How to gear up for a career in Analytics and understand the process of Data Science

2. amit_sharma_ai
amit-sharma-ai

3. • Faster andAccurate Decision support
• UnderstandingCustomer
• Better Risk management
• Business process Optimization
• List goes on …

4. Payroll Tax Time HR Talent Benefits
• Small, Midsized, Large, Multinationalmarkets
• Revenues:$11.7 billion in fiscal 2016
• ADP pays24 million (1 in 6) workersin U.S., and 12 million elsewhere
• FORTUNE 500®: Ranked 240
• Forbes®Global 2000: Ranked 334
• HumanCapitalManagement
ADP

6. Ask a
question
(Leader)
Source
the data
(Engg)
Explore
data
(Analyst)
Model
data
(Scientist)
Share
Results
(Scientist)

7. (Binary Classification)
(Multi-class classification)
(Anamoly detection)
(Regression)
(Unsupervised)
(Reinforcement)

8. 8

9. Ask a
question
What’s my
Company &
Product’s
Goal/Vision?
How can we
make our
products
‘smarter’?
What data do
we have or
can get?
Will this be
useful to end
user?
Business/DomainExpert
DB and Data Expert

10. Data
Security
Legal
Velocity
Granularity

11. Explore distributions
• Are they tellinga story
Handle missing data
• Can the missing data be ignored
• Does it need to be imputed
Look for outliers
• Do we want to
identify/filter/managethem

12. 0
2
4
6
8
10
12
14
16
18
1-3 4-6 7-10 11-14 15-18 19-22 23-26 27-30
No of Questions
%Dropout
0
2
4
6
8
10
12
14
16
18
0 100 200 300 400
%Dropout
No of Questions
How removing outliers helped uncover the correlation

14. Bias (underfitting) Optimal Variance (overfitting)
Solution
• Add more features
• Use a more complex model
Solution
• Fewer features
• More data to reduce
variance

15. overall accuracy is not good enough
People
resigned
People Not
Resigned
High Risk 30
100
(Type 1 Error)
Low Risk
70
(Type 2 Error)
800
OverallAccuracy – 83%
Sensitivity(Recall) – 30%

18. UI and Server side
DevelopmentML
Data Lake
Import/Export
Data Access
Feature Engineering
BenchmarkGeneration
Missing Data Imputation
Data Binning
Visualizations

19. Intellectually
Curious
Domain
Knolwedge
Great
Communications
Statistical
Knowledge
Hands-on
Programming

20. Learn
Onlineand
Curriculum
Compete
Analytics
Challenges
Explore
Datasets
Follow
Trend/Tech
Go Deep
Network
Share and Learn
Forums, Meetups