4 five numbers summary

www.reachoutanalytics.comwww.callforai.com
Advanced Data Science
(Machine Learning/AI)

Presenter Profile
K.Venkata Rao
IIMC, ISI
Experience:
Over 22 years of Experience Analytics
Projects & Case Studies :
294 projects completed from 2010 to till date

 Benz Mercedes Bangalore Data mining/Data Science 13th 14th
Feb 2020
 EY Bangalore Machine Learning with Python 6th Jan to 11th Jan
2020
 NMIM Business School Business Analytics 5th Jan 2020 to 23rd Feb 2020
 BVRIT Hyd Business Analytics with Tableau 2nd Nov 2019
 IARE Hyd Data Science with R 6th to 13th Sept 2019
 FORD INDIA Chennai Data Science with Python 9th April 2019
 IBM Bangalore Data Science with Python 25th 29th March 2019
 IBM Chennai Data Science with Python 18th 22th March
 2019
 IBM Pune SAS Advanced Analytics 7th to 11th 2019
 VISA Singapore Data Science with R 17th Dec to 21st Dec 2018
 ISB&M Pune Data Science with R and SAS 06th to 07th Dec 2018
 ISB&M Pune Data Science with R and SAS 26th to 27th Nov 2018
 Shriram Life Insurance Hyd Data Science Oct 10th to Nov 5th 2018
 VVIT Guntur Data Science with R Chief Guest for one day Workshop
7th Sept 2018
 EY Mumbai Advanced SAS Analytics 20th to 24th Aug 2018
 AON Bangalore Machine Learning, Data Science with R 06th Aug to
8th Aug 2018
 AON Gurgaon Data Science with R 24th July to 26th July 2018
 NIT Warangal FDP Data Science with R on May 3rd 4th
5th
 ISBM Pune Data Science with R and SAS 05th to 18th
April 018
 ISBM Pune Business Analytics 12th to 14th Dec 2017
 GENPACT Hyderabad Data Science with R Sept 10th to
13th 017
 ISBM Pune Business Analytics June 2017
 ANZ Bangalore SAS R Analytics Nov 28th 29th 2016
 Dr. Reddy Labs Hyderabad Excel R Data Science Deep Learning 21st 22nd Nov
 IISBM Pune R Business Analytics Oct 24th to 25th 2016
 BM Bangalore Data Science & Machine Learning with R SPSS Aug 29th 2016 to Sep
2nd 16
 Cognizant Bangalore SAS Business Analytics May 10th to June 10th
2016
 IBM Bangalore SAS R Business Analytics SAS Feb 22th to 24th
 Mastek Mumbai SAS Visual Analytics & Statistics 6th,7th Feb
2016
 ISBM Pune Data Science/Business Analytics 8th 15th Feb 16
 ISBM Pune Data Science/Business Analytics 11th 12th Jan
16
 ISBM Pune Data Science/Business Analytics 21st 22nd Dec
15
 IBM Bangalore Dec 2nd 2015 to Dec 5th 2015 on SAS
Analytics
 Deloitte SAS & R Business Analytics & Modelling SAS, R Sept 21st to Sept 25th
2015
 Deloitte SAS & R Business Analytics & Modelling Sept 7th to Sept 11th 2015
 Cognizant Advanced Data Analytics SAS Aug 05th to Sept 5th 2015
 IFF Business Intelligence and Statistical Modelling March 12th to 17th
2015 Cognizant Advanced Data Analytics 18th Aug to 6th sept. 2014
 Cognizant Advanced Data Analytics 23th July to 12th Aug 2014
 Cognizant Advanced Data Analytics 19th June to 10th July 2014
 IBM Bangalore Data Analysis with SAS Base 17th to 19th Feb 1014
 IBM Pune Data Scientist Training 27th to 29th Nov 2013
 DELL International 10th to 12th Oct 2013 Bangalore on SAS JMP Modelling
 GENPACT 23th to 27th Sept 2013 at Hyderabad SAS DI & Business
Intelligence tools IBM Bangalore 5th to 7th Aug 2013 Base SAS with predictive
modelling
 Met Life Insurance Noida, July 29th to Aug 2nd 2013 on SAS Base with Statistical
 Analysis
 HP Chennai June 3nd to 5th 2013 SAS BI Tools
Corporate Training: 147 corporate training delivered on Data Science – ML/AI-Business Analytics

