The document provides an overview of data science, emphasizing its multidisciplinary nature that combines programming, statistics, and domain expertise to derive insights from large datasets. It discusses the necessity of data science in handling big data challenges and application areas such as predictive modeling, product recommendations, and spam detection. Additionally, the document outlines essential skills and resources needed for a successful career in data science.

1. By,
Dr.V.Sumathy,
Assistant Professor,
Department of Data Science,
Loyola College, Chennai 600 034
Introduction to Data Science

2. Big data
Agenda
Definition of
Data Science
Application of
Data Science
Process of
Data Science
Project
Skillset to
acquire
Placement
Opportunities
Questions
and
Discussion
Big data
Vs
Traditional data
Need of Data
Science
Introduction to
Predictive and
prescriptive
models
Learning
Resources

3. Definition of Data Science
01
02
Data science is the study of data to extract meaningful
insights for business.
It is a multidisciplinary approach that combines domain
expertise, programming skills, machine learning algorithms,
and knowledge of maths and statistics.

4. Need for Data Science
Need
for Data
Science
Data
Abundance
Business
Value
Complexit
y of Data
Advancement
of Technology
Decision
Making
Personalisation
Social
Impact

5. Big Data
Streaming
Data -
Volume
Variety
of data
Velocity Veracity Value

8. Features involved in Pricing model
The amount charged for the
distance traveled during the trip.
A flat fee charged at the beginning
of every ride, regardless of distance
or time.
The amount charged for the
duration of the trip, typically based
on the time spent in the vehicle.
A fee charged to cover operational
costs, such as insurance and
customer support.
01 03
02 04
Base Fare
Per-Minute Rate
Per-Mile Rate
Booking Fee

9. Features involved in Pricing model
Additional fees may be added for
tolls, airport pickups, or other special
circumstances.
Uber frequently offers promotions,
discounts, and referral bonuses that
can affect the final price of a ride.
06 07
Tolls and Surcharges Promotions and Discounts
During times of high demand, Uber may implement surge pricing, which
increases the fares to encourage more drivers to be available. Surge
pricing multipliers can vary depending on the level of demand in the
area.
05
Surge Pricing

11. Features involved in product
recommendation
01 05
02 06
User Preferences and History
Item Attributes
Implicit Feedback
Explicit Feedback
03 07
Collaborative Filtering Contextual Information
04 08
Content-Based Filtering Seasonality and Trends

13. Features involved in spam detection
B
H
A
Sender
Reputation
Sources
Keyword/content
Whitelist/Blacklist
HTML code
Attachments
Header
analysis
Images
C
E
D
F

14. Credit card/ Loan sanction analysis

15. Data science in defence
➔ Predictive Maintenance
➔ Mission Planning and Optimization
➔ Target Identification and Tracking
➔ Health Monitoring and Medical Research
➔ Cybersecurity and Information Assurance
and many more

16. Data science in Rocket
launching
➔ Risk Assessment and Safety Analysis
➔ Real-Time Monitoring and Control
➔ Weather Forecasting and Environmental
Conditions
➔ Launch Site Selection and Infrastructure
Planning and many more

17. Types of Data Analysis
Predictive Prescriptive Diagnostic Descriptive

19. Obtain Data
B
H
A
Open source
Sources
Real time data
Video
Secondary/
Primary data
Text data
Real world
data
Images
C
E
D
F

20. Scrub Data
01 04
02 05
Handle missing values
Handle outliers
Drop unwanted columns
Data Transformation
03 06
Duplication data Data discretization

22. Explore Data
01 03
02 04
Create Histogram
Create scatterplot
Create Boxplot
Generate descriptive
statistics

26. Model Building

27. Model Building
Model in simple words is an equation that helps in making decisions be
it predictive, prescriptive, descriptive, or diagnostic analysis.
Example:

30. Training and Test data set

31. Evaluation metrics

32. Evaluation metrics

33. Types of Machine Learning Algorithms

34. Skill set to acquire
➔ Statistics-Descriptive and Inferential Statistics
➔ Mathematics- eigen, eigenvector, projection(Linear algebra)
➔ Programming language- Python, Spark
➔ DBMS, SQL, NoSQL
➔ Visualisation Tools- PowerBI/Tableau
➔ Cloud – Azure/GCP
➔ Web Scraping

35. Explore data
B
A
Kaggle
Explore data
UCI Repository
Data.gov
Twitter API
MIMIC-III
The World Bank
Open Data
C
E
D
F

36. Learning resources
B
A
Udemy
Learning
resources
Coursera
NPTEL
Linkedin
Medium.com
YouTube videos by
Krish Naik
C
E
D
F

37. Build Your Profile
B
A
Aptitude skill
Blocks
Mini project
Certifications
LinkedIn
profile
Hackathon
ranks
USP
C
E
D
F

40. Thanks!