BIG DATA and DATA ANALYTICS

(1)

Oleh: Tim Dosen

(2)

1. RPS / Course Plan (13 weeks)

2. Lab Activity (week 3-week 12)

3. Grade Percentage TASK1:UTS:TASK2:UAS -> 25%:25%:25%:25%

4. Class Rules : Be Active, On Time (Before Time)

5. Class Coordinator (pick one)

6. Lab Activity using the following tools : R Language (R Studio),

Orange, Weka, RapidMiner, Gephi

(3)

1. Understand conceptual, framework, opportunity and challenge of

Big Data

2. Understand concept, theory, framework from Data Analytics

activities

3. Able to choose and perform Data Analytics activities based on the

contextual business problem

4. Able to build description model and prediction model using

available data

(4)

o

Introduction, Background & Definitions

o

Data Driven Decision Making

o

Big Data Properties

o

Big Data Complexity

o

Big Data Framework and State of the Art

o

Big Data for Business

o

Case Study

(5)

(6)

(7)

o

We are generating huge amounts of data (from UGC to mobile habit)

o

Our society is leaving behind a digital footprint (so our behavior /

attitudes)

o

Finding unexpected pattern is so exciting (also useful for predictive

analytics)

o

The need to find the usage of Large Scale of Data Warehouse

(internal data)

(8)

(9)

o

Big Data : It is a term for data sets that are so large or complex that

traditional data processing tools are inadequate to process. The

challenges include analysis, capture, data curation, search, sharing,

storage, transfer, visualization, querying, updating and information

privacy (wikipedia)

o

Data Analytics : It is the science of examining raw data with the

purpose of drawing conclusions about that information. Data

Analytics is used in may industries to allow companies and

organization to make better business decisions and in the sciences

to verify or disprove existing models or theories (wikipedia)

(10)

o

Social Computing: It is an area of computer science that is concerned

with the intersection of social behavior and computational systems.

It is based on creating or recreating social conventions and social

contexts through the use software and technology (wikipedia)

o

Data Science : It is interdisciplinary field about processes and system

to extract knowledge or insight from data in various forms, either

structured or unstructured. This field is continuation of some the

data analysis field such as statistics, data mining, and predictive

analytics(wikipedia)

(11)

Example (in a supermarket) :

1. Descriptive : Total Product A,

B, C, D sold. Retailer will know

which product are sold /

2. Predictive : People who buy

product A, mostly also buy

product B. Retailer know /

predict the future event

3. Prescriptive : Giving

recommendation what

(12)

Data Driven Decision Making

• Data science involves principles, processes, and techniques

for understanding phenomena via the (automated)

analysis of data

• The ultimate goal of data science as improving decision

making, as this generally is of direct interest to business

• Statistically, the more data-driven a firm is, the more

productive it is

—

even controlling for a wide range of

possible confounding factors. And the differences are not

small. One standard deviation higher on the DDD scale is

associated with a 4%

–

6% increase in productivity.

• DDD also is correlated with higher return on assets, return

on equity, asset utilization, and market value, and the

(13)

(14)

(15)

Big Data

(16)

Big Data

Analytics

(17)

Big Data Approach

Framework

Some people prefer 3Vs,

6Vs or 7Vs even 12Vs to

explain big data. But the

original “bigness”

measurement metrics

are volume, velocity,

and variety.

(18)

Big Data

State of

The Art

(19)

Big Data

for

(20)

Big Data

for

Business

• Given a set of transactions, find rules that will predict the occurrence of an

item based on the occurrences of other items in the transaction

Market-Basket transactions

TID

Items

1 Bread, Milk

2 Bread, Diaper, Beer, Eggs

3 Milk, Diaper, Beer, Coke

4 Bread, Milk, Diaper, Beer

5 Bread, Milk, Diaper, Coke

Example of Association Rules

{Diaper}



{Beer},

{Milk, Bread}



{Eggs,Coke},

{Beer, Bread}



{Milk},

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Big Data Use Case by Industry

