1. Anurag Group of Institutions
International Conference on Advances in Computational Intelligence and Informatics
(ICACII – 2019)
Pre-Conference Workshop on
Data Analytics and Visualization
Anurag Group of Institutions

2. Data Analytics and Visualization Using R By Dr. A.V.Krishna Prasad
Associate Professor, CSE Dept, MVSR EC
Secretary, CSI Hyderabad Chapter

3. Importance of Topic Can you imagine a day without electricity?
Can you imagine a day without Computer / smart phone / mobile?
Can you imagine a day without Internet?
Can your imagine a day without doing Analysis / Analytics concept in any work?
Data Science & Analytics

4. Data Analytics / Data Science
Computer Science
Mathematical Statistics
Applications
One Programming
Language
Data Base Technologies AI ML
Data Mining
SPM
Real life applications
Insurance
Banking
Telecom Churn
Social Media
Stock Market
Financial account
Recommendation Systems
Linear Algebra
Probability
Statistics

5. Data Science refers to gain insights into data through computation, statistics, and visualization.
A Data Scientist Is... someone who knows more statistics than a computer scientist and more computer science than a statistician.”
- Josh Blumenstock
“Data Scientist = Statistician + Programmer +
Coach + Storyteller + Artist”.
- Shlomo Aragmon

6. The Data Scientist Hal Varian, Mckinsey Quarterly, January 2009:
“The sexy job in the next ten years will be statisticians… The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill.”
Ref:
“The critical job in the next 20 years will be the analytic scientist … the individual with the ability to understand a problem domain, to understand and know what data to collect about it, to identify analytics to process that data/information, to discover its meaning, and to extract knowledge from it—that’s going to be a very critical skill.”
- Kaisler, Armour, Espinosa, Money (2014) Amended
For both roles:
Analytic scientists require advanced training in specific domains, data science tools, multiple analytics, and visualization to perform predictive and prescriptive analytics. They may hold Ph.D.’s, but pragmatic experience in a domain will be equally important.

7. Data Scientist - Characteristics
● Quantitative skill:
such as mathematics or statistics
● Technical aptitude:
namely, software engineering, machine learning, and programming skills
● Skeptical mind-set and critical thinking:
It is important that data scientists can examine their work
critically rather than in a one-sided way.
● Curious and creative:
Data scientists are passionate about data and finding creative ways to solve problems and portray information.
● Communicative and collaborative:
Data scientists must be able to articulate the business value in a clear way and collaboratively work with other groups, including project sponsors and key stakeholders.

8. Example of Big Data Analytics
After analyzing consumer purchasing behavior, Target’s statisticians determined that the retailer made a great deal of money from three main life-event situations.

9. Example of Big Data Analytics Problem
After analyzing consumer purchasing behavior, Target’s statisticians determined that the retailer made a great deal of money from three main life-event situations.
● Marriage, when people tend to buy many new products
● Divorce, when people buy new products and change their spending habits
● Pregnancy, when people have many new things to buy and have an urgency to buy them

10. Analysis & Analytics
Data analysis is the process by which data becomes understanding, knowledge and insight.
Analytics is a process of inspecting, cleaning, transforming, and modelling big data with the goal of discovering useful information, suggesting conclusions, and supporting decision making.

11. Differences b/n Analytics and Analysis
Data Analysis term represent where you have collection of data to be analyzed.
Data can be collected from various sources and then converting that
Data into useful information. It can also be represented as pattern of data in the form of chart, bar graph through data visualization.
Data Analytics is a technology platform where all data models are applied over data to get better insights.
Machine learning, predictive analytics etc among are all the platforms.
Analysis – Knowledge expert, skilled person, domain knowledge required to do decision making.
Analytics – Naive user – Automating the decision making process.

