Evolution of analytics and challenges /opportunities for future implementation. Types of machine learning algorithms

1. The Analytics 4.0
The rise of Cognitive Technologies
Talk by Seema Chokshi

2. About me
• Over a 17 of experience in the analytics
• Worked in different countries US, UK, Singapore
,India
• Analytical Advisor with many big banks in North
America , Latin America, Europe, Middle East,
South-East Asia and Australia
• Started the foundational curriculum of Data
Science in Singapore Management University
• Faculty of Data Science in SMU
• Currently a researcher in Responsible adoption of
Artificial Intelligence

3. What am I
going to
talk about
today?
Evolution of Analytics
Concept of Data Science
Connection with Machine Learning
and AI
Challenges and opportunities
implementing Analytics /AI

4. It matters
because …
Analytics revolution is here to stay
Analytics has and will continue to transform
businesses and processes; the way people work
and live…..

5. What is
Analytics?
The extensive use of data, statistical and
quantitative analysis, exploratory, predictive
models, and fact-based decisions, to drive
decisions and actions “
Definition from “Competing on
Analytics” by Thomas Davenport defines Analytics as “

6. Evolution of
Analytics
:Source: Competing on Analytics by T. Davenport

7. Data
Science
• Scientific method of reasoning applied to
data-driven decisions
• Hypothesis, experiments, facts, logical
reasoning
+ data engineering.

8. Data
science
Data mining & machine learning
Data wrangling (munging), retrieval +
storage
Big data
Statistics

9. Machine learning/data mining
Explores data Finds patterns Predicts (scoring)

10. 1. Is this A or B?

11. 2. Is this Weird?
Is this Weird?

12. 3. How much? How many?
How many?
How much?

13. 4. How is this organized?
How is this organized?

14. 5. What should I do now?
What should I do now?

15. Wanna play a game ?
https://cte.smu.edu.sg/project/machine-
learning-learning-object

16. Analytics Competitors
Retail
Movie
Distribution
Music
Advertising
Networking
Search
Dating
Legal
Advice
Human
Resources
Technology
company
Campaigning
Real Estate
Maps
CRM
Taxis
Disruptive companies
differentiated by
INTELLIGENT
APPLICATIONS
using
Data Analytics
Apple

17. Main
hurdles in
becoming
an
Analytical
Competitor
Business and Process Related
Senior Management unwillingness to change
Slow and outdated data processes…………………
Technology Related
Time taken to build and develop new technology
We still have a long way to get to the self driving car
…………………..

18. Ongoing
discussion
on
Responsible
AI …..
Fairness and algorithmic bias
Data Security and Privacy
Transparency and Explainability
Trust and Distrust
Regulation

19. Getting
Started
with your
DS career
Listen Listen to data science podcasts
Attempt Attempt a challenge on Kaggle
Read Read a Book on Data Science
Learn
Take a Course online – Coursera
Specializations, others

20. Tools & salaries
Chart from "2016 Data Science Salary Survey"

21. Ask me
anything
you can reach me on:
seema.chokshi@pm.me