The document discusses the expansive field of data science, highlighting its relevance in a data-driven future and outlining the essential skills required for data scientists, including technical expertise in programming and business knowledge. It references the CRISP-DM methodology and various applications of data science in industries such as healthcare, marketing, and finance. Additionally, it addresses specific use cases like fraud detection and sentiment analysis, as well as the tools utilized in data science.

1. Data Scientist

2. Data
Science
Data
Mining
Automatic
Decisioning
Predictive
analysis
Machiine
Learning
Data
Analytics
Data
Analysis
The Big Picture

3. How much data is generated in a day – and what could this look like as
we enter an even more data-driven future?

4. The hierarchy of Data Science

5. Business
Understanding
Analytic Approach
Data Requirements
Data collection
Data understanding
Data preparationModelling
Evaluation
Deployment
Feedback
CRISP-DM methodology

6. Maths
and
Statistics
Computer
Skills
Domain
Knowledge
Machine
Learning
Traditional
Research
Data
Science
Traditional
software
M.Sc/ PhD in Maths or
Statistics
IT skills like C, C++,
Java, R , Python
etc.
Business knowledge like
Marketing / sales
/Procurement/ Finance/
Banking/ Healthcare /
Defence etc
Skills required for a data scientist

7. Skill Set as per Gartner

8. • As per Gartner the skill required is depicted in the below figure, the
coloured blocks represent the "minimum" level of skill required for
each role on a scale of 0 to 5

9. Health CareTravel Marketing AutomationBanking Social media Sales
Data Science usage
Dynamic
Pricing
Surge Pricing
In Uber and Ola
Disease
prediction
Medication
Effectiveness
Fraud
detection
Credit card
Risk detection
Customer
Churn
Upselling
Cross Selling
Sentiment
Analysis
Network
Analysis
Self Driving
Cars
Chat Bots
Demand
Forecasting
New product
positioning
Data Science Usage

10. Tools used for Data Science