1. IIM Bangalore AIMS 10th International Conference from January 6th to 9th of 2013 on “Elevating Performance Management System
through Knowledge Management by K. Venkata Rao and Anjali Rai ISBN 978-81- 924713-1-0.
2. IIM Ahmadabad 3rd International Conference on Advanced Data Analysis, Business Analytics and Intelligence April 13th to
14th of 2013, “f Customer Experience Adopting Mobile Banking Services in India" by K. Venkata Rao & Anjali Rai.
3. IIM Bangalore 1st international conference on Business Analytics and intelligence 11th -13th Dec 2013 “Customer
Experience adopting mobile banking services in india by K.Venkata rao Anjali Rai
4. IIT Roorkee, International Conference on Research and Sustainable Business" March 8th -9th , 2014 “ Assess Knowledge
Management Practices by Enabling Taxonomy in Software Firms “ by K.Venkata rao
5. IMT Ghaziabad Delhi AIMS 11th International Conference from December 21st to 24th of 2013 on “Consumer behavior in
Adopting Mobile Banking Services in India by K. Venkata Rao & Anjali Rai ISBN978-81-924713-1-0.
6. International Journal of Engineering and Management Research (IJEMR) by K. Venkata Rao and Anjali Rai
http://www.ijemr.net/DOC/AComprehensiveStudyOfEmotionalIntelligenc (405-411).pd. ISSN No. 2250-0758
7. IIM Bangalore 3st international conference on Business Analytics and intelligence 17th -19th Dec 2015 “A Study of
Customer buying behavior & E commerce: A Data mining Approach by K.Venkata rao B. Naveena Devi, Y. Rama Devi C.
Rajeswara Rao,
8. IIM Bangalore 3st international conference on Business Analytics and intelligence 17th -19th Dec 2015 “Elevating Cross
Functional Team for Knowledge Sharing In High Performing Indian Organization by K.Venkata rao Dr. Anjali Rai
9. Elevating Cross Functional team for Knowledge sharing in high performing Indian organization
http://www.vsrdjournals.com/pdf/VSRDIJBMR/2016_6_June/5_Anjali_Rai
_VSRDIJBMR_9859_Research_Paper_6_6_June_2016.pdf K.Venkata rao Dr. Anjali Rai
10. Customer Attitude estimates with Naive Bayes Machine Learning algorithm,1Nagarjun 2 Dr.S.Pazhanirajan, 3 Dr.T Anil
Kumar, 4 K. Venkata Rao,
Scopes China Conference Avvari Sai Saketh,
10-Publications & Articles-proceeding

Programme Highlights:
 Advanced Data Science Programme with Multiple
Tools Python-R -WEKA-SPSS-Excel-SAS
 5 Real time projects/cases studies

The Data Science AI/ML Lifecycle
Identify the
problem
Data
Preparation
Data
Exploration
Transform &
Select
Build Model
Validate
Model
Deploy
Model
Evaluate
Results
Business
Manager
Business
Analyst
IT Systems Data Scientist
(Artificial Intelligence/Machine Learning)

Data Science
Tools: Python-R-SPSS-SAS-JMP-Weka-Excel
Step1 :- Raise Relevant Business Questions
Step2 :- Frame the Hypothesis
Step3 :- Access &connect Relevant Data
Step4 :- Data Cleaning Data Validation Data Wrangling
Step5 :- EDA Data Analysis (Visualization Summarization(Y))
Step6 :- Frame the Hypothesis based on Described Data
Step7 :- Apply Machine Learning /AI
Step8 :- Summarization Visualization(T) Predicted information
Step9 :- Deployment Models
Step10 :- Feedback and monitoring

Step1 :- Raise Relevant Questions Identify the Business Problem
2020 Nov Apple launching 12 X pro Who will Buy ?
BIGBasket During COVID-19 Lockdown period who will buy Mutton how to
Increase ROI
YES Bank : Due Corona Economical crisis Who will become a Defaulters in 2020-21
predict them for more recovery
Beer & Diaper Walmart
“A number of convenience store clerks noticed that men often bought beer at the same time they
bought diapers.. So, the store began stocking diapers next to the beer coolers, and sales
Pregnancy prediction
Target developed a predictive model to predict who among their customers were parents-to-be. Used historical customer
purchasing patterns and other demographicsThey started sending out coupons and discount offers to such customers (sometimes
freaking them out as the customers themselves were unaware of the impending pregnancy).

