The document outlines a course on 'Programming for Data Science and AIML' aimed at teaching students how to solve machine learning and data science problems using Python. It covers essential Python libraries, job opportunities in various sectors, pedagogical strategies, and expected cognitive and non-cognitive learning outcomes. The course emphasizes hands-on experience to enhance students' programming and problem-solving skills, preparing them for careers in technology-related fields.

1. PREPARED BY MEENAKSHI GUPTA
BENCHMARKING on
Subject
“PROGRAMMING FOR DATA SCIENCE
AND AIML”
(DS-4 semester)
Meenakshi Gupta
Asst.Professor
Department of CSE-AIML/DS

2. INTRODUCTION OF THE SUBJECT

3. The subject is all about:
 How to start and deal with machine learning and data science problem using python.
 This is a purely hands-on subject where students learn how to do programming and use different
types of libraries in Python, to solve Machine Learning and Deep Learning problems.
 Prerequisite – Basic Python Programming
This subject is the foundation course for Artificial Intelligence, Machine Learning & Deep Learning.
PREPARED BY MEENAKSHI GUPTA

4. DISCUSSION | WHAT DOES A DATA SCIENTIST DO? (HUAWEI.COM)M/ENTERPRISE/EN

5. PREPARED BY MEENAKSHI GUPTA

6. History of Python
Python is a high level and general –purpose programming language .

It was created by Guido van Rossum, and first released in 1991.

It is developed during winter training project.
Some Interesting facts about the Python Creator:
Van Rossum holds the title of distinguish Engineer at Microsoft onwards November 2020.
Van Rossum is now part of team at Microsoft working to speed ups the
language performance. And recently, a Microsoft blog post reported that
Python 3.12 ( latest version) had brought speedups to 60% to some parts
of the language.
PREPARED BY MEENAKSHI GUPTA
GUIDO'S PERSONAL HOME PAGE (GVANROSSUM.GITHUB.IO)
Guido Van Rossum

7. Use of Python in Data Science
 Some famous projects developed using Python are- ChatGPT, Google Assistant, Siri and chatbots used in
customer service application.
 Python is utilized in speech recognition systems, examples include voice assistants like Amazon Alexa.
Python is used to build recommendation systems used by e-commerce platform like Amazon and streaming
services like Netflix.
Python is used in image recognition, Examples include facial recognition systems.
Python is also used in AI based healthcare applications, including medical image analysis, disease diagnose,
drug discovery, and personalized medicine .
 Python is also used in AI based financial applications for tasks such as algorithmic trading, fraud detection,
risk assessment, and credit scoring.
PREPARED BY MEENAKSHI GUPTA

8. Https:// www.google.com

9. PREPARED BY MEENAKSHI GUPTA
HTTP://MDUROHTAK.AC.IN/INFO/SYLLABI_BTECH.HTML

10. Course Content
The course covers following topics:
 Basic Python ( Overview on basic concepts and syntax of Python)
 NumPy ( Provide n-d arrays to store the data and performs mathematical functions on these arrays)
 Pandas ( provide data manipulation functions and data cleaning can be done using this)
 Scikit-learn ( Machine learning library used for Machine learning functions )
 NLTK (Deep learning library used for deep learning functions )
 Matplotlib &Seaborn (data visualization library used for making charts and graphs)
All these libraries are open source libraries so we can download them free. No license is required to use them.
PDSA notes link : https://drive.google.com/drive/folders/1NbPFcgU8idOBAj8j_hTVgwqCH37HceuP

11. Difference between C and python
PREPARED BY MEENAKSHI GUPTA
print('Hello, world!’)

12. SOURCE – LAB MANUAL OF PDSA

13. SOURCE – LAB MANUAL OF PDSA

14. SOURCE – LAB MANUAL OF PDSA

15. SOURCE – LAB MANUAL OF PDSA

16. 1.
State about the purpose (aim) of studying
“Programming in data science and AIML”?
How it will help students prepare for life?
Define learning from the course.
PREPARED BY MEENAKSHI GUPTA

17. •Helps to understand the basic concepts and syntax of python .
•Helps in implementation of its various libraries
(NumPy,Pandas,Matplotlib,scikit_learn,NLTK ) to write down the machine learning and
data science programs .
•Helps to improve programming skills in python libraries .
•Helps to improve problem solving skills .
•Helps to improve decision making skill .
PREPARED BY MEENAKSHI GUPTA
Purpose of the course

18. How will it help students prepare for life?
 Enhance the knowledge of Technology: Learning Python can provide students with a solid
foundation in technology and help them develop the skills and mindset needed to become
proficient in various technological fields.
 Career opportunities: Python is widely used in various industries such as web development,
data science, artificial intelligence, and more. Learning Python can open up a wide range of
career opportunities for students in the future.
 Entrepreneurship Opportunities :
o Start their data science consultation (data science consultant)
o Start giving machine learning solutions (freelancing)
o Data visualization services ( can provide charts and graphs for pitchers of the business to
investors)
o Offer courses and workshops for learning python ( trainers)
PREPARED BY MEENAKSHI GUPTA

19. Prepared by Meenakshi Gupta
Research Opportunity:
o Students who are taking higher degree , they use Python libraries for
research purposes, such as analyzing scientific data or developing new
algorithms.

