Business Analytics at TU München

Flashcards and summaries for Business Analytics at the TU München

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Study with flashcards and summaries for the course Business Analytics at the TU München

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Tasks performed in Data Engineering and Statistics

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What are the two dimensions of the Analytics Landscape?

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Reasoning
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Solution for Overfitting

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Subset Selection - Rule of thumb

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Algorithm for Generating Item Sets

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Maximum Likelihood Estimation
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Likelihood Function

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Tasks of Data Mining and Econometrics

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Testing and Training

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Algorithms and Optimization
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Lasso vs. Ridge Regression

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Exemplary flashcards for Business Analytics at the TU München on StudySmarter:

Business Analytics

Tasks performed in Data Engineering and Statistics
Organize data, execute large queries, describe means, and test hypotheses

Business Analytics

What are the two dimensions of the Analytics Landscape?
Competitive Advantage, Degree of Complexity

Business Analytics

Reasoning
good model is a simple model that achieves high accuracy on the given data

Business Analytics

Solution for Overfitting
Prune the tree

Business Analytics

Subset Selection - Rule of thumb
keep top 50 attributes

Business Analytics

Algorithm for Generating Item Sets
1. Generating Item Sets 2. Generating Rules from Item Sets

Business Analytics

Maximum Likelihood Estimation
statistical method for estimating coefficients of a model

Business Analytics

Likelihood Function
Measures probability of observing the particular set of dependent variable values that occur in the sample

Business Analytics

Tasks of Data Mining and Econometrics
Forecast events, predict time series, or discrete choice decisions of customers

Business Analytics

Testing and Training
Holdout procedure, stratified holdout, repeated holdout

Business Analytics

Algorithms and Optimization
Develop algorithms and optimization models for planning, scheduling, pricing, and revenue management

Business Analytics

Lasso vs. Ridge Regression
Lasso produces simpler & more interpretable models that involved only a subset of predictors; qualitatively similar behavior to ridge regression -> larger lambda, variance decr., bias incr.; lasso can generate more accurate predictions; Cross-validation to determine which approach better on particular data set

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