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
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
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
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)
20.
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