The document provides an overview of data, information, knowledge, and data mining. It defines data as facts/observations/measurements, information as processed data that is useful (e.g. for decision making), and knowledge as patterns in data/information with a high degree of certainty. Data mining is described as the process of extracting useful but non-obvious information from large databases through an interactive and iterative process. Common business applications and technologies involved in data mining are also discussed.
This presentation introduces some concepts of Data Analytics including: Data Science, Big Data, Social Network Analysis, Process Mining, Market Basket Analysis, and Pattern Recognition
Data Analytics with R, Contents and Course materials, PPT contents. Developed by K K Singh, RGUKT Nuzvid.
Contents:
Introduction to Data, Information and Data Analytics,
Types of Variables,
Types of Analytics
Life cycle of data analytics.
Data Mining: What is Data Mining?
History
How data mining works?
Data Mining Techniques.
Data Mining Process.
(The Cross-Industry Standard Process)
Data Mining: Applications.
Advantages and Disadvantages of Data Mining.
Conclusion.
My class presentation at USC. It gives an introduction about what is data science, machine learning, applications, recommendation system and infrastructure.
This presentation introduces some concepts of Data Analytics including: Data Science, Big Data, Social Network Analysis, Process Mining, Market Basket Analysis, and Pattern Recognition
Data Analytics with R, Contents and Course materials, PPT contents. Developed by K K Singh, RGUKT Nuzvid.
Contents:
Introduction to Data, Information and Data Analytics,
Types of Variables,
Types of Analytics
Life cycle of data analytics.
Data Mining: What is Data Mining?
History
How data mining works?
Data Mining Techniques.
Data Mining Process.
(The Cross-Industry Standard Process)
Data Mining: Applications.
Advantages and Disadvantages of Data Mining.
Conclusion.
My class presentation at USC. It gives an introduction about what is data science, machine learning, applications, recommendation system and infrastructure.
Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
This video will give you an idea about Data science for beginners.
Also explain Data Science Process , Data Science Job Roles , Stages in Data Science Project
Data Analytics For Beginners | Introduction To Data Analytics | Data Analytic...Edureka!
Data Analytics for R Course: https://www.edureka.co/r-for-analytics
This Edureka Tutorial on Data Analytics for Beginners will help you learn the various parameters you need to consider while performing data analysis.
The following are the topics covered in this session:
Introduction To Data Analytics
Statistics
Data Cleaning and Manipulation
Data Visualization
Machine Learning
Roles, Responsibilities and Salary of Data Analyst
Need of R
Hands-On
Statistics for Data Science: https://youtu.be/oT87O0VQRi8
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Data science is different from Data Analytics,Data Engineering,Big Data.
Presentation about Data Science.
What is Data Science its process future and scope.
Data Science Presentation By Amit Singh.
"Sexiest job of 21st century"
In this presentation, I have talked about Big Data and its importance in brief. I have included the very basics of Data Science and its importance in the present day, through a case study. You can also get an idea about who a data scientist is and what all tasks he performs. A few applications of data science have been illustrated in the end.
This lecture gives various definitions of Data Mining. It also gives why Data Mining is required. Various examples on Classification , Cluster and Association rules are given.
The slide aids to understand and provide insights on the following topics,
* Overview for Data Science
* Definition of Data and Information
* Types of Data and Representation
* Data Value Chain - [ Data Acquisition; Data Analysis; Data Curating; Data Storage; Data Usage ]
* Basic concepts of Big Data
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
The Data Lake and Getting Buisnesses the Big Data Insights They NeedDunn Solutions Group
Do terms like "Data Lake" confuse you? You’re not alone. With all of the technology buzzwords flying around today, it can become a task to keep up with and clearly understand each of them. However a data lake is definitely something to dedicate the time to understand. Leveraging data lake technology, companies are finally able to keep all of their disparate information and streams of data in one secure location ready for consumption at any time – this includes structured, unstructured, and semi-structured data. For more information on our Big Data Consulting Services, don’t hesitate to visit us online at: http://bit.ly/2fvV5rR
A Key to Real-time Insights in a Post-COVID World (ASEAN)Denodo
Watch full webinar here: https://bit.ly/2EpHGyd
Presented at Data Champions, Online Asia 2020
Businesses and individuals around the world are experiencing the impact of a global pandemic. With many workers and potential shoppers still sequestered, COVID-19 is proving to have a momentous impact on the global economy. Regardless of the current situation and post-pandemic era, real-time data becomes even more critical to healthcare practitioners, business owners, government officials, and the public at large where holistic and timely information are important to make quick decisions. It enables doctors to make quick decisions about where to focus the care, business owners to alter production schedules to meet the demand, government agencies to contain the epidemic, and the public to be informed about prevention.