12. Analytics •Connection to data mining
–Analytics include both data analysis (mining) and communication (guide decision making)
–Analytics is not so much concerned with individual analyses or analysis steps, but with the entire methodology
Analytics should act like an Extra Brain / Extra Eye / Extra ear / Extra Sensor like Sixth sense to an Organization.
It’s an Visualization Tool (Dashboard)

13. Data Storage Terminology

14. Data
Data - are encodings that represent the qualitative or quantitative attributes of a variable or set of variables.
Data - Numeric, Character, Integer, Real, Rational, Discrete, continuous, Binary, Interval Variable, Scaled , ordinal, rational
Catagorical ….
Data – Univariate, Bivariate, Multi Variate , --
Recent Data Trends - RFID Data, Web Term Data, Sensor Array Data, Gene Expression Data, Consumer Preference Data, Symbols,
Social Media data etc.
Emoticons - Smiley, Angry

15. Viewing the Data Data – Object type Array List Table Matrix Vector
Data Frame
One Dimensional
Two Dimensional
Multi-Dimensional

16. Multi-Dimensional Data
Example of two-dimensional query.
What is the total revenue generated by property sales in each city, in each quarter of 1997?’
Choice of representation is based on types of queries end-user may ask.
Compare representation - three-field relational table versus two-dimensional matrix.

17. Multi-Dimensional Data as Three-Field Table versus Two-Dimensional Matrix

18. Multi-Dimensional Data as Three-Field Table versus Two-Dimensional Matrix

19. Multi-Dimensional Data
Example of three-dimensional query.
‘What is the total revenue generated by property sales for each type of property (Flat or House) in each city, in each quarter of 1997?’
Compare representation - four-field relational table versus three-dimensional cube.

20. Multi-Dimensional Data as Four-Field Table versus Three-Dimensional Cube

21. Multi-Dimensional Data as Four-Field Table versus Three-Dimensional Cube

22. Types of Analytics

23. Analytics on what
Parameters - Identification of Parameters is most important while
doing analytics.
MOST – Mission, Objective that help to achieve the mission,
Strategies to move forward and Techniques to implement
strategies
PESTELE – Factors like Political, Economical, Socialogical,
Technical, Legal and Environment.
SWOT – Strengths, Weaknesses, Opportunities and Threats
CATWOE – Customers, Actors, Transformation process, World
view, Owner, Environmental constraints.

24. Types of Analytics A to Z Analytics High Dimensional Analytics
Big Data Analytics
Cloud – Analytics,
IOT – Analytics
Business Analytics
Outsourced Data Analytics, Crowd Sourcing Data Analytics
Smart Analytics, Video Analytics
Web Analytics, Mobile Analytics, Predictive Analytics, movie analytics, Social Analytics
In-Memory Analytics, Cognitive Analytics, Self Analytics SA

26. Python….-
< R….-:

27. Web References https://cran.r-project.org/ (For Software –R)
(For R studio – GUI)
(For Basic Learners)
(For Scripts)
(For Advanced level – Mining, Machine Learning..)

28. General Programming Components
Program Structure, Syntax, Semantics, Execution part
Comments, Data Storage Elements, Data Types, I/O Statements, Control Statements, Arrays, Structures, Functions, Files etc.

29. Implementation in R R Programming PPT Descriptive statistics
Visualization
Predictive statistics

30. SOCIAL MEDIA ANALYTICS

31. SOCIAL MEDIA

32. SOCIAL MEDIA ANALYSIS
Social Media Analysis has become some kind of new passion in Marketing. Every company wants to engage existing customers or attract new ones through this communication channel.
"Without data you are just another man with an opinion.” - Edward Deming

33. Social Media Analytics
1. Twitter Analytics
2. Face book Analytics
3. Google Analytics
4. Web Analytics
Tools
Tracking and reporting social media analytics used to be a hurdle for digital marketers – now the problem is finding the ideal tool.

34. List of Social Media Analytical Tools
1. Keyhole
12. NetBase
2. AgoraPulse
13. Oktopost
3. Brandwatch
14. quintly
4. Buffer
15. Rival IQ
5. BuzzSumo
16. Salesforce Marketing Cloud
6. Crowdbooster
7. Edgar
17. Socialbakers
8. Google Analytics
18. Iconosquare (Instagram)
9. Hootsuite
10. Klout
19. SocialBro (Twitter)
11. Little Bird
20. TweetReach (Twitter)

35. Some Important Works My Publications:
Web Analytics
Mining on Social Media
Social Media Monitoring tools in R
Social Network Analysis with R using Package igraph
Social Media Lab

36. Social media Analytics plays a vital role in this competitive world
Social media Analytics plays a vital role in this competitive world. If you are thorough with Analytics,
You can play with social media. Social media is in your control.
Live examples – Modi Winning Strategy, Trump winning Strategy all are depending on media.

