1) The document introduces data science and its core disciplines, including statistics, machine learning, predictive modeling, and database management. 2) It explains that data science uses scientific methods and algorithms to extract knowledge and insights from both structured and unstructured data. 3) The roles of data scientists are discussed, noting that they have skills in programming, statistics, analytics, business analysis, and machine learning.

1. Introduction to
Data Science
Connecting … in Analytics
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2. Mousum Dutta
Chief Data Scientist, Spotle.ai
IIT Kharagpur
Dr Sourish Das
Assistant Professor,
Chennai Mathematical Institute
Dr. Subhas Kulkarni
Distinguished Data Scientist
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Instructors

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All around us, data is exploding….
Machine
Learning
Traditional
Software
Data Analysis
Over 2.5
quintillion
bytes of
data are
created
every
single day
Source: domo.com

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And exploding at an increasing pace….
Machine
Learning
Traditional
Software
Data Analysis

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Every day your digital traces are being mined to….
Machine
Learning
Traditional
Software
Data Analysis
Recommend Your Next Buy
Fight Terrorism
Win Matches
Predict Cyclones
Prevent Frauds
Solve Homelesness
Invent the next superdrug
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And Data Scientists are leading the expedition
Machine
Learning
Traditional
Software
Data Analysis
• Fight terrorism
• Invent the next
superdrug
• Solve Homelesness
• Predict Cyclones
• Win Matches
• Recommend your next
buy
Demand for Data
Scientists/ Data
Engineers to
Grow 40% by
2020
Source: US department of statistics

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What is Data Science?
Domain Expertise
Statistics
Mathematics
Computer
Science
Machine
Learning
Traditional
Software
Data
Analysis
Data
Science
Data science is a multi-
disciplinary field that uses
scientific methods,
processes, algorithms and
systems to extract
knowledge and insights
from data in various forms,
both structured and
unstructured, similar to
data mining.
- Wikipedia
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Data
Science

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Data Science interconnects disconnected
processes to build automated analytical systems
Data Collection
• Usually happens outside of the analytical system
• Results of primary and secondary research/ customer data
Extract, Transform and Load (ETL)
• Extracting the data from various sources and converting into machine-readable
formats
• Is implemented by BI programmers
Core Analytics
• The statistician creates new models based on the data or fits existing models on
to it to derive insights
Reporting
• Reports are generated based on the data summarising past performances and
future forecasts
• This step is delivered by business analysts/ domain experts
DataScience
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Data Scientist – Skillsets
Better Programmer than a
Statistician
Better statistician than a
Programmer
Master Integrator
Statistics
Analytics
Programming
-Python
-R
Business
Analysis/
Domain
Expertise
Data Mining
Artificial
Intelligence/
Machine
Learning
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10. Data Science
Core Disciplines and
Techniques
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Statistical decision making
Hypothesis Testing
Testing Alternative
Hypotheses
Is it true that vitamin C has
the ability to cure or prevent
the common cold?
Or is it just a myth?
Analysis of Variance
Comparing Multiple
Options
Buying a new mobile phone
but not sure how it stacks
up against the alternatives?
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Predictive modelling
The science of analyzing current and historical facts to predict future or
otherwise unknown events.
For example, estimating the mileage of a car at prototype design phase.
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13. Exploratory analysis
Analysing data sets to find key characteristics.
Used to find out a group of customers from existing list of customers who are
probable customers for an add-on product. This forms the basis of customer
segmentation in market research.
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Classification analysis
Part of predictive modelling, this
technique is used to divide a
training data set into groups of
similar data.
Here the target output are classes
or clusters.
Used to determine if a new email
that this a spam or not.
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Time-series analysis
Time Series Analysis is
the technique of
analysing trends
across successive
periods.
As an example, Time
Series Analysis will be
used in analysing sales
trends over previous
quarters and
forecasting sales for
next quarter.
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Database management
This is all
great, but
where is all
the data
stored?
How is it
retrieved?
How is it
managed?
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Certificate Masterclass in Data Science
with R – What will you learn?
✓ Understand different data analysis problems and algorithms.
✓ Get a structured approach to convert high level data analytics problem
statements into well defined workflow for solutions.
✓ Learn how to take a problem statement and break it down into smaller
components and solve using an appropriate algorithm.
✓ Learn how to execute data science projects in one of the widely adopted
languages for data science, that is R.
✓ Linear algebra that is critical for data science algorithms.
✓ Fundamentals of statistics that is the backbone of data science.
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18. Learn Data Science In The Most Intense Course Ever: https://spotle.ai/learn/data-science-with-r
70 Interactive Videos with
illustrative examples
24/7 Access to chat and
career support
12
Live Classes With
Instructors to learn
concepts first-hand
50 Quizzes to test your
learning on the go
25 Hours Of Lecture
20 Practice sessions and
real-life projects
Learn At Your Own Pace With Integrated
Instructor Support
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Interactive Videos with
illustrative examples
Quizzes to test your
learning on the go
Hours Of Lecture
Live Classes With
Instructors to learn
concepts first-hand
Practice sessions and
real-life projects
Access to chat and
career support
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Your Take - aways
✓ Certificate Of Completion.
✓ Integrated live projects and performance based internship.
✓ Membership of Spotle AI Alumni club with priority
access to courses, events and mentoring.
✓ Optimized career path fitment through
AI.
✓ Personalised career
guidance and assistance.
Sign up for the Masterclass: https://spotle.ai/learn/data-science-with-r
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