In this on-demand session, you will learn about the capabilities of data virtualization as a modern data integration technique and how can organisations:
- Rapidly unify information from disparate data sources to make accurate decisions and analyse data in real-time
- Build a single engine for security that provides audit and control by geographies
- Accelerate delivery of insights from your advanced analytics project
Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
This video will give you an idea about Data science for beginners.
Also explain Data Science Process , Data Science Job Roles , Stages in Data Science Project
Data Analytics For Beginners | Introduction To Data Analytics | Data Analytic...Edureka!
Data Analytics for R Course: https://www.edureka.co/r-for-analytics
This Edureka Tutorial on Data Analytics for Beginners will help you learn the various parameters you need to consider while performing data analysis.
The following are the topics covered in this session:
Introduction To Data Analytics
Statistics
Data Cleaning and Manipulation
Data Visualization
Machine Learning
Roles, Responsibilities and Salary of Data Analyst
Need of R
Hands-On
Statistics for Data Science: https://youtu.be/oT87O0VQRi8
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Data science is different from Data Analytics,Data Engineering,Big Data.
Presentation about Data Science.
What is Data Science its process future and scope.
Data Science Presentation By Amit Singh.
"Sexiest job of 21st century"
In this presentation, I have talked about Big Data and its importance in brief. I have included the very basics of Data Science and its importance in the present day, through a case study. You can also get an idea about who a data scientist is and what all tasks he performs. A few applications of data science have been illustrated in the end.
This lecture gives various definitions of Data Mining. It also gives why Data Mining is required. Various examples on Classification , Cluster and Association rules are given.
The slide aids to understand and provide insights on the following topics,
* Overview for Data Science
* Definition of Data and Information
* Types of Data and Representation
* Data Value Chain - [ Data Acquisition; Data Analysis; Data Curating; Data Storage; Data Usage ]
* Basic concepts of Big Data
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
The Data Lake and Getting Buisnesses the Big Data Insights They NeedDunn Solutions Group
Do terms like "Data Lake" confuse you? You’re not alone. With all of the technology buzzwords flying around today, it can become a task to keep up with and clearly understand each of them. However a data lake is definitely something to dedicate the time to understand. Leveraging data lake technology, companies are finally able to keep all of their disparate information and streams of data in one secure location ready for consumption at any time – this includes structured, unstructured, and semi-structured data. For more information on our Big Data Consulting Services, don’t hesitate to visit us online at: http://bit.ly/2fvV5rR
A Key to Real-time Insights in a Post-COVID World (ASEAN)Denodo
Watch full webinar here: https://bit.ly/2EpHGyd
Presented at Data Champions, Online Asia 2020
Businesses and individuals around the world are experiencing the impact of a global pandemic. With many workers and potential shoppers still sequestered, COVID-19 is proving to have a momentous impact on the global economy. Regardless of the current situation and post-pandemic era, real-time data becomes even more critical to healthcare practitioners, business owners, government officials, and the public at large where holistic and timely information are important to make quick decisions. It enables doctors to make quick decisions about where to focus the care, business owners to alter production schedules to meet the demand, government agencies to contain the epidemic, and the public to be informed about prevention.
In this on-demand session, you will learn about the capabilities of data virtualization as a modern data integration technique and how can organisations:
- Rapidly unify information from disparate data sources to make accurate decisions and analyse data in real-time
- Build a single engine for security that provides audit and control by geographies
- Accelerate delivery of insights from your advanced analytics project
Introduction to Big Data
Big Data is a massive collection of data that is growing exponentially over time.
It is a data set that is so large and complex that traditional data management tools cannot store or process it efficiently.
Big data is a type of data that is extremely large in size.
Joe Caserta presents his vision of the future of Big Data in the Enterprise.
At the recent Harrisburg University Analytics Summit II, Joe Caserta gave this engaging presentation to Summit attendees including fellow academics, strategists, data scientists and analysts.
Nov 2014 talk to SW Data Meetup by Mike Olson, co-founder and chairman of Cloudera.
In business, we often deal with hype around trends in society, politics, economy and technology. We know we need to take claims of the next big thing with a grain of salt and that we should be careful not to set expectations too high. However, with Big Data analytics, the opposite is true. The hype that accompanies it actually conceals the enormity of its impact on the way we do business. In this talk I’ll discuss how new 'Data Driven' economies are emerging through relentless innovation across the public and private sectors.
