introduction to data science

1. Data science
Data Science
An emerging area of work concerned with the collection,
preparation, analysis ,visualization, management, and
preservation of large collections of information.
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2. Web page
much of the data in the world is non-numeric and
unstructured.
unstructured means that the data are not arranged in neat
rows and columns. Think of a web page
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3. $
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4. Data
architecture
Data
acquisition
Data
analysis
Data
archiving
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5. Data architect
providing input on how the data would need to be
routed and organized to support the analysis,
visualization, and presentation of the data to the
appropriate people.
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6. Data acquisition
focuses on how the data are collected, and
importantly , how the data are represented prior
to analysis and presentation.
Tool example :barcode
Different barcodes are used for the same product.
(for example, for different sized boxes of cereal).
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7. Data analysis
using portions of data (samples) to make
inferences about the larger context, and
visualization of the data by presenting it in tables,
graphs, and even animations.
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8. Data archiving
Preservation of collected data in a form that
makes it highly reusable ,so "data curation" is
a difficult challenge because it is so hard to
anticipate all of the future uses of the data.
Example(Twitter):
Geocodes : data that shows the geographical location
from which a tweet was sent could be a useful
element to store with the data.
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9. Learning the application domain
Communicating with data users
Seeing the big picture of a complex system
Knowing how data can be represented
:metadata
Data transformation and analysis
Visualization and presentation
Attention to quality
Ethical reasoning :privacy 9

10. About Data
•Data comes from the Latin word, "datum,"
meaning a "thing given“
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11. za15id05v2005kamel
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12. “The fundamental problem of
communication is that of
reproducing at one point either
exactly or approximately a
message selected at another
point”
CLAUDE SHANNON
yes
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0
No
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ASCII
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13. Identifying Data Problems
Data Science is an applied activity and data scientists
serve the needs and solve the problems of data users.
Hint:
The data scientist may never actually become a
farmer, but if you are going to identify a data problem
that a farmer has, you have to learn to think like a
farmer, to some degree.
3 questions:
 subject matter experts.
 ask about anomalies
 ask about risks and uncertainty
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14. Introduction To R
R is an integrated suite of software facilities for data
manipulation, calculation , graphical Display and other
things it has .
 "R" is an open source software program
an effective data handling and storage facility.
 a suite of operators for calculations on arrays, in
particular matrices,
 a large, coherent, integrated collection of
intermediate tools for data analysis,
 graphical facilities for data analysis and display
either directly at the computer or on hardcopy.
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15. Additional Pros:
 R was among the first analysis programs to
integrate capabilities for drawing data directly from
the Twitter(r) social media platform
 The extensibility of R means that new modules are
being added all the time by volunteers
 the lessons one learns in working with R are almost
universally applicable to other programs and
environments.
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16. CONS:
R is "command line" oriented
 R is not especially good at giving feedback or error
messages.
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17. How to write a text
myText <- "this is a piece of text"
 Create Data Set :
myFamilyAges <- c(43, 42, 12, 8, 5)
c(): Concatenates data elements together
 Assignment arrow: <-
 Some mathematical function :
sum():Adds data elements
range():Min value and max value
mean():The average
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