Data Science Tutorial | What is Data Science? | Data Science For Beginners | Edureka

DATA SCIENCE CERTIFICATION TRAINING www.edureka.co/data-science
Agenda
1. Need for Data Science
2. Walmart Use Case
3. What is Data Science?
4. Who is a Data Scientist?
5. Data Science – Skill Set
6. Data Science Job Roles
7. Data Life Cycle
8. Introduction to Machine Learning
9. K – Means Use Case
10. K – Means Algorithm
11. Hands - On
12. Data Science Certification

Need For Data Science

Data Sources
Mobile Cloud Smart Car
Evolution of
Technology
IOT
Social Media
Other factors
Telephone Desktop Car

Data Sources
Evolution of
Technology
IOT
Social Media
Other factors

Data Sources
Evolution of
Technology
IOT
Social Media
Other factors
347,222 tweets1,736,111 pictures 204,000,000 emails
300 hours of video
uploaded
4,166,667 likes &
200,000 photos
4,166,667 likes &
200,000 photos

Walmart Use Case

Data Analysis At Walmart
Halloween and cookie sales
Data scientist at Walmart found a connection between Halloween and the sales of cookies.

Hurricane and strawberry pop tarts
Data scientist at Walmart found that sales of Strawberry pop-tarts increased by 7 times before a Hurricane.

Social media and cake pops
Walmart is leveraging social media data to find about the trending products so that they can be introduced to
the Walmart stores across the world

What Is Data Science?

What is Data Science?
Data Science is the process of extracting knowledge and insights
from data by using scientific methods.
Scientific methods:
Programming + Statistics + Business
“Torture the data, and it will confess to anything.”
~ Ronald Coase, Economics, Nobel Prize

Who Is A Data Scientist?

Who Is A Data Scientist?
Mathematics
Business Technology

Data Science – Skill Set
Programming
languagesStatistics
Machine Learning
Big Data processing
frameworks
Data wrangling &
exploration
Data visualisation
Data extraction &
processing

Data Science Job Roles

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

Data Science Life Cycle

Data Life Cycle
Data Science
Business
requirements
Data
acquisition
Data
processing
Data
exploration
Modelling
Deployment

Data Life Cycle
Understand the problem
Identify central objectives
Identify variables that need
to be predicted
Business requirements
Data acquisition
Data Processing
Data exploration
Modelling
Deployment

Data Life Cycle
Data acquisition
Data Processing
Data exploration
Modelling
Deployment
What data do I need for my project?
What are the data sources?
How can I obtain the data?
What is the most efficient way to
store and access all of it?

Data Life Cycle
Data acquisition
Data Processing
Data exploration
Modelling
Deployment
Transform data into desired format
Data cleaning
• Missing values
• Corrupted data
• Remove unnecessary
data

Data Life Cycle
Data acquisition
Data Processing
Data exploration
Modelling
Deployment
understand the patterns in the data
Retrieve useful insight
form hypotheses

Data Life Cycle
Data acquisition
Data Processing
Data exploration
Modelling
Deployment
Determine optimal data features
for the machine-learning model
Create a model that predicts the
target most accurately
Evaluate & test the efficiency of
the model

Data Life Cycle
Data acquisition
Data Processing
Data exploration
Modelling
Deployment
Check the deployment environment
for dependency issues
Deploy the model in a pre-
production/ test environment
Monitor the performance

Introduction To Machine Learning

What Is Machine Learning?
Machine learning is a subset of artificial intelligence (AI) which provides machines the ability to learn automatically &
improve from experience without being explicitly programmed.
They look the same!
Cherry
Apple
Orange
Data
Algorithm

Types Of Machine Learning
Reinforcement LearningSupervised Learning Unsupervised Learning

K – Means Use Case

Brain Tumour Detection Using K - means
Brain tumour segmentation deals with the implementation of the k-means
algorithm for detection of range and shape of tumour in brain MR images.
K-Means clustering is an unsupervised learning algorithm used to partition a dataset
into k clusters in which each data point belongs to the cluster with the nearest mean.

K – Means Algorithm

Initialization
Cluster assignment
Move centroid
Optimization
Convergence
➢Randomly initialize k points called the cluster centroids.
Here, k = 2
➢Value of k(number of clusters) can be determined by the elbow
curve.

Initialization
Cluster assignment
Move centroid
Optimization
Convergence
➢Compute the distance between the data points and the
cluster centroid initialized.
➢Depending upon the minimum distance, data points are
divided into two groups.
1
2
Euclidean distance
Cluster
centroid

Initialization
Cluster assignment
Move centroid
Optimization
Convergence
➢Compute mean of red dots & reposition red cluster
centroid to this mean
➢Compute mean of green dots & reposition green
cluster centroid to this mean.
1
2

Initialization
Cluster assignment
Move centroid
Optimization
Convergence
1
2
➢Repeat previous two steps iteratively till the cluster
centroids stop changing their positions.

Initialization
Cluster assignment
Move centroid
Optimization
Convergence 1
2
➢Repeat previous two steps iteratively till the cluster
centroids stop changing their positions.

Initialization
Cluster assignment
Move centroid
Optimization
Convergence 1
2
➢Finally, k-means clustering algorithm converges.
➢Divides the data points into two clusters clearly visible in
red and green.

➢ Data Matrix
➢ Distance/ dissimilarity Matrix

Hands - On

Data Science Certification

Edureka’s Data Science Certification

Edureka’s Data Science Certification
Introduction to
Data Science
Statistical
Inference
Data extraction,
wrangling &
exploration
Introduction to
Machine Learning
Classification
techniques
Unsupervised
Learning
Recommender
engine Text Mining Time seriesDeep Learning

WebDriver vs. IDE vs. RC
➢ Data Warehouse is like a relational database designed for analytical needs.
➢ It functions on the basis of OLAP (Online Analytical Processing).
➢ It is a central location where consolidated data from multiple locations (databases) are stored.

Data Science Tutorial | What is Data Science? | Data Science For Beginners | Edureka

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