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www.edureka.co/data-scienceEDUREKA DATA SCIENCE CERTIFICATION TRAINING
What to expect?
 What is Data Science?
 Job Roles in Data Science
 Components of Data Science
 Concepts of Statistics
 Power of Data Visualization
 Introduction to Machine Learning using R
 Supervised & Unsupervised Learning
 Classification, Clustering & Recommenders
 Text Mining & Time Series
 Deep Learning

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What is Data Science?

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What is Data Science?
 Data Science involves using automated methods to analyze massive
amounts of data and to extract knowledge from them.
 By combining aspects of statistics, computer science, applied
mathematics and visualization, data science can turn the vast
amounts of data the digital age generates into new insights and new
knowledge.
Data Science Components

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Job Roles of Data Science

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Job Roles of Data Science
Data
Scientist
Data
Analyst
Data
Architect
Statistician
Data
Engineer
Database
Administrator
Business
Analyst
Data & Analyst
Manager

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www.edureka.co/data-scienceEDUREKA DATA SCIENCE CERTIFICATION TRAINING
Job Roles of Data Science
Data
Scientist
Data
Analyst
Data
Architect
Statistician
Data
Engineer
Database
Administrator
Business
Analyst
Data & Analyst
Manager
Role:
Cleans and organizes big data. Works
on distributed computing and predictive
modeling.
Languages:
R, SAS, Python, Matlab, SQL, Hive, Pig
and Spark

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www.edureka.co/data-scienceEDUREKA DATA SCIENCE CERTIFICATION TRAINING
Job Roles of Data Science
Data
Scientist
Data
Analyst
Data
Architect
Statistician
Data
Engineer
Database
Administrator
Business
Analyst
Data & Analyst
Manager
Role:
Collects, processes and performs
statistical data analyses
Languages:
R, Python, HTML, JS, C, C++ and SQL

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www.edureka.co/data-scienceEDUREKA DATA SCIENCE CERTIFICATION TRAINING
Job Roles of Data Science
Data
Scientist
Data
Analyst
Data
Architect
Statistician
Data
Engineer
Database
Administrator
Business
Analyst
Data & Analyst
Manager
Role:
Creates blueprints for data management
systems to integrate, centralize, protect
and maintain data sources.
Languages:
SQL, XML, Hive, Pig and Spark

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www.edureka.co/data-scienceEDUREKA DATA SCIENCE CERTIFICATION TRAINING
Job Roles of Data Science
Data
Scientist
Data
Analyst
Data
Architect
Statistician
Data
Engineer
Database
Administrator
Business
Analyst
Data & Analyst
Manager
Role:
Develops, constructs, tests and
maintains architectures such as
databases and large-scale processing
systems.
Languages:
SQL, Hive, Pig, R, Matlab, SAS, Python,
Java, Ruby, C++ and Perl

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www.edureka.co/data-scienceEDUREKA DATA SCIENCE CERTIFICATION TRAINING
Job Roles of Data Science
Data
Scientist
Data
Analyst
Data
Architect
Statistician
Data
Engineer
Database
Administrator
Business
Analyst
Data & Analyst
Manager
Role:
Collects, analyses and interprets
qualitative and quantitative data with
statistical theories and methods
Languages:
R, SA, SPSS, Matlab, Tableau, Stata,
Python, Perl, Hive, Spark and SQL

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www.edureka.co/data-scienceEDUREKA DATA SCIENCE CERTIFICATION TRAINING
Job Roles of Data Science
Data
Scientist
Data
Analyst
Data
Architect
Statistician
Data
Engineer
Database
Administrator
Business
Analyst
Data & Analyst
Manager
Role:
Ensures that the database is available to
all relevant users, is performing properly
and is being kept safe
Languages:
SQL, Java, Ruby on Rails, XML, C# and
Python