Mike (co-founded Cloudera in 2008 and served as its CEO until 2013 when he took on his current role of chief strategy officer (CSO.) As CSO, Mike is responsible for Cloudera’s product strategy, open source leadership, engineering alignment and direct engagement with customers. Prior to Cloudera Mike was CEO of Sleepycat Software, makers of Berkeley DB, the open source embedded database engine. Mike spent two years at Oracle Corporation as vice president for Embedded Technologies after Oracle’s acquisition of Sleepycat in 2006. Prior to joining Sleepycat, Mike held technical and business positions at database vendors Britton Lee, Illustra Information Technologies and Informix Software. Mike has a Bachelor’s and a Master’s Degree in Computer Science from the University of California, Berkeley.
The 20th annual Enterprise Data World (EDW) Conference took place in San Diego last month April 17-21. It is recognized as the most comprehensive educational conference on data management in the world.
Joe Caserta was a featured presenter. His session “Evolving from the Data Warehouse to Big Data Analytics - the Emerging Role of the Data Lake," highlighted the challenges and steps to needed to becoming a data-driven organization.
Joe also participated in in two panel discussions during the show:
• "Data Lake or Data Warehouse?"
• "Big Data Investments Have Been Made, But What's Next
For more information on Caserta Concepts, visit our website at http://casertaconcepts.com/.
How to build a successful data lake Presentation.pptxTarekHassan840678
A presentation by Waterline data on how to build a successful data lake. Listing all factors needed to take into consideration when building a new data lake
Assessing New Databases– Translytical Use CasesDATAVERSITY
Organizations run their day-in-and-day-out businesses with transactional applications and databases. On the other hand, organizations glean insights and make critical decisions using analytical databases and business intelligence tools.
The transactional workloads are relegated to database engines designed and tuned for transactional high throughput. Meanwhile, the big data generated by all the transactions require analytics platforms to load, store, and analyze volumes of data at high speed, providing timely insights to businesses.
Thus, in conventional information architectures, this requires two different database architectures and platforms: online transactional processing (OLTP) platforms to handle transactional workloads and online analytical processing (OLAP) engines to perform analytics and reporting.
Today, a particular focus and interest of operational analytics includes streaming data ingest and analysis in real time. Some refer to operational analytics as hybrid transaction/analytical processing (HTAP), translytical, or hybrid operational analytic processing (HOAP). We’ll address if this model is a way to create efficiencies in our environments.
Accelerate Cloud Migrations and Architecture with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3N46zxX
Cloud migration brings scalability and flexibility, and often reduced cost to organizations. But even after moving to the cloud, more often than not, organizational data can be found to be siloed, hard to access and lacking centralized governance. That leads to delay and often missed opportunities in value creation from enterprise data. Join Amit Mody, Senior Manager at Accenture, in this keynote session to learn why current physical data architectures are hindrance to value creation from data, what is a logical data fabric powered by data virtualization and how a logical data fabric can unlock the value creation potential for enterprises.
How do you balance the need for structured and rule-based governance to assure enterprise data quality - with the imperative to innovate in order to stay relevant and competitive in today's business marketplace?
At the recent CDO Summit in NYC, a range of C-Level Executives across a variety of industries came to hear Joe Caserta, president of Caserta Concepts, put it all in perspective.
Joe talked about the challenges of "data sprawl" and the paradigm shift underway in the evolving big data and data-driven world.
For more information or to contact us, visit http://casertaconcepts.com/
presentation on data mining for b.tech student or other . This topic is about data mining you can give in seminar and it is easy to edit and it look like made own . You can study from is ppt all important topic is give like (content, definition, techniques, kcc and so on.
Machine Learning 2 deep Learning: An IntroSi Krishan
Provides a brief introduction to machine learning, reasons for its popularity, a simple walk through example and then a need for deep learning and some of its characteristics. This is an updated version of an earlier presentation.
Continues with Excel basics giving information on cell addressing styles and worksheet functions and their nesting. Also gives an example of precision setting
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
20. Data
Mart
• A
Data
Mart
is
a
smaller,
more
focused
Data
Warehouse
–
a
mini-‐warehouse.
• A
Data
Mart
typically
reflects
the
business
rules
of
a
specific
business
unit
within
an
enterprise.
29. DBMS,
OLAP,
and
Data
Mining
DBMS OLAP Data Mining
Task
Extraction of detailed
and summary data
Summaries, trends and
forecasts
Knowledge
discovery of
hidden patterns
and insights
Type of result Information Analysis
Insight and
Prediction
Method
Deduction (Ask the
question, verify
with data)
Multidimensional data
modeling,
Aggregation,
Statistics
Induction (Build the
model, apply it
to new data, get
the result)
Example question
Who purchased
mutual funds in
the last 3 years?
