Data Science & BI at International School Of Management | Flashcards & Summaries

Lernmaterialien für Data Science & BI an der International School of Management

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What is the difference between Data Science & Business Intelligence?

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Data Science deals with predictive & prescriptive analysis. That means it deals with the unknown unknowns, so it calculates best predictions without given formulas.


Business intelligence deals mainly with descriptive analysis. That means it deals with known unknowns, so it calculates KPIs and predictions using known formula.

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What is the difference between Data Scientist and BI professional?

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Data Scientists are more knowledgable about data & analytics.


BI professionals understand the needs & requirements to build & maintain BI solutions & reports

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What are descriptive, predictive and prescriptive analytics?

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Descriptive analytics: use statistics to describe the data used to gain information or other purposes.


Predictive analytics: makes predictions about unknown future events, discloses reasons behind them typically by advanced analytics


Prescriptive analytics: optimizes indicators and recommends actions for smart decision-making

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Explain Bid data, Advanced analytics, Data analysis and Data analytics

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Big data: 

  • data that are too large / too complex
  • fast processing time
  • many users
  • cannot be effectively and/or efficiently handled by traditional data-related theories, technologies and tools


Advanced analytics:

  • theories, technologies and tools to get in-depth understanding of Big data


Data analysis /analytics:

  • theories, technologies and tools that enable in-depth understanding into data
  • consist of descriptive, predictive and prescriptive analytics
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What are explicit, implicit and deep analytics?

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Explicit analytics:

  • Focus on descriptive analytics
  • involving observable aspects by reporting, descriptive analytics, alerting & forecasting


Implicit analytics:

  • focus on deep analytics
  • involving hidden aspects by predictive modeling, optimization, prescriptive analytics and actionable knowledge delivery


Deep analytics:

  • refers to data analytics
  • in-depth understanding of why & how things have happened, are happening or will happen
  • cannot be addressed by descriptive analytics
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What is the difference between traditional and new systems?

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Traditional systems:

  • structured data
  • transaction systems
    • ERP, SCM, PLM - product data, HR data, customer data


New systems:

  • semi-structured data (XML)
  • unstructured data (text/ files)
  • machine systems / sensor data
    • manufacturing data, weather information
  • position data (mobile / IT)
    • RFID, telecom data
  • Relationship & behavior data (social / semantic web)
    • twitter, linkedin, facebook
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What is the difference between business intelligence and business analytics?

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Business intelligence:

describe and understand data

(data mining, statistics)


Business analytics:

predict and optimize data

(simulation, predictive methods, optimization methods)


Both together: improve

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What are the V's of Big Data?

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Volume, Variety, Velocity, Value, Validity

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What are the general steps of Data Science?


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A. Identification of management problem

1. Data preparation

2. Selection of algorithm to model data

3. Tuning model parameters for selected algorithm

4. Evaluation & Validation of models based on their accuracy 


B. Alternative solutions to the management problem

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Explain preparation of data

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  • selection of variables
  • characteristic extraction
  • handling of missing data (approximate, calculate, delete values)
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Explain the selection of an algorithm to model data

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a) unsupervised learning


k-means clustering

association analysis

principal component analysis


What patterns are hidden in my data?

We don't know which pattern we are looking for.


b) supervised learning


regression analysis

k-nearest neighbours

decision tree


derive forecasts from the patterns in my data

predictions based on predefined patterns


c) encouraging learning


A/B test, slimming epsilon

strategy (multi-armed bandits)


derive predictions from patterns in my data and improve predictions as new data arrives

improves itself by using results as feedback

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TESTE DEIN WISSEN

Explain the tuning of model parameters for the selected algorithm 

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Parameters:

options that allow you to vary the settings of an algorithm


Hypersensitive: 

error: overfit - can hardly be transferred to other data


Insensitive:

error: unterfit - ignores obvious structures, neglects trends, no reliable conclusions

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Q:

What is the difference between Data Science & Business Intelligence?

