Introduction to Data Analytics and Data Science

27/07/2025 20AD201 - DATAANALYTICS - Mrs. C. Kavitha, AP/AI&DS 1
Presentation by
Mrs. C. Kavitha
Assistant Professor(OG)/AI & DS
SRI RAMAKRISHNA ENGINEERING
COLLEGE
[Educational Service : SNR Sons Charitable Trust]
[Autonomous Institution, Reaccredited by NAAC with ‘A+’ Grade]
[Approved by AICTE and Permanently Affiliated to Anna University, Chennai]
[ISO 9001:2015 Certified and all Eligible Programmes Accredited by NBA]
VATTAMALAIPALAYAM, N.G.G.O. COLONY POST, COIMBATORE – 641 022.
20AD201 – DATA ANALYTICS
Department of Artificial Intelligence and Data
Science

COURSE OUTCOME
CO1: Understand the key concepts of data science and address the
different sectors used in data science.
CO2: Examine the basic concepts of exploratory data analysis and
feature selection algorithms.
CO3: Apply data analytics process for feature generation and
selection.
CO4: Analyze a complex dataset using data visualization tool to
inspire real-time projects.
CO5: Explore tools and practices for working with text and
recommender system.

20AD201 DATA ANALYTICS
Introduction to Data Science, Different Sectors using Data science,
Purpose and Components of Python in Data Science. Applications of
Data Science, Data Science and Ethical Issues- Discussions on privacy,
security, ethics- A look back at Data Science- Next-generation data
scientists.
MODULE I : INTRODUCTION TO DATA SCIENCE 10

Textbooks and References
TEXTBOOKS
1. Joel Grus, “Data Science from Scratch”, Shroff Publisher /O’Reilly Publisher Media,2019.
2. Cathy O’Neil and Rachel Schutt, “Doing Data Science, Straight Talk from The Frontline”, O’Reilly
Publisher Media, 2013.
REFERENCES
1. Jure Leskovek, Anand Rajaraman and Jeffrey Ullman, “Mining of Massive Datasets”, Cambridge University Press, 2020.
2. João Moreira, Andre Carvalho,”A General Introduction to Data Analytics”, Wiley Publisher,2018.
3. Jake VanderPlas, “Python Data Science Handbook”, O’Reilly Publisher Media, 2016.
4. Philipp Janert, “Data Analysis with Open Source Tools”, O’Reilly Publisher Media, 2010.
WEB REFERENCE
5. https://nptel.ac.in/courses/110/106/110106072/
6. https://www.coursera.org/learn/julia-programming

20AD201 - DATAANALYTICS - Mrs. C. Kavitha, AP/AI&DS
27/07/2025
DATA
Data is a collection of facts, such as numbers, words,
measurements, observations or just descriptions of things.
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TYPES OF
DATA
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QUANTITATIVE DATA
 Numerical information(numbers)
 Deals with numbers and things you can measure objectively: dimensions
such as height, width, and length, temperature and humidity, prices, area
and volume.

QUALITATIVEDATA
 Descriptive information: It is used to describe or categorize something
 It deals with characteristics and descriptors which cannot be measured but
can be observed subjectively.
 Example: Smells, taste, texture, colour etc

QUANTITATIVEDATA
CONTINUOUS DISCRETE
• It represents measurement • It represents items that can be
counted
• For example, height can be measured
in more precise scale: meters,
centimetres etc
• Forexample, total number of
students in a class
• Eg: Height= 165.5 cm, 155.8 cm etc • Eg: Total number of students = 25. It
cannot be 22.5 or 20.3 etc

QUALITATIVEDATA
NOMINAL ORDINAL
• Nominal scale is a naming scale where
variables are simply named or labelled.
• Ordinal scale, just beyond naming the
variables and it follows a specific
order.
• It is unordered. • It is ordered.
• Eg:Name of your school, type
of car etc.
• Eg:Rating a restaurant on a
scale from 0(lowest) to 5(highest).

Structureofbigdata

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WHAT IS HAPPENING IN
INTERNET?
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DATA
INFLATION
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MAKING DATA WORK FOR
YOU
Use data to better describe the present or better predict the
future
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UNDERSTANDIN
G DATA
SCIENCE
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UNDERSTANDIN
G DATA
SCIENCE
17

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UNDERSTANDIN
G DATA
SCIENCE
18

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UNDERSTANDIN
G DATA
SCIENCE
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UNDERSTANDIN
G DATA
SCIENCE
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UNDERSTANDING DATA
SCIENCE
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DATA SCIENCE
 Data Science is the process of using data to find solutions to predict outcomes
for a problem statement.

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DATA SCIENCE PROCESS
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DATA SCIENCE PROCESS
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THE DATA SCIENCE
STEPS
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DATA
SOURCES
From
Where data
comes
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DATA
SOURCES
Company data
• Collected by companies
• Helps them make data-driven
decisions
Open data
• Free, open data sources
• Can be used, shared, and built-on by
anyone
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COMPANY DATA
WEB DATA
SURVEY
DATA
CUSTOMER DATA LOGISTICS DATA
FINANCIAL TRANSACTIONS

OPEN DATA
Open data is data that can be freely used, re-used and redistributed by anyone
Public APIs Public APIs

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OTHER DATA
TYPES
Image data
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OTHER DATATYPES
Text data

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OTHER DATA
TYPES
Network data
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DATA STORAGE
Cloud
Big Data

DATA GENERATION
Internet Search

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DATA
GENERATION
Recommendation Systems
35

scientists.

