriptive statistics ?

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TESTE DEIN WISSEN
 Measure Definition Application Mean Weighted center (influences by values of every entry) Quantitative values (1st choice) Median Weighted center (middle number of ordered values) Quantitative values & extreme/ many outliers Mode Most frequently occurring value Categorical values Variance Indicator for dispersion around the mean Standard deviation Square root of the variance Correlation (coefficient) Linear relationship between two numeriacal values (-1 - +1) Data preparation – dimensionality reduction – eliminate highly correlated attributes
Lösung ausblenden
TESTE DEIN WISSEN

Lösung anzeigen
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1. Data Cleaning
• Missing values
• noisy data
2. Data Integration
• integrate various sources/tables in one view/ table by using a query
3. Data Reduction
• attribute subset selection
• Dimensionality reduction
4. Data Transformation
• Aggregation
• Normalization
• Discretization
• Feature construction/ Feature engineering
Lösung ausblenden
TESTE DEIN WISSEN

Whats an outlier ? Why is it important to look do a proper oulier handling ?

What techniques can you use for Detection

Lösung anzeigen
TESTE DEIN WISSEN
 Outlier Observation that does not fit to the others, numerically distant from the other points Reasons - Data Qualtity problems (e.g. typos/ wrong measurements)- Exceptional or unusual situations (rare behaviour) Handling Exclusion, not representative for most of the data points, misleading values for the mean, ensure that the model can generalize (not to specific) Detection Techniques - Box Plots (quartile range based)- DBSCAN (Clustering-based method)
Lösung ausblenden
TESTE DEIN WISSEN

Please give a brief defintion of the term "Artificial Intelligence" ?

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TESTE DEIN WISSEN

- study/ science of making a computer (program)  performing tasks by using some kind of humanlike intelligence

-making computer performing things at which people are better

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TESTE DEIN WISSEN

What are stages of AI ? What stage are we currently in ?

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TESTE DEIN WISSEN
• ANI, Artificial Narrow Intelligence (weak)
•  machines perform specific tasks better than a human,
• simple tasks e.g. chat bot
• currently in
• - Artificial General Intelligence (strong)
• think perform like a human
• Artificial Super Intelligence
• Surpass the human beeing
• Far Of speculative

Lösung ausblenden
TESTE DEIN WISSEN

Please do a quick classification of intelligence taks ?

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TESTE DEIN WISSEN
• perception, common sense, natural language processing, reasoning
• Mathematics, games
• financial analysis, engineering,

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TESTE DEIN WISSEN

What are the fields of Application in AI ? (AI Branches)

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TESTE DEIN WISSEN
• Robtics
• Speech (speech to text, text to speech)
• vision (image recognition, machine vision)
• Machine Learning
• Natural Language Processing (classification, machine translation, question answering, text generation)
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TESTE DEIN WISSEN

Please give a short defintion/ explanation of the "Machine Learning" term!

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TESTE DEIN WISSEN
• Study of enabling computers to learn without being explicitly programmed
• Using algorithms to learn the intelligence giving them examples
• Gradually learning & improve the accuracy (imitating the humans learning process)
• Subdiscipline of Artificial Intelligence
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TESTE DEIN WISSEN

What are stages in the Machine Learning process ?

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TESTE DEIN WISSEN

1. Data preparation

2. Model building

3. Model evaluation

4. Model optimizaition

5. Prediction (Model deployment)

!! iterative nature - e.g.  data shift etc. !!

Lösung ausblenden
TESTE DEIN WISSEN

What are the different types of Machine Learning ?

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TESTE DEIN WISSEN
• Supervised
• Labelled data, goal: prediction task (reg/ class)
• Unsupervised
• Unlabeled data, goal: determine patterns groups
• e.g. clustering, association
• Semi-supervised
• Using labelled & unlabelled training data
• Few labelled instances to train and then label the unlabelled data using initial model then adding to labelled data begin process again (more and more data)
• Reinforcement
• Using a system where an agent is getting a feedback (reward) for performing actions in a defined actions scope in a specific/ defined environment
• System learns with the trail&error method by maximizing the reward
Lösung ausblenden
TESTE DEIN WISSEN

Briefly explain the goal of the first step of the first phase of the CRISP-DM process.

What can be the problems which can occour by not executing this phase properly? What can be done to prevent the problems ?

Lösung anzeigen
TESTE DEIN WISSEN

Goal

1. Cleary enunciate the project objectives in terms of the business

2. translate into a proper data mining definition

3. prepare preliminary strategy

Problems/ problem sources (Project Owner perspective vs. Data Analyst perspective)

• - Communication
• technical terms (for PO) vs business terms for (DA)
• Lack of Understanding
• PO doesn't know the capabilties of the DA/ models (other expectations initally) vs DA doesn't undersstand how to help PO
• Organization
• adoption of requirements because of problems with data in later stages vs. PO is not engaged enough in the project

Solution/ Optimization Approaches

• Bridging gap between DA/ PO in terms of Business Understanding using tools like cognitive maps
• define primary objectives (critical thinking, question them, precise enough? feasibility
• determine analytics goals
• interpretability (no black box, management)
• reproducibility/ stability of the analysis
•  runtime (restriction time, ressources)
• interestingeness of outcome ("surprise" domain expert)
Lösung ausblenden
TESTE DEIN WISSEN

What is noisy data ? What tools can you use to detect it?