A:

Data Science deals with predictive & prescriptive analysis. That means it deals with the unknown unknowns, so it calculates best predictions without given formulas.


Business intelligence deals mainly with descriptive analysis. That means it deals with known unknowns, so it calculates KPIs and predictions using known formula.

Q:

What is the difference between Data Scientist and BI professional?

A:

Data Scientists are more knowledgable about data & analytics.


BI professionals understand the needs & requirements to build & maintain BI solutions & reports

Q:

What are descriptive, predictive and prescriptive analytics?

A:

Descriptive analytics: use statistics to describe the data used to gain information or other purposes.


Predictive analytics: makes predictions about unknown future events, discloses reasons behind them typically by advanced analytics


Prescriptive analytics: optimizes indicators and recommends actions for smart decision-making

Q:

Explain Bid data, Advanced analytics, Data analysis and Data analytics

A:

Big data: 

  • data that are too large / too complex
  • fast processing time
  • many users
  • cannot be effectively and/or efficiently handled by traditional data-related theories, technologies and tools


Advanced analytics:

  • theories, technologies and tools to get in-depth understanding of Big data


Data analysis /analytics:

  • theories, technologies and tools that enable in-depth understanding into data
  • consist of descriptive, predictive and prescriptive analytics
Q:

What are explicit, implicit and deep analytics?

A:

Explicit analytics:

  • Focus on descriptive analytics
  • involving observable aspects by reporting, descriptive analytics, alerting & forecasting


Implicit analytics:

  • focus on deep analytics
  • involving hidden aspects by predictive modeling, optimization, prescriptive analytics and actionable knowledge delivery


Deep analytics:

  • refers to data analytics
  • in-depth understanding of why & how things have happened, are happening or will happen
  • cannot be addressed by descriptive analytics
Mehr Karteikarten anzeigen
Q:

What is the difference between traditional and new systems?

A:

Traditional systems:

  • structured data
  • transaction systems
    • ERP, SCM, PLM - product data, HR data, customer data


New systems:

  • semi-structured data (XML)
  • unstructured data (text/ files)
  • machine systems / sensor data
    • manufacturing data, weather information
  • position data (mobile / IT)
    • RFID, telecom data
  • Relationship & behavior data (social / semantic web)
    • twitter, linkedin, facebook
Q:

What is the difference between business intelligence and business analytics?

A:

Business intelligence:

describe and understand data

(data mining, statistics)


Business analytics:

predict and optimize data

(simulation, predictive methods, optimization methods)


Both together: improve

Q:

What are the V's of Big Data?

A:

Volume, Variety, Velocity, Value, Validity

Q:

What are the general steps of Data Science?


A:

A. Identification of management problem

1. Data preparation

2. Selection of algorithm to model data

3. Tuning model parameters for selected algorithm

4. Evaluation & Validation of models based on their accuracy 


B. Alternative solutions to the management problem

Q:

Explain preparation of data

A:
  • selection of variables
  • characteristic extraction
  • handling of missing data (approximate, calculate, delete values)
Q:

Explain the selection of an algorithm to model data

A:

a) unsupervised learning


k-means clustering

association analysis

principal component analysis


What patterns are hidden in my data?

We don't know which pattern we are looking for.


b) supervised learning


regression analysis

k-nearest neighbours

decision tree


derive forecasts from the patterns in my data

predictions based on predefined patterns


c) encouraging learning


A/B test, slimming epsilon

strategy (multi-armed bandits)


derive predictions from patterns in my data and improve predictions as new data arrives

improves itself by using results as feedback

Q:

Explain the tuning of model parameters for the selected algorithm 

A:

Parameters:

options that allow you to vary the settings of an algorithm


Hypersensitive: 

error: overfit - can hardly be transferred to other data


Insensitive:

error: unterfit - ignores obvious structures, neglects trends, no reliable conclusions

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