This will give you an idea about what is data analytics and what is the importance of data analytics.

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Gwalior
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DATA ANALYTICS & ITS IMPORTANCE
cs - 503
Data Analytics
INSTITUTE OF TECHNOLOGY & MANAGEMENT
Presented to - Presented by - (Gr. 1)
Dr. Pradeep Yadav Abhay Dhupar (01)
Associate Professor Abhay Bhadouriya(02)
(Dept. of CSE) Abhinav Goyal(03)
Abhinav Gupta(04)

2. Introduction to Data
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Gwalior
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• Data is a collection of facts, such as numbers, words,
measurements, observations or just descriptions of things.
• The quantities, characters, or symbols on which operations are
performed by a computer, which may be stored and transmitted in
the form of electrical signals and recorded on magnetic, optical, or
mechanical recording media.
• Data can be qualitative or quantitative.

3. Qualitative vs Quantitative
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Gwalior
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• Qualitative data is descriptive information (it describes something)
• Quantitative data is numerical information (numbers)
 Discrete data can only take
certain values (ex. Whole
numbers.
 Continuous data can take any
value (within a range)

4. Analytics
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Gwalior
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• Analytics is the systematic computational analysis of data or
statistics
• Analytics is the discovery, interpretation, and communication of
meaningful patterns or summary in data
• Analytics is not a tool or technology, rather it is the way of
thinking and acting on data.

5. Data Analytics
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• “is a process of inspecting, cleansing, transforming, and modeling
data with the goal of discovering useful information, suggesting
conclusions, and supporting decision-making”. - Wikipedia
• "leverage data in a particular functional process (or application) to
enable context-specific insight that is actionable.“ - Gartner
• “is using our current data sets to extract useful information to
support advanced decision making” - ATC

6. The Process
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The process involved in data analysis involves several different steps:
1) The first step is to determine the data requirements or how the data is grouped. Data may be
separated by age, demographic, income, or gender. Data values may be numerical or be
divided by category.
2) The second step in data analytics is the process of collecting it. This can be done through a
variety of sources such as computers, online sources, cameras, environmental sources, or
through personnel.
3) Once the data is collected, it must be organized so it can be analyzed. Organization may take
place on a spreadsheet or other form of software that can take statistical data.
4) The data is then cleaned up before analysis. This means it is scrubbed and checked to ensure
there is no duplication or error, and that it is not incomplete. This step helps correct any
errors before it goes on to a data analyst to be analyzed.

7. Components of Data Analytics
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• Data mining: Data mining breaks down huge reserves of raw data into small chunks of information that
can be usable. They also identify anomalies in groups of data and assess the dependencies between
different data groups to come up with correlations between them. Data mining is used for determining
behavioural patterns in patient data in many clinical trials.
• Text analytics: Text analytics is used to develop auto-correct for your phone and predictive typing for
your emails. It involves processing huge chunks of unstructured texts to develop algorithms. It includes
linguistic analysis, pattern recognition in textual data and filtering out junk emails from useful ones.
• Data visualisation: It involves laying out data in a visual format for a better assessment. It helps make
complex data understandable. Examples include bar charts, histograms, graph, and pie charts.
• Business intelligence: It involves transforming data into actionable insights for a business. These results
are used for making business strategies such as product placement and pricing. It involves using visual
tools such as heat maps, pivot tables and mapping techniques.

8. Data Analytics Pillars and Foundation
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9. Data Analytics vs Data Analysis
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• Data analysis is a process involving the collection,
manipulation, and examination of data for getting
a deep insight. Data analytics is taking the
analyzed data and working on it in a meaningful
and useful way to make well-versed business
decisions.
• Tools used for data analysis are Open Refine,
Rapid Miner, KNIME, Google Fusion Tables,
Node XL, Wolfram Alpha, Tableau Public, etc.
Tools used in Data analytics are Python, Tableau
Public, SAS, Apache Spark, Excel, etc.

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Gwalior
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Types of Data Analytics
There are 4 different types of analytics. Here, we start with the simplest one
and go further to the more sophisticated types. As it happens, the more
complex an analysis is, the more value it brings.
• Descriptive Analysis.
• Diagnostic Analysis.
• Predictive Analysis.
• Prescriptive Analysis.

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Gwalior
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12. ITM
Gwalior
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Types of Data Analytics
There are 4 different types of analytics. Here, we start with the simplest one
and go further to the more sophisticated types. As it happens, the more
complex an analysis is, the more value it brings.
• Descriptive Analysis.
• Diagnostic Analysis.
• Predictive Analysis.
• Prescriptive Analysis.

13. ITM
Gwalior
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