Purpose And Components Of Python In Data
Science
 Beginner-friendly syntax
 Rich ecosystem of libraries
 Strong community support
 Integration with databases, web, and cloud
 Rapid development and prototyping
WHY PYTHON?

Purpose And Components Of Python In Data
Science
Feature Python R Java
Ease of Learning ✅ Easy Moderate Hard
Visualization ✅ Good ✅ Excellent Basic
ML/AI Support ✅ Excellent ✅ Good ✅ Moderate
Community Support ✅ Strong ✅ Strong ✅ Strong
Scalability ✅ High Moderate ✅ High

APPLICATIONS
Recommendation
Tracking Customer Spending Habit, Shopping Behavior

APPLICATIONS
Data about the condition of the
traffic of different road, collected
through camera kept beside the
road, at entry and exit point of the
city, GPS device placed in the
vehicle.
Smart Traffic system

APPLICATIONS
 Big data analysis helps drive a car without human
interpretation.
 In the various spot of car camera, a sensor placed, that
gather data like the size of the surrounding car, obstacle,
distance from those, etc.
 These data are being analyzed, then various calculation
like how many angles to rotate, what should be speed,
when to stop, etc carried out.
 These calculations help to take action automatically.
Auto Driving Car

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APPLICATIONS
Image & Speech Recognition
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APPLICATIONS
 what type of video, music users are
watching, listening most
 how long users are spending on site
Media and entertainment sector

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APPLICATIONS
Health Care
44

APPLICATIONS
Forecasts

scientists.

What is Data Ethics?
Data Ethics is the study and practice of responsible data collection,
storage, use, and sharing.
It addresses questions such as:
• Are we collecting data fairly?
• Who owns the data?
• Are our models causing harm?

Why Ethics in Data Science?
Data influences real-life decisions:
The systems we build using data can affect important parts of our lives
— like who gets a job, a loan, medical treatment, or even how long
someone stays in jail.
Bias in algorithms can be harmful:
If the data used to train these systems is biased or unfair, the outcomes
will also be unfair. For example, a hiring tool may favor one gender or
race over another without anyone realizing.
Wrong use of data breaks trust:
When companies or governments misuse personal data — like selling it
or using it without permission — people lose trust, and it can lead to
lawsuits, scandals, or even harm to individuals.

Ethical Questions in Data Science
 Did people agree to share their data?
Are users clearly told what data is being collected and why — and did they give
permission knowingly?
 Are we open about how we use the data?
Do we clearly explain how the data will be used, who will have access, and what
decisions will be made based on it?
 Is the system treating everyone fairly?
Are all groups — regardless of race, gender, or background — getting equal and
unbiased outcomes from the AI or data system?
 Do people have control over their data?
Can users say "no" to being tracked or remove their data from the system if they
want to?

What is Data Privacy?
Privacy refers to the right of individuals to control their personal
data.
In data science, privacy involves:
 Limiting data access
 Using data only for intended purposes
 Protecting identity

Common Privacy Violations
 Collecting data without user consent
 Storing personal data without encryption
 Tracking users without informing them (e.g., cookies)
 Selling or sharing data to third parties without permission

Protecting Privacy
 Anonymization: Remove identifiers
 Data minimization: Collect only what's needed
 Encryption: Secure data at rest and in transit
 Privacy policies: Be transparent with users

What is Data Security?
 Protecting data from unauthorized access, theft, or damage
 Ensures confidentiality, integrity, and availability (CIA Triad)
Common Threats
Hacking and phishing
Weak authentication
Insider threats (e.g., employees misusing access)
Cloud misconfigurations

Best Practices for Data Security
 Use strong passwords and two-factor authentication
 Encrypt data and regularly back it up
 Assign role-based access to sensitive data
 Audit data usage and access logs

scientists.

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Data Science THEN vs NOW
Past Present
Manual analysis Automated ML pipelines
Small samples Big data sets
Excel sheets Python, Jupyter Notebooks
Local processing Cloud-based systems

scientists.

Who Are Next-Generation Data Scientists?
 They are the future data experts who won’t just write code but
will also think, question, and act responsibly.
 They will solve real-life problems with data – not just build
models for profits.
 They care about accuracy, fairness, and impact.
“A good data scientist doesn’t just predict the future — they help shape it wisely.”

Skills and Qualities They Need
 Strong in math, coding, and statistics
 Can prepare and clean messy data
 Follow every step carefully — don’t rush!
 Ask questions and test ideas like a scientist
 Use simple and honest models, not just complex ones

Current Gen vs. Next-Gen Data Scientists
Aspect Current Generation Next-Generation
Goal
Deliver fast results for business
needs
Solve problems responsibly for long-
term impact
Approach
Often rush to apply trendy
algorithms
Take time to understand data and the
problem
Data
Handling
Focus mostly on model building
Spend more time on data cleaning &
understanding
Skills Technical coding and ML skills
Technical + soft skills + ethical
thinking
Mindset Task-oriented (complete the job)
Curious, careful, and scientific
thinker
Model Use
Use black-box models without full
understanding
Prefer interpretable and fair models
Ethics Often overlooked in the rush to build Core part of decision-making

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Thank You
62

Introduction to Data Analytics and Data Science

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