37. 1. Business Understanding
Steps in Analytical Process
1. Business Understanding
Identify the Business objective
Assess the situation
Determine the Analytical goals
Produce a project plan
2. Data Understanding
Collect the data
Describe the data
Explore the data
Verify the data Quality
3. Data Preparation
Select the data
Clean the data
Construct the data
Integrate the data
Format the data
4. Modeling
Select a modeling technique
Generate a test Design
Build a model
Assess a model
5. Evaluation
Evaluate the results
Review the process
Determine the next steps
6. Deployment
Deploying the plan
Monitoring and maintenance of the plan
Producing the final report
Reviewing the project
Business Value
Data
Modeling
Evaluation 5. Deployment
ADDING BUSINESS
VALUE 37

38. Github – Twitter Sentiment Analysis

39. FACE BOOK ANALYTICS Developers.facebook.com ( To get access token )
Required libraries in R
Rfacebook, httpuv, Rjson, Rcolorbrewer, Rcurl

40. FACE BOOK ANALYTICS / Linked in
How many of my friends are single, married, youth, agegroup etc
To find the relationship based on location, age group, gender, education, skillset, profession, groups etc.

41. Twitter Analytics using R
Public handles
#namo, #kejri wal, #ipl, #climate #nba
Packages used :
twitteR - Provides Access to Twitter Data
tm – Provides functions for Text Processing
wordcloud – visualize the results with wordcloud
Extract Tweets from Twitter.
Text Processing
Apply Analytical Methods
Visualization

42. Wordcloud

43. https://dev.twitter.com/
Consumer Key and Consumer Secret number , Access Token
Authentication key and number
Text Processing:

44. Twitter Analytics using R
R packages used in Social Media Lab (planned)
twitteR (for collecting Twitter data)
tm (text mining)
wordcloud (text word clouds)
RTextTools (machine learning package for automatic text classification)
igraph (network analysis and visualization)
RCurl (collecting WWW data)
XML (reading and creating XML documents)
R.utils (programming utilities)
ape and dendextend (dendo grams, hierarchical clustering)
FactoMineR and homals (multiple correspondence analysis)
plyr and stringr (text sentiment analysis

45. Some recent ML Competitions at https://www.kaggle.com/
The Home of Data Science & Machine Learning
Kaggle helps you learn, work, and play

46. ESSENTIALS OF MACHINE LEARNING ALGORITHMS
Using Python and R
Essentials
Linear Regression
Logistic Regression
Decision Tree
SVM
Naive Bayes
KNN
K-Means
Random Forest
Dimensionality Reduction Algorithms
Supervised Learning
Unsupervised Learning
Reinforcement Learning

47. DATA VISUALIZATION IN R
Basic Visualization
Histogram
Bar / Line Chart
Box plot
Scatter plot
Advanced Visualization
Heat Map
Mosaic Map
Map Visualization
3D Graphs
Correlogram
Demo(graphics) for Demo in R tool
Package : HistData,
Library – ggplot2, RColorBrewer

48. Sample Works Forecasting on $ Rate Forecasting on Ionoshperic data
(R Commands for plotting, Coloring, Graph Labeling)
3. Weather Forecasting

49. Searching based on human thoughts
Key word based Search
Keep Track of searching's
Search based on previous history
Analyze human search behavior pattern
Produce the Results
At least 40 % possible
Add AI Intelligence, Fuzzy, Neural, Semantic Web, Cognitive

50. Time Series Data Model - Forecasting Methods
(BITCOIN –Data Set)
Method 1 – Start with a Naive Approach
Method 2 – Simple average
Method 3 – Moving average
Method 4 – Single Exponential smoothing
Method 5 – Holt’s linear trend method
Method 6 – Holt’s Winter seasonal method
Method 7 – ARIMA
Weather Forecasting, Stock Market predictions,
Food Grain Prediction

52. IBM Watson Analytical Tool
Developer.twitter.com
Developer.google.com
Developer.ibm
API
Amazon Web services

53. “Life Long Loyal Love for Learning”
The purpose of LIFE is
“Awakening with in You and Awakening the Others”
“Life Long Loyal Love for Learning”