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www.edureka.in/data-science
Data Science
Make Business decisions
Smarter

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Objectives
What is data mining
What is data science??
What is need of data scientist??
Stages of data mining??
Roles and Responsibilities of a Data Scientist.
Sentiment analysis on Zomato reviews
At the end of this session, you will be able to

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www.edureka.in/data-scienceSlide 3
Data Science Applications: Wine Recommendation
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www.edureka.in/data-scienceSlide 4
Data Science Applications: Pizza Hut
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www.edureka.in/data-scienceSlide 5
Data Science Applications: Summarize News

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www.edureka.in/data-scienceSlide 6
How about this?
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What’s Common in these Applications?
According to Wikipedia: Data science is the study of the generalizable extraction of knowledge
from data, yet the key word is science.
These scenarios involve:
 Storing, organizing and integrating huge amount of unstructured data
 Processing and analyzing the data
 Extracting knowledge, insights and predict future from the data
Storage of big data is done in Hadoop. For more details on Hadoop please refer Big data and
Hadoop blog http://www.edureka.in/blog/category/big-data-and-hadoop/
Processing, Analyzing, extracting knowledge and insights are done through Machine Learning.
All above technologies and steps together can be termed as data mining process.
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Cross Industry standard Process for data mining ( CRISP – DM )
Stages of Analytics / Data Mining

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Knowledge discovery and data mining ( KDD)
Stages of Analytics / Data Mining

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What is data science??
“More data usually beats better algorithms,” Such as: Recommending movies or music
based on past preferences
No matter how extremely unpleasant your algorithm is, they can often be beaten simply by
having more data (and a less sophisticated algorithm).

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Components data science??

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What is R
R is Programming Language
R is Environment for Statistical Analysis
R is Data Analysis Software

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Data Science: Demand Supply Gap
Big Data Analyst
Big Data Architect
Big Data Engineer
Big Data Research Analyst
Big Data Visualizer
Data Scientist
50
43
44
31
23
18
50
57
56
69
77
82
Filled job vs unfilled jobs in big data
Filled Unfilled
Vacancy/Filled(%)
Gartner Says Big Data Creates Big Jobs: 4.4 Million IT
Jobs Globally to Support Big Data By
2015http://www.gartner.com/newsroom/id/2207915

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R : Characteristics
Effective and fast data handling and storage facility
A bunch of operators for calculations on arrays, lists, vectors etc
A large integrated collection of tools for data analysis, and visualization
Facilities for data analysis using graphs and display either directly at the computer or paper
A well implemented and effective programming language called ‘S’ on top of which R is built
A complete range of packages to extend and enrich the functionality of R

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Data Visualization in R
This plot represents the
locations of all the traffic
signals in the city.
It is recognizable as
Toronto without any other
geographic data being
plotted - the structure of
the city comes out in the
data alone.

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Data Science: Job Trends

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Machine Learning
We have so many algorithms for data mining which can be used to build systems that can read past data and can
generate a system that can accommodate any future data and derive useful insight from it
Such set of algorithms comes under machine learning
Machine learning focuses on the development of computer programs that can teach themselves to grow and change
when exposed to new data
Train data
ML
model
Algorithms

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Types of Learning
Supervised Learning Unsupervised Learning
1. Uses a known dataset to make
predictions.
2. The training dataset includes
input data and response values.
3. From it, the supervised learning
algorithm builds a model to make
predictions of the response
values for a new dataset.
1. Draw inferences from datasets
consisting of input data without
labeled responses.
2. Used for exploratory data analysis
to find hidden patterns or grouping
in data
3. The most common unsupervised
learning method is cluster analysis.
Machine Learning

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• Common Machine Learning Algorithms
Types of Learning
Supervised Learning
Unsupervised Learning
Algorithms
 Naïve Bayes
 Support Vector Machines
 Random Forests
 Decision Trees
Algorithms
 K-means
 Fuzzy Clustering
 Hierarchical Clustering
Gaussian mixture models
Self-organizing maps

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Use Case : Zomato Ratings Review

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 Module 1
» Introduction to Data Science
 Module 2
» Basic Data Manipulation using R
 Module 3
» Machine Learning Techniques using R Part -1
- Clustering
- TF-IDF and Cosine Similarity
- Association Rule Mining
 Module 4
» Machine Learning Techniques using R Part -2
- Supervised and Unsupervised Learning
- Decision Tree Classifier
Course Topics
 Module 5
» Machine Learning Techniques using R Part -3
- Random Forest Classifier
- Naïve Bayer’s Classifier
 Module 6
» Introduction to Hadoop Architecture
 Module 7
» Integrating R with Hadoop
 Module 8
» Mahout Introduction and Algorithm
Implementation
 Module 9
» Additional Mahout Algorithms and Parallel
Processing in R
 Module 10
» Project
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Questions?
Enroll for the Complete Course at : www.edureka.in/data_science
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www.edureka.in/data_science
Please Don’t forget to fill in the survey report
Class Recording and Presentation will be available in 24 hours at:
http://www.edureka.in/blog/application-of-clustering-in-data-science-using-real-life-examples/