Enabling Technologies

Enabling technologies that lead to the big advancement in data science
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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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Big data characteristics: Five V’s and corresponding challenges

Match the labels with the right characteristics and challenges

5-vs.jpg

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Volume
Variety
Velocity
Veracity
Value
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Big data characteristics: Five V’s explained

By today’s standards, one Terabyte or greater is considered a big data. IDC has predicted that 40 ZB of data will be processed by 2030, meaning each person may have
5.2 TB of data to be processed.

The high demands large storage capacity and analytical capabilities to handle such massive s of data. The high implies that data comes in many different formats, which can be very difficult and expensive to manage accurately. The high refers to the inability to process big data in real time to extract meaningful information or knowledge from it. The implies that it is rather difficult to verify data. The of big data varies with its application domains.

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volume
variety
value
veracity
velocity
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The evolution of data science

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

  1. (1) transparent 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) transparent 1997: Statistics renamed as data science
  3. (3) transparent 2001: Knowledge discovery and data mining
  4. (4) transparent 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

 

  1. (1) transparent names
  2. (2) transparent emails
  3. (3) transparent dates
  4. (4) transparent addresses
  5. (5) transparent credit card numbers
  1. (6) transparent videos
  2. (7) transparent stock information
  3. (8) transparent photos
  4. (9) transparent geolocation
  5. (10) transparent 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?

Business needs
Domain knowledge
Analytical skills
Programming expertise
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Phases of value chain of big data

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

(1) transparent (2) transparent (3) transparent (4) transparent

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Acquisition
Data generation
Analysis
Storage
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Functional components of data science

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

  • data science is considered as the (1) transparent 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) transparent are developed while (3) transparent are designed
  • The (4) transparent field is formed by intersecting domain expertise with mathematical statistics
  • (5) transparent has resulted from the intersection of domain expertise and programming skills
  • (6) transparent is the intersection of programming skills and mathematical statistics.
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models
modelling
algorithms
Data analytics
intersection
Algorithms
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