Step2 :-formulate Business Problem into Data Science problem
H0 : 25 years Age people will buy
H1 : It may not : 25 years Age people will buy
H0 : Temporary Employees will do fraud significantly
H1 : It may not significantly
H0 : For immunity vegetarians will become Non veg 0.05 significant
H1 : it may not other reasons
H0 : Beers and Diapers for Adults or Kids 0.05 significant
H1 : it may not for Adults or Kids
H0 : for immunity vegetarians will become Non veg 0.05 significant
H1 : it may not for immunity vegetarians will become Non veg

Step3 :- Identify Relevant Data Source
 Data Source, Data Collection
 Connect with Meta data Servers (BSFI, Insurance)
 SQL, Data Warehousing
 No SQL Hadoop Systems

Data Science
Data
Information
Data
Information
Data
Information
Staging
Area
Legacy Data
Operational
Data
Flat
Files
Process of a Data Warehouse
Raw Data
Summary
Data
Meta Data
Data Warehouse

Data Science
Step4 :- Data Cleaning Data Validation Data Wrangling
(Data Transformation ,Data preparation)

Data Science
Target
Information
Fact Based
Decision
Making
AI/ML-Data Scientist
Summarize
Visualize
Predict
Estimate
Forecast
AI,ML/Data Science Insights
Python-R-SAS-Weka-SPSS-Excel

Data Science Tools Python
R Console
R Rattle
R Cmdr
WEKA
Ruby
Excel
SPSS
SAS Base
SAS Enterprise Guide
SAS Miner
JMP
Tableau
Spotfire
Power BI
IBM Watson
Open Source Tools
Commercial Tools
200 * tools are available for Data Science subject correction

1959-90 :Statistics
Business Question Easy Data Expensive
2010-2020: Machine Learning
1990-2010: Analytics
Business Question Not Easy Data is Not Expensive
Business Question Expensive Data is Easy
2020- Artificial Intelligence
Business Question Expensive Data is Massive
1970 to 2020 Journey Statistics to Data Scientist

1959-90 :Statistics :Time series
2010-2020: Machine Learning :Time series
1990-2010: Analytics :Time series
2020- Artificial Intelligence –Deep Learning
ARIMA (S plus)
ARIMA(R,Excel)
Stats models (Python)
RNN (TensorFlow, Keras )
1959-90 :Statistics : Regression
2010-2020: Machine Learning :Prediction
1990-2010: Analytics :Liner Regression
2020- Artificial Intelligence – Deep Learning
ARIMA (S plus)
ARIMA(R,Excel)
ANN (TensorFlow, Keras )
Forecasting Models
Functional Models
Classification Models
1959-90 :Statistics : Logistics Regression
2010-2020: Machine Learning :Prediction
1990-2010: Analytics :Non liner Regression
2020- Artificial Intelligence – Deep Learning
ARIMA (S plus)
ARIMA(R,Excel)
ANN (TensorFlow, Keras )
1970 to 2020 Journey Statistics to Data Scientist

Source : 4rd IIMA International Conference on Advanced Data Analysis & Business Analytics and Intelligence
Mathematics
CT
Dispersions
Mean
Median
Mode
Tri mean
Std
CV
IQR
Continues Distribution
Binomial Distribution
Exp Mean (MU)
Variance (sigma)
Statistics
Exp Mean (MU)
Variance (sigma)
Hypothesis
T TEst
Z test
F test
ANOVA
MANOVA
DW
Residual
Hypothesis
Chi-square
Wald Chi-Square
Pseudo R-
R-Square
Cox
Snell
Goodness-of-Fit
a+b1+x1*b2+e
1/(1+exp(-Reg)
Deep Learning
ANN CNN RNN
Regression
Logit Regression
Poisson Distribution
Discrete Distribution

1. Marketing Models
2. Internet Modeling and Web Analytics
3. Statistics in Finance
4. Marketing Research
5. Text Mining
6. Insurance Models
7. Advertising and Media
8. Revenue Management
9. Investment and Portfolio Models
10. Data Analysis in Retailing
11. Bioinformatics
12. Data Analysis in Banking and Financial
13. CRM Health Sciences
14. Risk Analytics
15. Pricing Analytics
16. Industrial Applications
17. Legal Analytics
18. Analytics for Strategy
19. Supply Chain Management
20. Analytics for Public Policy
21. Quality Management
22. Analytics for Environment
1. Exploratory Data Analysis
2. Classification Analysis
3. Operations Research
4. Cluster Analysis
5. Regression Modeling
6. Probability
7. and Stochastic Processes
8. Data Visualization
9. Pattern Recognition
10. Time Series Analysis
11. Machine Learning
12. Forecasting
13. Bayesian Methods
14. Computational Intelligence
15. Multivariate Analysis
Area of Analytics

