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CSC1033- Summary Notes
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Notes for the module CSC1033. Summary notes for examination in May/June.
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Big DataWhat is Big Data?
. It means bigger than “normal” (not universally agreed definition). The term is an all-encompassing
one including data, data frameworks along with the tools and techniques used to process and
analyze the data.
. There are different ways that data can be big:
o Many Data points.
o Many Variables.
o High Frequency.
o Data Complexity.
. Big Data refers to a massive amount of data that keeps on growing exponentially with time.
. It is so voluminous (capacious) that it cannot be process or analyzed using convectional data
processing techniques.
. It includes:
o data mining - involves discovering patterns, trends, correlations, or useful information
from large datasets using various techniques such as machine learning, statistics, and
artificial intelligence. ( Identifying customer purchasing patterns from e-commerce data to
improve marketing strategies.)
o data storage - Data storage refers to the process of storing and organizing vast
amounts of data in a structured manner to ensure efficient retrieval and management.
(Using distributed file systems like Hadoop Distributed File System (HDFS) for storing and managing
large datasets.)
o data analysis - Data analysis involves inspecting, cleaning, transforming, and modeling
data to discover useful information, draw conclusions, and support decision-making.
(Analyzing sales data to identify best-performing products and optimize inventory management)
o data sharing - Data sharing involves the exchange of data between different entities or
systems, either within an organization or between organizations. (Data sharing involves the
exchange of data between different entities or systems, either within an organization or between
organizations.)
o data visualisation - Data visualization is the representation of data in graphical or
visual formats, such as charts, graphs, and maps, to make complex datasets more
understandable. (Creating a dashboard with visualizations to show key performance indicators (KPIs)
and trends in real-time.)
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, Type of Data
Unstructured Data:
. Unstructured data contains a significant amount of uncertain
and imprecise data, such as social media data is inherently
uncertain.
. It refers also to the data that lacks any specific form or
structure whatsoever. This makes it very difficult, and time
consuming to process and analyze unstructured data. (e.g.
Email).
Structured Data:
. Is the data that can be processed, stored, and retrieved in a fixed
format.
. It refers to highly organized information that can be readily and
seamlessly stored and accessed from a database by simple search
engine algorithms.
. E.g The employee table in a company database will be structured as the employee details, their
job positions, their salaries, etc., will be present in an organized manner.
Semi - Structured Data:
. Relates to the data containing both the formats mentioned
above, that is, structured and unstructured data.
. It refers to the data that although has not been classified
under a particular repository (database) yet contains vital
information or tags that segregate individual elements within
the data.
.
The advantage of having semi-structured is that is its
flexible and portable. However, queries are less efficient
that in constrained structure.
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