Lösung anzeigen
TESTE DEIN WISSEN

--> random error in a variable. Results in a specific variance

tools:

Boxplot  - Binning - Regression - Clustering

Lösung ausblenden
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Q:

What are the different measure of the descriptive statistics ?

A:
 Measure Definition Application Mean Weighted center (influences by values of every entry) Quantitative values (1st choice) Median Weighted center (middle number of ordered values) Quantitative values & extreme/ many outliers Mode Most frequently occurring value Categorical values Variance Indicator for dispersion around the mean Standard deviation Square root of the variance Correlation (coefficient) Linear relationship between two numeriacal values (-1 - +1) Data preparation – dimensionality reduction – eliminate highly correlated attributes
Q:

A:
1. Data Cleaning
• Missing values
• noisy data
2. Data Integration
• integrate various sources/tables in one view/ table by using a query
3. Data Reduction
• attribute subset selection
• Dimensionality reduction
4. Data Transformation
• Aggregation
• Normalization
• Discretization
• Feature construction/ Feature engineering
Q:

Whats an outlier ? Why is it important to look do a proper oulier handling ?

What techniques can you use for Detection

A:
 Outlier Observation that does not fit to the others, numerically distant from the other points Reasons - Data Qualtity problems (e.g. typos/ wrong measurements)- Exceptional or unusual situations (rare behaviour) Handling Exclusion, not representative for most of the data points, misleading values for the mean, ensure that the model can generalize (not to specific) Detection Techniques - Box Plots (quartile range based)- DBSCAN (Clustering-based method)
Q:

Please give a brief defintion of the term "Artificial Intelligence" ?

A:

- study/ science of making a computer (program)  performing tasks by using some kind of humanlike intelligence

-making computer performing things at which people are better

Q:

What are stages of AI ? What stage are we currently in ?

A:
• ANI, Artificial Narrow Intelligence (weak)
•  machines perform specific tasks better than a human,
• simple tasks e.g. chat bot
• currently in
• - Artificial General Intelligence (strong)
• think perform like a human
• Artificial Super Intelligence
• Surpass the human beeing
• Far Of speculative

Q:

Please do a quick classification of intelligence taks ?

A:
• perception, common sense, natural language processing, reasoning
• Mathematics, games
• financial analysis, engineering,

Q:

What are the fields of Application in AI ? (AI Branches)

A:
• Robtics
• Speech (speech to text, text to speech)
• vision (image recognition, machine vision)
• Machine Learning
• Natural Language Processing (classification, machine translation, question answering, text generation)
Q:

Please give a short defintion/ explanation of the "Machine Learning" term!

A:
• Study of enabling computers to learn without being explicitly programmed
• Using algorithms to learn the intelligence giving them examples
• Gradually learning & improve the accuracy (imitating the humans learning process)
• Subdiscipline of Artificial Intelligence
Q:

What are stages in the Machine Learning process ?

A:

1. Data preparation

2. Model building

3. Model evaluation

4. Model optimizaition

5. Prediction (Model deployment)

!! iterative nature - e.g.  data shift etc. !!

Q:

What are the different types of Machine Learning ?

A:
• Supervised
• Labelled data, goal: prediction task (reg/ class)
• Unsupervised
• Unlabeled data, goal: determine patterns groups
• e.g. clustering, association
• Semi-supervised
• Using labelled & unlabelled training data
• Few labelled instances to train and then label the unlabelled data using initial model then adding to labelled data begin process again (more and more data)
• Reinforcement
• Using a system where an agent is getting a feedback (reward) for performing actions in a defined actions scope in a specific/ defined environment
• System learns with the trail&error method by maximizing the reward
Q:

Briefly explain the goal of the first step of the first phase of the CRISP-DM process.

What can be the problems which can occour by not executing this phase properly? What can be done to prevent the problems ?

A:

Goal

1. Cleary enunciate the project objectives in terms of the business

2. translate into a proper data mining definition

3. prepare preliminary strategy

Problems/ problem sources (Project Owner perspective vs. Data Analyst perspective)

• - Communication
• technical terms (for PO) vs business terms for (DA)
• Lack of Understanding
• PO doesn't know the capabilties of the DA/ models (other expectations initally) vs DA doesn't undersstand how to help PO
• Organization
• adoption of requirements because of problems with data in later stages vs. PO is not engaged enough in the project

Solution/ Optimization Approaches

• Bridging gap between DA/ PO in terms of Business Understanding using tools like cognitive maps
• define primary objectives (critical thinking, question them, precise enough? feasibility
• determine analytics goals
• interpretability (no black box, management)
• reproducibility/ stability of the analysis
•  runtime (restriction time, ressources)
• interestingeness of outcome ("surprise" domain expert)
Q:

What is noisy data ? What tools can you use to detect it?

A:

--> random error in a variable. Results in a specific variance

tools:

Boxplot  - Binning - Regression - Clustering

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