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www.edureka.co/data-scienceEDUREKA DATA SCIENCE CERTIFICATION TRAINING
Job Roles of Data Science
Data
Scientist
Data
Analyst
Data
Architect
Statistician
Data
Engineer
Database
Administrator
Business
Analyst
Data & Analyst
Manager
Role:
Improves business processes as
intermediary between business and IT
Languages:
SQL, C, Excel, Tableau, Power BI and
Python

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www.edureka.co/data-scienceEDUREKA DATA SCIENCE CERTIFICATION TRAINING
Job Roles of Data Science
Data
Scientist
Data
Analyst
Data
Architect
Statistician
Data
Engineer
Database
Administrator
Business
Analyst
Data & Analyst
Manager
Role:
Manages a team of analysts and data
scientists
Languages:
SQL, R, SAS, Python, Matlab and Java

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Components of Data Science

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Components of Data Science
Data Science has the following components.
Statistics Visualization
Machine
Learning
Deep
Learning
Statistics

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Concepts of Statistics

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Concepts of Statistics
 Statistics is a branch of mathematics dealing with the collection,
analysis, interpretation, presentation and organization of data.
 Statistics began in the ancient civilization, going back at least to
the 5th century BC, but it was not until the 18th century that it
started to draw more heavily from calculus and probability theory.
Collection
Analysis
Interpretation
Presentation
DATA
Figure: Concepts of Statistics
Visual RepresentationPredictive Analysis

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Power of Visualization

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Scope of Visual Analytics

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Data Visualization
Integrate Different Data Sets Analyze Visualize

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Introduction to
Machine Learning using R

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Machine Learning using R
 Machine Learning explores the study and construction of algorithms that can learn from
and make predictions on data.
 Closely related to computational statistics.
 Used to devise complex models and algorithms that lend themselves to a prediction
which in commercial use is known as predictive analytics.
Speech Recognition Face Recognition Anti Virus Weather Prediction

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Supervised &
Unsupervised Learning

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Supervised & Unsupervised Learning
Supervised Learning
Supervised learning is the machine
learning task of inferring a function from
labelled training data. The training data
consists of a set of training examples.
E.g. If you built a fruit classifier, the labels will be “this is
an orange, this is an apple and this is a banana”, based
on showing the classifier examples of apples, oranges
and bananas.
Algorithms: SVM, Regression, Naive Bayes, Decision
Trees, K-nearest Neighbour
Algorithm & Neural Networks
Unsupervised learning is a type of machine
learning algorithm used to draw inferences
from datasets consisting of input data
without labelled responses.
Unsupervised Learning
E.g. In the same example, a fruit clustering will
categorize as “fruits with soft skin and lots of dimples”,
“fruits with shiny hard skin” and “elongated yellow
fruits”.
Algorithms: Clustering, Anomaly Detection,
Neural Networks and Latent Variable Models

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Reinforcement Learning
 Reinforcement learning is an area of machine learning concerned with
how software agents ought to take actions in an environment so as to
maximize some notion of cumulative reward.
 It differs from standard supervised learning in that correct input/output
pairs are never presented nor sub-optimal actions explicitly corrected.
Reinforcement Learning
Applications: Robots used in Manufacturing, Advertising, Inventory
Management, Player vs AI Games.

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Classifiers

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Introduction to Classification
 Classification is the problem of identifying to which set of categories a
new observation belongs.
 Classification belongs to the supervised learning.
 It is based on the training set of data containing observations.
Figure: Examples of Classification

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Radial Basis
Function (RBF)
Non Linear
PerceptronLinear
Classification Algorithms
Classifier
Quadratic
Linear
SVM
Logistic Regression
Naive Bayes
Neural Networks
Decision Trees
Kernel Estimation
Recurrent Neural
Network (RNN)
Modular Neural Network

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Classification Example
 Let us look at how a classification algorithm
works.
 Here is an example of Linear Regression using
alternating least squares method.

