1. Data Mining, Data Science, Big Data

2. Data Science Data Science aims to extract insights from large data
Less emphasis on algorithms
More emphasis on ‘outreach’
Term Data Science is about 10 years old, very popular nowadays
Many people reinvent themselves as Data Scientists
data miners, statisticians, BI people, analysts, database developers

3. Data Mining & Data Science
Data Mining fff
Statistics
Computational methods
Dealing with large data
Visualisation
Involving domain knowledge
Interpretable and interpreted results

4. Big Data Because you can… Administrative/financial reasons
cheap storage
Administrative/financial reasons
Internet and social computing
Internet of Things, ubiquitous computing
1980
1990
2000
2010
$0.01 $1
$100
$10,000
$1,000,000
cost per Gigabyte
in dollars

5. Cheap Storage
350 million photos uploaded to Facebook per day
almost 20 additional racks per day required
1956, IBM 350, 5 Mb
90 Tb

6. Big Data Many facets, often people focus on only one
Very, very large data
CERN, Google, Facebook, Twitter, …
Analytics
Internet-generated
Social data
Heterogeneous, unstructured data
Large-scale technologies
MapReduce, Hadoop

7. Size-complexity trade-off
Technological restrictions produce a trade-off
Many Big Data projects algorithmically not so complex
Embarrassingly parallel
complexity
size
CERN