This document discusses data integration and transformation. It defines data integration as combining data from multiple sources into a coherent data store, such as a data warehouse. There are two major approaches for data integration: tight coupling, where data is combined from sources into a single location through ETL; and loose coupling, where an interface queries source databases directly. Data transformation prepares data for mining through techniques like smoothing, aggregation, generalization, normalization and attribute construction. Normalization techniques discussed include min-max, z-score, and decimal scaling normalization.
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.
Even though exploring data visually is an integral part of the data analytic pipeline, we struggle to visually explore data once the number of dimensions go beyond three. This talk will focus on showcasing techniques to visually explore multi dimensional data p 3. The aim would be show examples of each of following techniques, potentially using one exemplar dataset. This talk was given at the Strata + Hadoop World Conference @ Singapore 2015 and at Fifth Elephant conference @ Bangalore, 2015
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.
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This presentation gives the idea about Data Preprocessing in the field of Data Mining. Images, examples and other things are adopted from "Data Mining Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei "
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.
Even though exploring data visually is an integral part of the data analytic pipeline, we struggle to visually explore data once the number of dimensions go beyond three. This talk will focus on showcasing techniques to visually explore multi dimensional data p 3. The aim would be show examples of each of following techniques, potentially using one exemplar dataset. This talk was given at the Strata + Hadoop World Conference @ Singapore 2015 and at Fifth Elephant conference @ Bangalore, 2015
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
This presentation gives the idea about Data Preprocessing in the field of Data Mining. Images, examples and other things are adopted from "Data Mining Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei "
This presentation briefly discusses about the following topics:
Data Analytics Lifecycle
Importance of Data Analytics Lifecycle
Phase 1: Discovery
Phase 2: Data Preparation
Phase 3: Model Planning
Phase 4: Model Building
Phase 5: Communication Results
Phase 6: Operationalize
Data Analytics Lifecycle Example
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
This presentation introduces big data and explains how to generate actionable insights using analytics techniques. The deck explains general steps involved in a typical analytics project and provides a brief overview of the most commonly used predictive analytics methods and their business applications.
Vijay Adamapure is a Data Science Enthusiast with extensive experience in the field of data mining, predictive modeling and machine learning. He has worked on numerous analytics projects ranging from healthcare, business analytics, renewable energy to IoT.
Vijay presented these slides during the Internet of Everything Meetup event 'Predictive Analytics - An Overview' that took place on Jan. 9, 2015 in Mumbai. To join the Meetup group, register here: http://bit.ly/1A7T0A1
What’s The Difference Between Structured, Semi-Structured And Unstructured Data?Bernard Marr
There are three classifications of data: structured, semi-structured and unstructured. While structured data was the type used most often in organizations historically, artificial intelligence and machine learning have made managing and analysing unstructured and semi-structured data not only possible, but invaluable.
This is the 3- Tier architecture of Data Warehouse. This is the topic under Data Mining subject. Data mining is extracting knowledge from large amount of data.
North Raleigh Rotarian Katie Turnbull gave a great presentation at our Friday morning extension meeting about data visualization. Katie is a consultant at research and advisory firm, Gartner, Inc.
Our regular Introduction to Data Management (DM) workshop (90-minutes). Covers very basic DM topics and concepts. Audience is graduate students from all disciplines. Most of the content is in the NOTES FIELD.
This presentation briefly discusses about the following topics:
Data Analytics Lifecycle
Importance of Data Analytics Lifecycle
Phase 1: Discovery
Phase 2: Data Preparation
Phase 3: Model Planning
Phase 4: Model Building
Phase 5: Communication Results
Phase 6: Operationalize
Data Analytics Lifecycle Example
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
This presentation introduces big data and explains how to generate actionable insights using analytics techniques. The deck explains general steps involved in a typical analytics project and provides a brief overview of the most commonly used predictive analytics methods and their business applications.
Vijay Adamapure is a Data Science Enthusiast with extensive experience in the field of data mining, predictive modeling and machine learning. He has worked on numerous analytics projects ranging from healthcare, business analytics, renewable energy to IoT.
Vijay presented these slides during the Internet of Everything Meetup event 'Predictive Analytics - An Overview' that took place on Jan. 9, 2015 in Mumbai. To join the Meetup group, register here: http://bit.ly/1A7T0A1
What’s The Difference Between Structured, Semi-Structured And Unstructured Data?Bernard Marr
There are three classifications of data: structured, semi-structured and unstructured. While structured data was the type used most often in organizations historically, artificial intelligence and machine learning have made managing and analysing unstructured and semi-structured data not only possible, but invaluable.
This is the 3- Tier architecture of Data Warehouse. This is the topic under Data Mining subject. Data mining is extracting knowledge from large amount of data.
North Raleigh Rotarian Katie Turnbull gave a great presentation at our Friday morning extension meeting about data visualization. Katie is a consultant at research and advisory firm, Gartner, Inc.
Our regular Introduction to Data Management (DM) workshop (90-minutes). Covers very basic DM topics and concepts. Audience is graduate students from all disciplines. Most of the content is in the NOTES FIELD.