What is the average
income of mutual
fund buyers by
region by year?
Who will buy a
mutual fund in
the next 6
months and
why?
42. Association
Model:
Application
1
• Marke+ng
and
Sales
Promo+on:
• Let
the
rule
discovered
be
{Bagels,
…
}
-‐-‐>
{Potato
Chips}
• Potato
Chips
as
consequent
=>
Can
be
used
to
determine
what
should
be
done
to
boost
its
sales.
• Bagels
in
the
antecedent
=>
Can
be
used
to
see
which
products
would
be
affected
if
the
store
discon+nues
selling
bagels.
• Bagels
in
antecedent
and
Potato
chips
in
consequent
=>
Can
be
used
to
see
what
products
should
be
sold
with
Bagels
to
promote
sale
of
Potato
chips!
43. Association
Model:
Application
2
• Supermarket
shelf
management.
• Goal:
To
iden+fy
items
that
are
bought
together
by
sufficiently
many
customers.
• Approach:
Process
the
point-‐of-‐sale
data
collected
with
barcode
scanners
to
find
dependencies
among
items.
• A
classic
rule
-‐-‐
• If
a
customer
buys
diaper
and
milk,
then
he
is
very
likely
to
buy
beer.
• So,
don’t
be
surprised
if
you
find
six-‐packs
stacked
next
to
diapers!
45. ClassiDication:
Application
1
• Direct
Marke+ng
• Goal:
Reduce
cost
of
mailing
by
targe4ng
a
set
of
consumers
likely
to
buy
a
new
cell-‐phone
product.
• Approach:
• Use
the
data
for
a
similar
product
introduced
before.
• We
know
which
customers
decided
to
buy
and
which
decided
otherwise.
This
{buy,
don’t
buy}
decision
forms
the
class
aQribute.
• Collect
various
demographic,
lifestyle,
and
company-‐interac+on
related
informa+on
about
all
such
customers.
• Type
of
business,
where
they
stay,
how
much
they
earn,
etc.
• Use
this
informa+on
as
input
aDributes
to
learn
a
classifier
model.
46. ClassiDication:
Application
2
• Fraud
Detec+on
• Goal:
Predict
fraudulent
cases
in
credit
card
transac+ons.
• Approach:
• Use
credit
card
transac+ons
and
the
informa+on
on
its
account-‐
holder
as
aDributes.
• When
does
a
customer
buy,
what
does
he
buy,
how
oqen
he
pays
on
+me,
etc
• Label
past
transac+ons
as
fraud
or
fair
transac+ons.
This
forms
the
class
aDribute.
• Learn
a
model
for
the
class
of
the
transac+ons.
• Use
this
model
to
detect
fraud
by
observing
credit
card
transac+ons
on
an
account.
47. ClassiDication:
Application
3
• Customer
ADri+on/Churn:
• Goal:
To
predict
whether
a
customer
is
likely
to
be
lost
to
a
compe+tor.
• Approach:
• Use
detailed
record
of
transac+ons
with
each
of
the
past
and
present
customers,
to
find
aDributes.
• How
oqen
the
customer
calls,
where
he
calls,
what
+me-‐of-‐the
day
he
calls
most,
his
financial
status,
marital
status,
etc.
• Label
the
customers
as
loyal
or
disloyal.
• Find
a
model
for
loyalty.
49. Clustering:
Application
1
• Market
Segmenta+on:
• Goal:
subdivide
a
market
into
dis+nct
subsets
of
customers
where
any
subset
may
conceivably
be
selected
as
a
market
target
to
be
reached
with
a
dis+nct
marke+ng
mix.
• Approach:
• Collect
different
aDributes
of
customers
based
on
their
geographical
and
lifestyle
related
informa+on.
• Find
clusters
of
similar
customers.
• Measure
the
clustering
quality
by
observing
buying
paDerns
of
customers
in
same
cluster
vs.
those
from
different
clusters.
50. Clustering:
Application
2
• Document
Clustering:
• Goal:
To
find
groups
of
documents
that
are
similar
to
each
other
based
on
the
important
terms
appearing
in
them.
• Approach:
To
iden+fy
frequently
occurring
terms
in
each
document.
Form
a
similarity
measure
based
on
the
frequencies
of
different
terms.
Use
it
to
cluster.
• Gain:
Informa+on
Retrieval
can
u+lize
the
clusters
to
relate
a
new
document
or
search
term
to
clustered
documents.
60. Thank
you
for
Viewing
my
Presenta+on
For
ques+ons,
you
can
contact
me
at
iksinc@yahoo.com
Also
visit
my
blog
“From
Data
to
Decisions”
at
iksinc.wordpress.com