Enabling Technologies

Enabling technologies that lead to the big advancement in data science

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QUESTION OF
Views #: 652
Questions #: 10
Time: 10 minutes
Pass Score: 80.0%
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The concept of data science is pretty much new to the computing discipline

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volume_mute
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Big data characteristics: Five V’s and corresponding challenges

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Match the labels with the right characteristics and challenges

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(1)
(2)
(3)
(4)
(5)
Volume
Velocity
Value
Veracity
Variety
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Big data characteristics: Five V’s explained

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The evolution of data science

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Forbes, Wikipedia and NIST have provided some historical reviews of the data science evolution.  Match each stage with its definition

  1. (1) 1968: The science of dealing with data, once they have been established, while the relation of the data to what they represent is delegated to other fields and sciences.
  2. (2) 1997: Statistics renamed as data science
  3. (3) 2001: Knowledge discovery and data mining
  4. (4) 2013: Data Science is the extraction of actionable knowledge directly from data through a process of discovery, hypothesis, and analytical hypothesis analysis
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Data logy
Statistics
KDD
Big Data
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The data science evolution enables the extraction of knowledge from massive volumes of structured data only

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Classify each of the following data as structured or unstructured

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  1. (1) names
  2. (2) emails
  3. (3) dates
  4. (4) addresses
  5. (5) credit card numbers
  1. (6) videos
  2. (7) stock information
  3. (8) photos
  4. (9) geolocation
  5. (10) social media
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structured
unstructured
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The process of extraction of actionable knowledge directly from data through data discovery, hypothesis and analytical hypothesis

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What are the knowledge area that a data scientist should acquire in order to manage the end-to-end scientific process through each stage in the big data life cycle?

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Phases of value chain of big data

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Rearrange the phases to start from the first till the last phase

(1) ⇨ (2) ⇨ (3) ⇨ (4)

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Acquisition
Data generation
Analysis
Storage
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Functional components of data science

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Functional components of data science supported by some software libraries on the cloud in 2016

  • data science is considered as the (1) of three interdisciplinary areas: computer science or programming skills, mathematics and statistics, and application domain expertise
  • Through the combination of domain knowledge and mathematical skills, specific (2) are developed while (3) are designed
  • The (4) field is formed by intersecting domain expertise with mathematical statistics
  • (5) has resulted from the intersection of domain expertise and programming skills
  • (6) is the intersection of programming skills and mathematical statistics.
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models
modelling
algorithms
Data analytics
intersection
Algorithms
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✕
0
sin cos tan
sin-1 cos-1 tan-1 π e
xy x3 x2 ex 10x
y√x 3√x √x ln log
( ) 1/x % n!
7 8 9 + MS
4 5 6 – M+
1 2 3 × M-
0 . EXP ÷ MR
± RND C = MC
Keywords
Year 11