This document provides an introduction to understanding big data analytics. It defines big data as information that can't be processed or analyzed using traditional tools. Big data is growing rapidly, doubling every year, and by 2020 about 1.7 megabytes of new information will be created every second for every person on Earth.
The document outlines a plan to explain what big data is, why it is important, what data analytics is, and where it is used. It defines data analytics as examining, inspecting, cleansing, transforming, and modeling data to draw conclusions. The document discusses descriptive, predictive, diagnostic and unsupervised/supervised analytics methods. It concludes that big data analytics is an important research topic that allows for descriptive and predictive analysis
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Big data analytics
1.
2. Name of the Book:Understanding Big Data: Analytics
for Enterprise Class Hadoop and streaming Data
Author:Paul C. Zikopoulos, Chris Eaton, Dirk deRoos,
Thomas Deutsch, George Lapis and Steven Sit.
Date:
3. Introduction
• Data is everywhere. In fact, the amount of digital data
that exists is growing at a rapid rate, doubling every year.
some researchers states that Data is growing faster than
ever before and by the year 2020, about 1.7 megabytes
of new information will be created every second for
every human being on the planet. Which makes it
extremely important to at least know the basics of the
field of big data and data analytic and knowing the
difference between the two terms.
source:
Big Data: 20 Mind-Boggling Facts Everyone Must Read,
Forbes magazine
4. Plan
1. What Is Big Data?
2. Why Is Big Data Important?
3. What Is Data Analytic?
4. Where Is It Used ?
5. What IsBigData?
• the term Big Data applies to information that
can’t be processed or analyzed using
traditional processes or tools.
6. WhyIs Big Data Important?
Key Big Data Principles
Big Data solutions are ideal:
1. For analyzing not only raw structured data, but
semistructured and unstructured data from a wide
variety of sources.
2. When all, or most, of the data needs to be analyzed
versus a sample of the data
3. For iterative and exploratory analysis when business
measures on data are not predetermined.
4. Aalysis and achieving synergy with existing solutions
for better business outcomes.
7. Data Analytics involves automating
insight into a certain dataset
Big Data is something that
can be used to analyze
insights wich can lead to
better decision and stratigic
busness move
8.
9. What Is DataAnalytic?
Analytics Is in Our Blood:
o I need to go to campus faster!
o Hmm.. Looking at the sky today, I think it’ll be
rain
10. What Is DataAnalytic?
Data analytics (DA) is the process of Examining,
Inspecting, Cleansing, Transforming, and Modeling
data s in order to draw conclusions about the
information they contain, increasingly with the aid of
specialized systems and software like “SAP Analytics”,
“Google Analtics”, “Microsoft power BI”…
11. Analytics Lifecycle
Explore
Elaborate
Model
Plan Model
Use Prepare Data
Comunicate
•Explore the avaible data
•Formulate the questions
•Determine the
necessary Data
•Collect Data
•Clean Data
•Formulate the
questions
•Determine wich
variables to explain &
wich one sto predict
•Select the possible
model and algorithmes
•Implement the model
•Validate it after
choosing the best one
•Interpret result
•Make decision based
on results
•Goals to reach
Source:
Schmarzo(2012):Data analytic lifecycle
13. Unsupervised analytics :when we
don’t know the truth about
something in the past. The
result is segment that we need
to interpret
Example:
We want to do segmentation over
the student, based on the
historical exam score,
attendance, and late history.
Supervised analytics: when we
know the truth about
something in the past
Example:
we have historical weather
data. The temperature,
humidity, cloud density and
weather type (rain, cloudy, or
sunny). Then we can predict
today weather based on temp,
humidity, and cloud density
today
TypesOf Analysis
Do we need only descriptive analysis? Or we need to go with predictive analysis?
Predictive Method
14. TypesOf Analysis
Do we need only descriptive analysis? Or we need to go with predictive analysis?
descriptive analysis
does exactly what its
name implies to
"describe" or
summarize the raw
data and make them
interpretable by
humans.
Descriptive Method
15. TypesOf Analysis
Do we need only descriptive analysis? Or we need to go with predictive analysis?
Examines data or content to
answer the question “Why did it
happen?”, and is characterized
by techniques such as data
discovery and data mining.
Diagnostic Method
16. Conclusion
Big Data Analytics is a hot research topic among the database
researchers as well as the business community. However,
currently we have different methods to analyse big data
which we have mentioned in our paper but there is a lot of
scope to create or invent new method of analytics. There
are different tools and open source software available.
Some of which we have mentioned briefly in the paper.
There is a scope for the future research to compare the
tools and find out the best in a particular situation by
applying it. Also new can always be searched and invented.
There are many more issues which can be further
investigated like: Big data privacy and security,
completeness, Data Quality etc
Data coming from various human & machine activity
Big data is just data with:
More volume
Faster data generation (velocity)
Multiple data format (variety)
For example, typically, data bound for the analytic warehouse has to
be cleansed, documented, and trusted before it’s neatly placed into a
strict warehouse schema
When you stop and think about it, it’s little wonder we’re drowning in
data
When you stop and think about it, it’s little wonder we’re drowning in
data
When you stop and think about it, it’s little wonder we’re drowning in
data
ANALYTICS IS IN YOUR BLOOD
As human being we always try to predict and analyse the world around us so we applicate the same thing for data
with the goal of discovering useful information
When you stop and think about it, it’s little wonder we’re drowning in
data
Telecom: They are one of the most important contributors to Big Data. The telecommunications industry improves the quality of service and routes traffic more efficiently. By analyzing real-time call data records, these companies can identify fraudulent behavior and act immediately.
Education:By using Big Data technology as a learning tool instead of traditional conferencing methods, we have improved student learning and helped teachers better track their performance.
Healthcare: Health care uses massive data analysis to reduce costs, predict epidemics, prevent preventable diseases, and improve quality of life in general.
banks and financial services companies use Analytic systems to suggest immediate actions, such as blocking irregular transactions, stop fraud before it happens, and improve profitability.
Whay might happen in the future
There is 2 types of
What’s happening now ?
Diagnostic analytics takes a deeper look at data to attempt to understand the causes of events and behaviors. .
When you stop and think about it, it’s little wonder we’re drowning in
data
When you stop and think about it, it’s little wonder we’re drowning in
data