What Is Data Science? Data Science Course - Data Science Tutorial For Beginners | Edureka

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Edureka’s Data Science Certification Training
What is Data Science

What Will You Learn Today?
What is Data Science
Need Of Data Science Use case of Data Science
Business Intelligence
vs. Data Science
Tools used in Data Science Lifecycle of Data Science
1 2 3
4 6
5

Need Of Data Science
Revolution
of
Technology
Unstructured Data
Data Storage
Lack of scientific
insights
Data Science
Prediction
Decision making
Pattern discovery
Data Flow
Lack of predictive
analytics

THEN
NOW

You can use Data Science to
 Recommend the right product to the right customer to
enhance business.
 Predict the characteristics of high LTV customers and
helps in customer segmentation.
 Build intelligence and ability in machines.
 Predict fraudulent transactions beforehand.
 Perform sentiment analysis to predict the outcome
of elections.

What Is Data Science

What Is Data Science?
 Data Science is a blend of various tools,
algorithms, and machine learning principles with
the goal to discover hidden patterns from the raw
data.
 Data Science is primarily used to make decisions
and predictions.

Now, lets understand Data
Science with the help of some
use cases.

 Basketball teams are using data for tracking team
strategies and outcome of matches.
 Below parameters will be used for model building.
• Average pass time of ball.
• Number of successful passes.
• Speed and accuracy of successful baskets.
• Area of court the player on average is
shadowing.
 Models built on the basis of data science algorithms
help in pattern discovery of player game.

 Amazon has huge amount of consumer purchasing
data.
 The data consists of consumer demographics (age,
sex, location), purchasing history, past browsing
history.
 Based on this data, Amazon segments its
customers, draws a pattern and recommends the
right product to the right customer at the right
time.

Google self driving car is a smart, driverless car.
 It collects data from environment through
sensors.
 Takes decisions like when to speed up, when to
speed down, when to overtake and when to turn.

Use Cases Of Data Science

Skills Of Data Scientist

Role Of A Data Scientist
The Data Scientist will be responsible for designing and creating processes and layouts for complex, large-
scale data sets used for modeling, data mining, and research purposes.
Responsibilities
 Selecting features, building and optimizing classifiers using machine learning techniques.
 Data mining using state-of-the-art methods.
 Extending company’s data with third party sources of information when needed.
 Processing, cleansing, and verifying the integrity of data for analysis.
 Building predictive models using Machine Learning algorithms.

BI Vs. Data Science
Characteristics Business Intelligence Data Science
Perspective Looking Backward Looking Forward
Data Sources
Structured
(Usually SQL, often Data Warehouse)
Both Structured and Unstructured
( logs, cloud data, SQL, NoSQL, text)
Approach Statistics and Visualization
Statistics, Machine Learning, Graph
Analysis, Neuro- linguistic Programming
(NLP)
Focus Past and Present Present and Future
Tools Pentaho, Microsoft BI, QlikView, R RapidMiner, BigML, Weka, R

Tools Used In Data Science
Data analysis Data warehousing Data visualization Machine learning
• R
• Spark
• Python
• SAS
• Hadoop
• SQL
• Hive
• R
• Tableau
• Raw
• Spark
• Mahout
• Azure ML studio
Commonly used tools by Data Scientists

Lifecycle Of Data Science

What if we could predict the
occurrence of diabetes and
take appropriate measures
beforehand to prevent it?
Definitely! Let me take you
through the steps to
predict the vulnerable
patients.

Discovery
Data Preparation
Model Planning
Model Building
Operationalize
Communicate Results
 Discovery involves acquiring data from all the identified internal and
external sources that can help answer the business question.
 This data could be
• logs from webservers
• social media data
• census datasets
• data streamed from online sources via APIs

Discovery
Data Preparation
Model Planning
Model Building
Operationalize
Communicate Results
Doctor gets this data from the medical
history of the patient.
Attributes:
npreg – Number of times pregnant
glucose – Plasma glucose concentration
bp – Blood pressure
skin – Triceps skinfold thickness
bmi – Body mass index
ped – Diabetes pedigree function
age – Age
income – Income
Income is an irrelevant attribute in the
prediction of diabetes

Discovery
Data Preparation
Model Planning
Model Building
Operationalize
Communicate Results
 The data can have a lot of inconsistencies like missing values, blank
columns, abrupt values and incorrect data format which need to be
cleaned.
 It is required to explore, preprocess and condition data prior to modeling.
 This will help you to spot the outliers and establish a relationship between
the variables.

Discovery
Data Preparation
Model Planning
Model Building
Operationalize
Communicate Results
This data has lot of anomalies and needs cleansing before further
analysis can be done.

Discovery
Data Preparation
Model Planning
Model Building
Operationalize
Communicate Results
We clean and preprocess this data by removing the outliers, filling up the
null values and normalizing the data type.

Discovery
Data Preparation
Model Planning
Model Building
Operationalize
Communicate Results
 Here, we determine the methods and techniques to draw the relationships
between variable.
 Apply Exploratory Data Analytics (EDA) using various statistical formulas and
visualization tools.

Discovery
Data Preparation
Model Planning
Model Building
Operationalize
Communicate Results
Use of visualization techniques like histograms, line graphs, box plots to get a fair
idea of the distribution of data.

Discovery
Data Preparation
Model Planning
Model Building
Operationalize
Communicate Results
 Develop datasets for training and testing purposes.
 Consider whether existing tools will suffice for running the models.
 Analyze various learning techniques like classification, association and
clustering to build the model.

Discovery
Data Preparation
Model Planning
Model Building
Operationalize
Communicate Results
This is a decision tree based on different attributes.

Discovery
Data Preparation
Model Planning
Model Building
Operationalize
Communicate Results
Deliver final reports, briefings, code and technical documents.
Implement pilot project in a real-time production environment.
Look for performance constraints if any.

Discovery
Initialization
Model Planning
Model Building
Deployment
Communicate Results
 Identify all the key findings and communicate to the stakeholders.
 Explaining the model and result to medical authorities.
 Determine if the results of the project are a success or a failure based on the
criteria developed.

Discovery
Initialization
Model Planning
Model Building
Deployment
Communicate Results
 Diabetes Positive set:
• glucose > 154
• glucose >127 & <= 154 + bmi >30.9
• glucose<=127 + pregnant >5
• glucose<=127 + pregnant <=5 + age >28
• glucose<=127 + pregnant <=5 + age <=28 +bmi > 30.9
 Diabetes Negative set:
• glucose > 154
• glucose >127 & <= 154 + bmi <=30.9
• glucose<=127 + pregnant <=5 + age <=28 +bmi <= 30.9
 We can use this decision tree result to know whether the patient is
vulnerable to diabetes or not.

Course Details
Go to www.edureka.co/data-science
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What our learners have to say about us!
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What Is Data Science? Data Science Course - Data Science Tutorial For Beginners | Edureka

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