VisualAnalytics
Histogram
Box Plot
Bar Chart
Pie Chart
Bubble Chart
Correlation Plot
Scatter Plot
Line Chart
Decision Tree
Cluster Charts
SummaryAnalytics
Data Type
Central Tendency
Dispersions
Five number
Distributions
Cross Tabulations
Reporting & Data Validation

Data Science
Step5 :- EDA Data Analysis (Visualization Summarization(Y))

Data Science
Summary Analytics(EDA)
Data Type
Continues
discrete
Ordinal
Nominal
Binary
Urinary
Mean
Mode
Median
Weight Mean
Geo mean
Hermean
Trimmed Mean
95% upper mean
95% Lower Mean
Std
CV (coefficient of
Variation)
Variation
Min
Max
Range
IQR – inter quartile
range
Skew SE
Kurt SE
Mean SE
Skew SE
Kurt SE
Mean SE
Q0 (0%) Min
Q1 (25%)
Q2 (50%) Median
Q3 (75%)
Q4 (100%) Max
5% percentile
95% percentile
Uniform Dsit
Normal Dist
Histogram
standaized plot
Stemleaf
Boxplot
Skwed dist
left skwed dist
right skwed dist
lipto kurtic
plato kurtic
massokurtic
PP plot
QQ plot
Central Tendency Dispersions
Five number

Step6 :- Apply Machine Learning /AI
1 Linear Regression
1.1 SLR/Multiple Regression
1.2 Dummy Regression
1.3 Transformed Regression
1.3.1 Sqrt Reg
1.3 2 Log10 Reg
1.3.3 Sqr(^2)
1.3.4 Quadric Reg
1.4 Stepwise Regression
1.5 Backward Regression
1.6 Forward Regression
2. Logistics Regession
2.1 Logit
2.2 Probit
2.3 Ordinal Regression
2.4 Multinomial Regression
2.5 Stepwise Regression
2.6 Backward Regression
2.7 Forward Regression
2.8.1 Transformed Reg
2.8.1 Sqrt Reg
2.8.2 Log10 Reg
2.8.3 Sqr(^2)
2.8.4 Quadric Reg
3 Decision Tree
3.1 GINI
3.2 Entropy
3.3 CHAID
3.4 CART
3.4 Prunned /Unpruned Tree (Weka)
4 Random Forestry
5 Navie Naysen
6. KNN
7 PCA
8 Cluster
9 Association Mining
10 Recommendation
11 SVM
12 ANN( deep Learning )
13 Timeseries
13.1 Navie model
13.2 Moving Averages
13.3 Weighted Moving Averages
13.4 Exponential Smoothing
14 Hazard Models
Supervised Learning
Unsupervised Learning
Semi Supervised Learning
Reinforcement Learning
Deep Learning
2020
48 Machine Learning
Algorithms

Summary Analytics
Data Type
Central Tendency
Dispersions
Five number
Distributions
Cross Tabulations

Data Science
Data Types
Numbers CharactersGeneral Terms
Matrices Non MatricesMathematical
Parametric Non ParametricStatistical
Fact DimensionsData warehousing
Quantitive/Tangible Qualitative/IntangibleAnalytics
SQL(Structure OLTP) No SQL(images,Test,pdf)Data Science
Structure(Target) Unstructured(Non-target)AI/ML

Data Science
Structure(Target) Unstructured(Non-target)AI/ML
Continues
Nominal
Discreet
SPSS: Scale
Ordinal
Urinary
Binary
SAS :intervalPython: MeasurableR: Measurable
Rating(1,2,3,4,5)
Education(1,2,3,4,5,)
Salary Range(1,2,3,4,5)
Location(1-Hyd,2-Blg,3-kol
Religion
Colors
Good/Bad(1,0
Yes,No (1,0)
Risk,Safe(1,0)
ADHAR-PAN-A/C, ID
Temp,
Wages,
Score,
No of CC ,
No of Childs
Count Books
Temp Wages Score
34.23 45,900.50 670
23.20 43,00.67 720
18.12 35,00.34 540
14.24 87,70.00 760
Books Credit Cards Child
20 5 2
4 4 5
6 5 3
14 2 4
Scale(SPSS)
Ratio(MINITAB)

K. Venkata Rao
IIM ISI Alumni
www.linkedin.com/in/reachout-analytics-pvt-ltd
Thank Q

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