Characterizing and Processing of Big Data Using Data Mining TechniquesIJTET Journal
Abstract— Big data is a popular term used to describe the exponential growth and availability of data, both structured and unstructured. It concerns Large-Volume, Complex and growing data sets in both multiple and autonomous sources. Not only in science and engineering big data are now rapidly expanding in all domains like physical, bio logical etc...The main objective of this paper is to characterize the features of big data. Here the HACE theorem, that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective, is used. The aggregation of mining, analysis, information sources, user interest modeling, privacy and security are involved in this model. To explore and extract the large volumes of data and useful information or knowledge respectively is the most fundamental challenge in Big Data. So we should have a tendency to analyze these problems and knowledge revolution.
Just finished a basic course on data science (highly recommend it if you wish to explore what data science is all about). Here are my takeaways from the course.
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This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
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The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
2. Data Integration
* Data Integration involves combining data from several
disparate source, which are stored using various technologies
and provide a unified view of the data.
* The later initiative is often called a data warehouse.
* It merges the data from multiple data stores (data
source).
* It includes multiple databases, data cubes or flat
files.
* Metadata, correlation analysis, data conflict detection
and resolution of semantic heterogeneity contribute towards
smooth data integration.
3. Data Integration Define :
It combines data from multiple sources into a coherent
data store, as in data warehousing. These sources may include
multiple databases, data cubes, or flat files.
The data integration systems are formally defined as triple<G,S,M>
Where G: The global schema
S:Heterogeneous source of schemas
M: Mapping between the queries of source and global
schema
4. Advantages :
1. Independence.
2. Faster query processing.
3. Complex query processing.
4. Advanced data summarization & storage possible.
5. High volume data processing.
Disadvantages :
1. Latency (since data needs to be loaded using ETL).
2. Costlier (data localization, infrastructure, security).
6. Data Integration Approach:
There are mainly 2 major approaches for data
integration – one is “tight coupling approach” and another is
“loose coupling approach”.
Tight Coupling:
• Here, a data warehouse is treated as an information retrieval component.
• In this coupling, data is combined from different sources into a single physical
location through the process of ETL – Extraction, Transformation and Loading.
Loose Coupling:
• Here, an interface is provided that takes the query from the user, transforms it in a
way the source database can understand and then sends the query directly to the
source databases to obtain the result.
• And the data only remains in the actual source databases.
7. There are a number of issues to consider during data integration.
1. Schema Integration.
2. Redundancy.
3. Detection and resolution of data value conflicts.
Schema integration :
The real-world entities from multiple source be matched
is referred to as the entity identification problem.
For example,
Data analyst or the computer be sure that customer_id in
one database and cust_number in another refer to the same
entity. Databases and data warehouses that is a data about the
data it’s a meta data.
8. Redundancy :
* It is another important issue.
* An attribute may be redundant if it can be “derived”
from another table, such as annual revenue.
* Some redundancies can be detected by correlation
analysis.
For example,
Two attributes, such analysis can measure how
strongly one attribute implies the other based on the
available data.
The correlation between attributes attribute A and Bby
9. Detection and resolution of data value conflicts :
* A third important issue in data integration is the
detection and resolution of data value conflicts.
* The same real-world entity, attribute values from
different sources. This may be due to differences in
representation, scaling, or encoding.
* An attribute in one system may be recorded at a
lower level of abstraction than the “same” attribute in another.
* For example, the total sales in one database may
refer to one branch of All Electronics, an attribute of the same
name in another database may refer to the total sales for All
Electronics stores in a given region.
10. Data Transformation
* Data transformation the data are transformed or
consolidated into forms in appropriate for mining.
* Data transformation can involve
1. Smoothing.
2. Aggregation.
3. Generalization.
4. Normalization.
5. Attribute construction.
Such
Smoothing :
Which works to remove the noise from data.
techniques include binning, clustering and regression.
11. Aggregation :
* Where summary or aggregation operations are applied to
the data.
* For example, the daily sales data may be aggregated so
as to compute monthly and annual total amounts.
Generalization :
* The data where low-level or “primitive” data are placed
by higher-level concepts through the use of concept through
the use of concept hierarchies.
* For example, the attributes like street can be generalized
to higher-level concept city or country when the numeric
attributes to higher-level concept young, middle- aged and
street.
12. Normalization :
Where the attribute data are scaled so as to fall within
a specified range, such as -1.0 to 1.0 or 0.0 to 1.0
Attribute construction :
Where new attribute are a constructed and added
from the given set of attributes to help the mining
process.
There are many method for data normalization.
* Min-Max normalization.
* Z-Score normalization.
* Normalization by decimal scaling.
13. Min – Max Normalization :
It performs a linear transformation on the original data.
Suppose that min A and max A are the minimum and
maximum values of attributes A. A Min – Max
normalization maps a value v ofAto v’in the range.
Z – Score Normalization :
The Z – Score normalization a value of an attribute A
are normalized based on the mean and standard deviation of
A.Avalue v ofAis normalized to v’
14. Normalization by Decimal Scaling :
Normalization by decimal scaling normalizes by moving
the decimal point of values of attribute A.
The number of decimal points moved depends on the
maximum absolute value of A. A value v of A is normalized
to v’ by computing
where j is the smallest integer such that Max(|V’|) < 1.