Session 2

Question 1

what are the four steps of business perfromance management?

Answer 1

1. strategies
2. plan
3. monitor/analyse
4. act/adjust

Question 2

what happens in the strategize phase?

Answer 2

finding out about mission, value, goals, objectives, incentives and strategy maps

Question 3

what happens in the plan phase?

Answer 3

we nee to conduct budgeting, forecasting, modelling, introducing initiatives or setting targets

Question 4

what happens in the monitor/analyse phase?

Answer 4

we need to check perfromance dashboards, reports and analytical tools

Question 5

what happens in the act/adjust phase?

Answer 5

we execute the strategy, we need to interpret, collaborate, assess, decide, act adjust and track what is happeninhg tp deal with changes circumstances

Question 6

What is data?

Answer 6

collection of facts usually obtained as the result of experiences, observations and experiments
- may consist of numbersm, words or images
- lowest level of abstraction from which information and knowledge are derived
- data becomed information and knowledge once we analyze it

Question 7

what are the two types of data?

Answer 7

structured
unstructured

Question 8

what are the data categories for structured data?

Answer 8

Question 9

what is categorial data?

Answer 9

can be put into groups and categories using data and labels

Question 10

what is nominal data?

Answer 10

type pf categorial data: 1. nominal data: data classified without an ordering or a rank (female/ male)

Question 11

what is ordinal data?

Answer 11

type of categorical data: 2. ordinal data: data that can be ranked w/o measurable intervals in-between (lower middle upper class)

Question 12

what is numerical data? (and the two sub categories)

Answer 12

data referring to numbers (can be categorised, ranked and has equal intervals in-between)

Question 13

what is interval data?

Answer 13

type of numerical data:
whithout a true/ natural zero (e.g. celsius, 0 degrees is no absence of temp, and 40 degrees is not 2x times 20 degrees)

Question 14

what is ratio data?

Answer 14

type of numerical data:
with a true /natural zero that indicated compleete absence of quantuty (weight, income)

Question 15

what three operations can we make to measure centrality?

Answer 15

1. arthimetic mean (sum/ nomber of observations)
2. mean (middle value of an odered dataset)
3. mode (number that occurs most often)

Question 16

what operastions can we make to measure dispersion?

Answer 16

1. range
2. variance
3. standard deviation
4. mean absolute deviation
5. quartile and interquartile range
6. box-wiskers plot
7. shape and distribution (skewness, kurtosis)

Question 17

what is the range?

Answer 17

(diff. between highest an dlowest value)

Question 18

what is a variance?

Answer 18

measures of how much data spreasds around the average)

Question 19

what is a standard deviation?

Answer 19

(square root of variance)

Question 20

hat is the mean absolute deviation?

Answer 20

(average distance between each data point and the mean)

Question 21

what is quartile and interquartile range?

Answer 21

range of each quartile of data

Question 22

what is the boy-whiskers plot?

Answer 22

graphical representation of the data dispersion

Question 23

what is the shape and distribution (skewness, kurtosis)?

Answer 23

where does the data have more points than elsewhere?

Question 24

what is kurtosis?

Answer 24

desribes the peakiness of the pronability of distribution
the more peaky –> positive kurtosis

Question 25

what is skewness?

Answer 25

quantififes extend and direction of departure from horizontal symmetry in a data set? --> positive is to the left --> negative is to the right

Question 26

what is a dashboard?

Answer 26

dashbaords provide visiual displays of important information, that is consolidated and arranged on a single screen so that the information can be easily digested at one glance and easily drilled in and further explored

Question 27

what should a dashboard have?

Answer 27

1. visual components to highlight data and exceptions that require an action 2. transparency to the user, so that it required minimal training and ie easy to use (especially for execs) 3. combining data from a variety of systems into a single, summarized and unified view of the business 4. enabling drill-down (go from monthly to weekly to daily) or drill through (go deeoer into data point) for underlyinh data sources and reports 5. preseneting a dynamic, real-world view with timely data 6. requiring little coding to implement /deploy / maintain

Question 28

what are best practices for dashboards designs?

Answer 28

1. benchmark KPIs with industry standards to see where the company stands 2. wrap the metrics with contextual metadata: include which data is inlcuded and when last update was made 3. validate the design by usability specialist 4. prioritze the rank alerts and exceptions, but be careful, that there are only important warnings 5. pick the right visual constructs: histograms for distribution, pie chats for market shares 6. provide guided analytics

Question 29

what is included in text analysis?

Answer 29

information retrieval, natural language processing, text mining, web mining and data mining

Question 30

why is text difficult to analyse?

Answer 30

it is unstructured data: - it has linguistic strcuture intended fir human consumption, not for computers - its is relatively dirty: spelling errors, emojis, abbreviations, grammatical errors, or sarcasm - context is important

Question 31

how can we represent data for analysis?

Answer 31

by turning it into a feature-vector form (vector with values, we get varibales from the text)

Question 32

what is a token / term ?

Answer 32

small indivudal elemnts that compose a document, may be words, sentences or paragraphs depending on our definition

Question 33

what is a document?

Answer 33

one piece of text regardless of how large or small

Question 34

what is a corpus?

Answer 34

a collection of doduments (all of wikipedia, all tweets etc.)

Question 35

what are 5 representation tequiniues for textual data?

Answer 35

1. bag of words 2. term frequency (TF) 3. inverse document frequency (IDF) 4. TFIDF 5. N-grams

Question 36

what is the bag of words method?

Answer 36

- treat every document as just a collection of individual words - ignore grammar, word order, sentence structure and punctuation - inexpensive and straigtfoward - we can check if word exists (0 or 1) example: used in spam filters

Question 37

what is the term frequency method?

Answer 37

examine word count in three steps: 1. normalization: every term becomes lowercase 2. stemming: suffixes are removed and plurals are turned into singulars (only leave stem) 3. removal of stop words: very common words in the respective leanguage are being parsed, which do not have any useful meaning (the, and, of etc.)

Question 38

what is the inverse document frequency method?

Answer 38

- next to TF also look at the distribution of a term over a corpus: the term should neither be too rare nor too common - IDF: boost a term gets for being rare as the measure increases the more rare the term is

Question 39

what is the TFIDF method?

Answer 39

we produce TF and and IDF -> it is specific to a single doument, whereas IDF depends on an entire corpus

Question 40

what is the n-gram method?

Answer 40

- in some cases, word order is important and we want to preserve some information about it - so we include sequences of adjacent words, called n-grams as terms (1.000 words, we have 1.000 ns) example:

Question 41

what is an advantage and what is a disadvanatge of n-grams?

Answer 41

+: easy to generate, required no linguistic knowledge -: greatly increases with the size of the feature set, so it needs some special considerations for dealing with massive numbers of features and computational storage space

Question 42

what is the sentiment analysis?

Answer 42

goal is to asnwer the question what people feel about a certain topic (LIWC as dictionary for sentiments and words)