The document discusses data science, its applications, various modeling methods, and stages of data science projects. It covers practical examples, including Amazon's recommendation systems and fraud detection in banking, along with the necessary steps and roles involved in a data science project. Additionally, it outlines essential algorithms and the use of R for data analysis and visualization.

1. By Rahul Gulab Singh

2.  What is Data Science and its
Application
 Stages of Data science and Project
roles
 Modelling methods namely
classification , decision tree ,
random forest
 Demo on technology using R studio
and programming

3.  Data science is managing the process that can
transform hypotheses and data into actionable
predictions.
Acquire
Data
Manage
Data
Choose
Modelling
Method
Write
Code
Verify
Result

4.  Amazon’s product recommendation systems
 Google’s advertisement valuation systems
 LinkedIn’s contact recommendation system
 Twitter’s trending topics
 Walmart’s consumer demand projection
systems

5. • Statistics, Linear Algebra, Optimization,
Time Series, etc.Math and Theory
• Machine Learning, Data Structures,
Parallel Algorithms, etc.
Applied
Algorithms
• Storage and computing platforms,
statistical tools ,etc.Technologies
• Text, Finance, Images, Econometrics etc.Domain Expertise
• Visualization, InfographicsArt

6. •Represents the business interestsProject sponsor
•Represents end users’ interestsClient
•Sets and executes analytic strategyData scientist
•Manages data and data storageData architect
•Manages infrastructureOperations

7. Define the
Goal
Collect
and
manage
data
Build the
model
Evaluate
the model
Present
results
Deploy
the model

8.  Prediction of customer buying pattern
 Identifying fraudulent transactions
 Determining price elasticity
 Best way to present product listings when a
customer searches
 Customer segmentation
 Evaluating marketing campaigns
 Organizing new products into a product
catalog

9. Linear
Discriminant
Analysis (LDA)
Classification and
Regression Trees
(CART)
k-Nearest
Neighbors (kNN)
Support Vector
Machines (SVM)
with a linear
kernel
Random Forest
(RF)
Training , Test
and Validation

10.  Training , Test and Validation
 Loan application prediction example
DAT
A
Test/
Train
Split
Trainin
g
DATA
Test
DATA
Training
Process
Model
Predictio
ns

11.  Example :- Finding bad loan applications
 Input variables :-
Age, salary , any other loan , address, other
income , education , background data
 1000 applications exist out of which 200 have
been defaulted
 Decision Tree for identifying Potential
defaulters

12. Durat
ion>5
0
Amo
unt>
4
millio
n
Amo
unt>
1mil
Amo
unt<
5 mil
Bad
(0.68)
Dura
tion
>120
Good
(0.75)
Good
(0.56)
Bad
(1.0)
Good
(0.61)
Bad
(0.88)

13. Input Variables

14. Input Variables

15. Tree 1 Tree 3Tree 2

16. Input
All Trees
Prediction
Tree1:
Tree2:
Tree3:
Random
Forest
Predicts:

17. Application where random forest algorithm is
widely used:
 Banking -loyal customer and fraud customers
 Medicine-Disease (patient’s medical records)
 Stock Market- Stock behavior, loss , Profit
 E-commerce- Similar customer , segmentation

18.  Example : Male , Female distribution
Hair
Length
(cms)
60
40
20
0/ 140 150 160 170 180 190 200
Height (cms)

19.  Example : Male , Female distribution
Hair
Length
(cms)
60
40
20
0/ 140 150 160 170 180 190 200
Height (cms)

21.  Installing the R platform.
 Loading the dataset.
 Summarizing the dataset.
 Visualizing the dataset.
 Evaluating some algorithms.
 Making some predictions

22.  Practical Data Science with R
 Demo commands
 R and R Studio installation files