The document discusses the skills and responsibilities required to become a data scientist, including learning programming languages like Python and R, statistics, machine learning techniques, and how to work with large datasets, and notes that data science has become more viable in recent years due to increases in data storage, processing power, and the availability of open source tools and cloud technologies. It also provides an overview of different machine learning techniques like regression, classification, and clustering and the machine learning life cycle.

1. When can I call my self as Data Scientist
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2. What you must learn
• Python or R
• Statistics / Math’s
• Database
• Machine learning
• Visualization
• Cloud
• BigData/NoSQL
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3. Responsibilities of Data Science
• Collecting large amounts of data and transform it into a more usable
format
• Solving business problems using data
• Should be a good programmer
• Should be strong in Data Analytics
• Solid knowledge on Statics techniques like hypothesis testing,
distributions
• Should have knowledge on different Machine learning techniques
• Communication and presentation skills
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4. Why Now ?
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Bayes Theorem
Regression
Neural networks
Thomas Bayes some where 1700’s
Legendre,Gauss and Galton some where 1800’s
McCulloch and Pitts some where 1940s

5. Why Now?
• Storage is cheap comparatively past 10 years, so we can store all our
data.
• Processing speed is becoming cheap, so we can process the huge
amount of data
• We know how to deal/process huge amount of data because of Big
Data tools are maturing
• Open source tools are emerging
• Cloud technologies
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6. How Machine Learning works
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7. Machine with data
1. As we know from our childhood, we use to learn by seeing/sensing things multiple times, so
that we identify the things when we see them again.
1. Let's say in my region I find only few types of Apples(may be apple with red and green color) and I see them
multiple times so I can say it is apple when ever I see it,
2. Let's say when I move to another region, I see apples in different colors which I didn’t seen before, but still I can
predict it as an apple may be by smell, looks or some other attributes
2. Same way machine learning works we need to train machine with good data, so that when
ever the new data comes it can predict the Outcome.
1. In out above example I trained my machine learning model with the data shown above, it has attributes like Area,
Type of the house, number of bedrooms/bathrooms and the price(The value which we need to predict when ever
we got the new attributes)
2. Once my Machine Learning model got trained when I pass attributes like Area, Type of the house, number of
bedrooms/bathrooms my model will predict the price.
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Area(sqt) Type NoBedrooms NoBathrooms Price
1024villa 3 2 1 lak
2048Villa 5 3 2 lak
900appartment 2 1 0.5 lak
Label/predicted/dependent variable

8. Types of Machine Learning
Machine
Learning
AI
Supervised
Task Driven
Unsupervised
Data Driven
Reinforcement
Learn from Mistakes
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Regression
Classification
Clustering
It looks for a statistical relationship
Between the variables that may give
us an Estimation/prediction of a particular outcome.
Eg:-
1. House price prediction
2. Weather forecasting
3. Stock price prediction
Its like regression but it will look for the
separations based on the relevance or
predefined class
Eg:-
1. Good or bad customer
2. Credit risk
3. Spam or not spam email
I know what to predict but I don’t
have the labeled data(good or bad customer)
trying to find groups or sets based upon data

10. Granule level
of types of
Machine
Learning
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11. Business
understanding
Data acquire
Data
Preparation
EDA
Hypothesis
and model
selection
Model building
and deploying
Optimization
Life Cycle
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12. BI vs DS
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