Energy

Telecommunication

Retail

• Smart Meter Analytics

• Distribution Load Forecasting and

Scheduling

• Condition-Based Maintenance

• Network Performance

• New Products and Service Creation

• Call Detail Records (CDRs) Analysis

• Customer Relationship Management

• Dynamic Price Optimization

• Localized Assortment

• Supply-Chain Management

• Customer Relationship Management

Manufacturing

Banking

Insurance

• Supply Chain Management

• Customer Care Call Centre

• Preventive Maintenance and Repairs

• Customer Relationship Management

• Fraud Detection

• Trade Surveillance

• Compliance and Regulatory

• Customer Relationship Management

• Catastrophe Modeling

• Claims Fraud

• Reputation Management

• Customer Relationship Management

Public

Media

Healthcare

• Fraud Detection

• Fighting Criminality

• Threats Detection

• Cyber Security

• Large-Scale Clickstream Analytics

• Abuse and Click Fraud Prevention

• Social Graph Analysis and Profile

Segmentation

• Campaign Management and Loyalty

• Clinical Trials Data Analysis

• Patient Care Quality and Program

Analysis

• Supply Chain Management

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(24)

Business

Intelligence Vs

Data Science

Based on data analytics types, Data

Science’s practices focuses on

(25)

Case Study : Hurricane Frances

“Hurricane

Frances was on its way, barreling across the Caribbean, threatening a direct hit on

Florida’s

Atlantic coast. Residents made for higher ground, but far away, in Bentonville, Ark., executives at Wal-Mart

Stores decided that the situation offered a great opportunity for one of their newest data-driven weapons

BIG DATA and DATA ANALYTICS

Oleh: Tim Dosen

1.

RPS / Course Plan (13 weeks)

2.

Lab Activity (week 3-week 12)

3.

Grade Percentage TASK1:UTS:TASK2:UAS -> 25%:25%:25%:25%

4.

Class Rules : Be Active, On Time (Before Time)

5.

Class Coordinator (pick one)

6.

Lab Activity using the following tools : R Language (R Studio),

Orange, Weka, RapidMiner, Gephi

1.

Understand conceptual, framework, opportunity and challenge of

Big Data

2.

Understand concept, theory, framework from Data Analytics

activities

3.

Able to choose and perform Data Analytics activities based on the

contextual business problem

4.

Able to build description model and prediction model using

available data

o

Introduction, Background & Definitions

o

Data Driven Decision Making

o

Big Data Properties

o

Big Data Complexity

o

Big Data Framework and State of the Art

o

Big Data for Business

o

Case Study

o

We are generating huge amounts of data (from UGC to mobile habit)

o

Our society is leaving behind a digital footprint (so our behavior /

attitudes)

o

Finding unexpected pattern is so exciting (also useful for predictive

analytics)

o

The need to find the usage of Large Scale of Data Warehouse

(internal data)

o

Big Data : It is a term for data sets that are so large or complex that

traditional data processing tools are inadequate to process. The

challenges include analysis, capture, data curation, search, sharing,

storage, transfer, visualization, querying, updating and information

privacy (wikipedia)

o

Data Analytics : It is the science of examining raw data with the

purpose of drawing conclusions about that information. Data

Analytics is used in may industries to allow companies and

organization to make better business decisions and in the sciences

to verify or disprove existing models or theories (wikipedia)

o

Social Computing: It is an area of computer science that is concerned

with the intersection of social behavior and computational systems.

It is based on creating or recreating social conventions and social

contexts through the use software and technology (wikipedia)

o

Data Science : It is interdisciplinary field about processes and system

to extract knowledge or insight from data in various forms, either

structured or unstructured. This field is continuation of some the

data analysis field such as statistics, data mining, and predictive

analytics(wikipedia)

Example (in a supermarket) :

1. Descriptive : Total Product A,

B, C, D sold. Retailer will know

which product are sold /

popular