Datascience at Universität Giessen

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Some challenges of Data Science (Netflix)    

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Data science is (3 areas)

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Data is
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Data vs. information vs. knowledge

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Data into business value

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Which are the sources of Data?

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Sources of data


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Secondary Data

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Keep it simple

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Use the right display

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Perceptual effectiveness
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Use color strategically

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Datascience

Some challenges of Data Science (Netflix)    
- Massive data
- Curse of dimensionality
- Missing data
- Complicated set of factors (actors, direcors, genre)
- probably overfitting (test data vs. training data)

Datascience

Data science is (3 areas)
areas:
- Math & Statistics
- Computer Science & Hacking Skills
- Domain expertise

Goal:
- Extract knowledge from data in various forms, either structured or unstructured

Keyword:
science

Datascience

Data is
- like crude oil
- every 10 min 5 exabytes data are generated (in the past less)
- various sources
- structured (numbers) vs. unstructured (text, images)
- lowest level of abstraction (raw facts from which information and knowledge are derived)

Datascience

Data vs. information vs. knowledge
Data is raw facts:
- unorganized and no deeper meaning on their own

Information requires meaning:
- understanding of relations (answers the "what?")

Knowledge requires context:
- understanding of patterns (answers the "how?")

Data must be transformed into information & knowledge to be useful for decision making

Datascience

Data into business value
Business Question -> DS (needs Data) -> Business Value

DS in businesses:
- Aims at generating BV using data
- Managers can make data-driven decisions insted of gut

Examples:
- website characteristics correlate with its click rate?
- group consumers into categories according to their purchase behavior?
- historic social media posts to improve effectiveness of social media marketing?

(Monetary) benefits obvious --> diffuclt because of Big Data

Datascience

Which are the sources of Data?
- internal Data
- external Data

Datascience

Sources of data


Internal data 

- Procured and consolidated from different branches within the organization (e.g. within a company) - Examples: Internal sales data; marketing data



External data


- Obtained from outside the organization 

- Examples: Purchased data from social media platforms; financial market data - Can be either primary or secondary

Datascience

Secondary Data

- Data collected by someone else


-  Can originate from various sources: e.g. websites, previous research publications, government data, etc.


- Secondary data may be available in published or unpublished form

Datascience

Keep it simple
- Mazimize data-ink ratio:
Ratio: Data ink/ Total ink used in graphic

- no Pie charts

- avoid chartjunk
extraneous visual elements distract from the message

Datascience

Use the right display
- Bars vs. lines
- Trends
- Proportions
- Stacked bar chart
- scatterplots for correlations
- histogram: distribution of values of single variable 

Datascience

Perceptual effectiveness
1. Importance Ordering
2. Expressiveness
3. Constistency

Datascience

Use color strategically
categorical data: 5-8 colors

ordinal data: vary luminance and saturation

color blindness

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