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Clustering

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 Clustering is the problem of categorizing objects to
which different groups without any prior information
about labels or classes.
 Clustering belongs to the unsupervised learning.
Clustering

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Recommender Systems

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Recommender Systems
 Recommender System is a subclass of information filtering system that seeks
to predict the "rating" or "preference" that a user would give to an item.
 Recommendations can be everywhere from Netflix & BookMyShow movies to
YouTube videos, Amazon products to Goibibo hotels, Xbox games to Zomato
restaurants.
Figure: Companies using Recommendation Systems

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Recommender Systems - Example
Recommendation systems work in two ways:
1. Collaborative Filtering: Collaborative filtering
approaches building a model from a user's past behaviour
as well as similar decisions made by other users.
2. Content-based Filtering: Content-based filtering
approaches utilize a series of discrete characteristics of an
item in order to recommend additional items with similar
properties.
Figure: Movie Recommendation in IMDb

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Text Mining

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Text Mining
Text
Clustering
Text
Categorization
Sentiment
Analysis
Concept
Extraction
Document
Summarization

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Text Mining
Text categorization (a.k.a. text
classification) is the task of
assigning predefined categories
to free-text documents.
E.g. News categories, academic
paper categories.
Text
Categorization

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Text Mining
Document
Summarization
Multi-document summarization
is an automatic procedure aimed
at extraction of information from
multiple texts written about the
same topic.

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Text Mining
Text
Clustering
Text Clustering is the application
of cluster analysis to textual
documents. It has applications
in automatic document
organization, topic extraction
and fast information retrieval or
filtering

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Text Mining
Concept
Extraction
Concept mining is an activity
that results in the extraction of
concepts from artifacts.
Solutions to the task typically
involve aspects of artificial
intelligence and statistics, such
as data mining and text mining.

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Text Mining
Sentiment
Analysis
Sentiment Analysis is the
process of determining whether
a piece of writing is positive,
negative or neutral.
Use Case: Twitter Sentiment
Analysis, Customer Sentiment
Analysis

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Time Series

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Time Series
 A time series is a series of data points indexed (or listed or graphed) in time order.
 Most commonly, a time series is a sequence taken at successive equally spaced points in
time. Thus it is a sequence of discrete-time data.
 Examples of time series are heights of ocean tides, counts of sunspots, and the daily
closing value of the Dow Jones Industrial Average.
Ocean Tides Sunspots Stock Market Prices

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Deep Learning

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Deep Learning
Before moving ahead let us look at some of the drawbacks of machine
learning.
1. Traditional ML algorithms are not useful while working with
high dimensional data, that is where we have a
large number of inputs and outputs.
For example, in case of handwriting recognition we have large
amount of input where we will have different type of inputs
associated with different type of handwriting.
2. Second major challenge with traditional machine learning models is
a process called feature extraction. Specifically, the programmer
needs to tell the computer what kinds of things it should look for
so as to make more accurate decision.

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Deep Learning
 Deep learning is one of the only methods by which we can
circumvent the challenges of feature extraction in machine
learning.
 This is because deep learning models are capable of learning to
focus on the right features by themselves, requiring little guidance
from the programmer.
 Therefore, we can say that Deep Learning is:
1. A collection of statistical machine learning techniques
2. Used to learn feature hierarchies
3. Often based on artificial neural networks
Artificial Intelligence
Machine Learning
Deep
Learning

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Deep Learning Examples
Figure: Face Recognition using Deep Learning

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Deep Learning Examples
Speech Recognition Self Driving Cars Automatic Translation

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Summary

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Summary
Power of Data VisualizationConcepts of Statistics Machine Learning using R
Components of Data ScienceJob Roles in Data ScienceWhat is Data Science?

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Summary
Time SeriesText Mining Deep Learning
Recommendation SystemsClusteringClassification
 Clustering is the problem of categorizing objects to which different
groups without any prior information about labels or classes.
 Clustering belongs to the unsupervised learning.

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Thank You …
Questions/Queries/Feedback