Enabling Technologies

The concept of data science is pretty much new to the computing discipline

volume_mute

True

False

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Big data characteristics: Five V’s and corresponding challenges

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

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) 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) 1997: Statistics renamed as data science
(3) 2001: Knowledge discovery and data mining
(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

volume_mute

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False

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Classify each of the following data as structured or unstructured

(1) names
(2) emails
(3) dates
(4) addresses
(5) credit card numbers

(6) videos
(7) stock information
(8) photos
(9) geolocation
(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

volume_mute

data science

data mining

data warehouse

database

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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?

volume_mute

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) ⇨ (3) ⇨ (4)

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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) 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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The concept of data science is pretty much new to the computing discipline

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Explanation

Big data characteristics: Five V’s and corresponding challenges

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Explanation

Big data characteristics: Five V’s explained

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Explanation

The evolution of data science

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Explanation

The data science evolution enables the extraction of knowledge from massive volumes of structured data only

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Explanation

Classify each of the following data as structured or unstructured

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Explanation

The process of extraction of actionable knowledge directly from data through data discovery, hypothesis and analytical hypothesis

Correct Answer

Explanation

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?

Correct Answer

Explanation

Phases of value chain of big data

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Explanation

Functional components of data science

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Explanation

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