20. Learning from the course
• Students will Stay Industry relevant and grow in their career.
• Students will learn usage of NumPy, Matplotlib, Pandas along with Basics of Python
• Students will Understand how to apply data visualization practices in real-world
scenarios
• Student will get an opportunity to Work with different types of data.
• Students will learn usage of sk_learn and NLTK and its various functionalities.
o

21. Internships
By learning python, students will be able to crack the internships in 5th semester
onwards. For this, they have to register on some websites who are providing the
internships.
Some website link are:
https://www.glassdoor.co.in
https://www.niti.gov.in/internship
https://internship.aicte-india.org
PREPARED BY MEENAKSHI GUPTA

22. SOURCE HTTPS://INTERNSHIP.AICTE-INDIA.ORG

23. RECENTLY GOT INTERNSHIPS USING AICTE WEBSITE
PREPARED BY MEENAKSHI GUPTA

24. Job Profiles
 DATA ENGINEER
 MACHINE LEARNING ENGINEER
 DATA ANALYST
 DATA SCIENTIST
 SOFTWARE ENGINEER
PREPARED BY MEENAKSHI GUPTA

25. MADE BY MEENAKSHI GUPTA
Data
collection
Data cleaning
or
preprocessing
Data
Modelling
Data
visualisation
Numpy Pandas Sk_learn &NLTK Matplotlib &seaborn
Data Engineer
Data Analyst
Machine learning Engineer
Data Scientist
Job Profiles

26. DATA SCIENCE JOBS IN DIFFERENT SECTORS
 FMCG SECTOR (HUL , P&G ,D-MART,ITC)
REAL ESTATE SECTOR (DLF,UNITECH,)
E-COMMERCE INDUSTRIES(AMAZON,MYNTRA,FLIPKART,EBAY)
IT SECTOR (WIPRO,TCS,GOOGLE,HCL)
AGRICULTURE SECTOR(BALWAAN KRISHI,EICHER MOTORS)
AUTOMOBILE SECTOR( MARUTI,HONDA,HERO,TATA MOTOR)
HEALTH SECTOR(FORTIS,MEDANTA)
APP BASED SERVICES(ZOMATO,SWIGGY,UBER,OLA)
TELECOM SECTOR(AIRTEL,JIO, VODAFONE-IDEA)
DEFENCE SECTOR(DRDO,DEFENCE ORGANISATION(CID,CBI)
FINANICIAL SECTOR(INCOME TAX DEPARTMENT,EXCISE DEPARTMENT, TAXATION DEPARTMENT)
PREPARED BY MEENAKSHI GUPTA

27. 2.
Define scope of the subject in terms of expected
Learning outcomes (LOCs). It should be Cognitive
and Non-Cognitive learnings outcomes.
PREPARED BY MEENAKSHI GUPTA

28. Cognitive learning outcome :
1.Problem-solving skills: Learning Python encourages students to think logically and develop
strategies to solve problems, which are valuable skills in many areas of life.
2.Programming concepts: Python teaches fundamental programming concepts such as variables,
loops, conditionals, and functions, which form the basis of understanding other programming
languages and technologies.
3.Data analysis and manipulation: Libraries like NumPy and pandas teach students how to work with
data, perform data analysis, and manipulate datasets, which are essential skills in fields like data
science and analytics.
4.Machine learning and artificial intelligence: Libraries like scikit-learn and TensorFlow introduce
students to the concepts of machine learning and AI, enabling them to develop intelligent systems
and applications.
5.Visualization: Libraries like matplotlib teach students how to visualize data effectively using
different graphs, helping them communicate complex ideas and insights visually.
PREPARED BY MEENAKSHI GUPTA

29. Non-Cognitive Outcomes:
1.Creativity: Python's versatility allows for creative expression, encouraging students to think outside the
box and come up with innovative solutions to problems.
2.Collaboration & Teamwork: Python is often used in groups projects, teaching students how to work
effectively in teams and communicate complex ideas clearly.
3.Critical thinking: Students work on different programs for solving real world problems. So for making
minimum line program, you have to think and this enhances the critical thinking.
4.Communication: it is very necessary skill , students have to develop so that they can converse about
project input and output with their colleagues.
5.Confidence: Successfully learning Python and its libraries can boost students' confidence in their abilities
to learn new technologies and tackle complex problems in machine learning .
6.Patience and determination : Learning Python requires patience and determination, as programming
can be challenging. This can help students develop a growth mindset and the ability to persevere in the
face of difficulties
7.Adaptability: Python's popularity and wide range of applications make it a valuable skill that can be
applied in various fields, teaching students to be adaptable and open to new opportunities.
8.Time management: Learn to submit their work on time.
PREPARED BY MEENAKSHI GUPTA

30. 3.
Why non-cognitive skills are more important than
cognitive skill.
PREPARED BY MEENAKSHI GUPTA

31. 4.
Define the pedagogies to be adopted by the teacher for
effective transaction in the classroom in terms of mode
of delivery/lecture method.
PREPARED BY MEENAKSHI GUPTA

32. Flipped Classroom
HTTPS://YOUTU.BE/XGUCXTE2MHM?SI=ZTAW4HN2S7BMOYHP (NPTEL LINK)
HTTPS://SWAYAM.GOV.IN/ND1_NOC19_CS59/PREVIEW
Physical Learning Mode chalk and talk
Hands-On Coding Sessions Lab sessions
Online Learning Platforms nptel,youtube ,swayam
Discussion about Case studies and real world examples
Prerecorded lectures
Industry Expert Session Seminars

33. Lab session
SOURCE – LAB MANUAL OF PDSA

34. SOURCE – LAB MANUAL OF PDSA

35. SOURCE – LAB MANUAL OF PDSA

36. SOURCE – LAB MANUAL OF PDSA

37. Photo taken from DPGITM staff what's up-group
SEMINARS HELD AT DPGITM ON PYTHON
AND TABLEAU
CONDUCTED BY CSE-AIML/DS DEPARTMENT CONDUCTED BY BCA DEPARTMENT

38. 5.
What pedagogical support a teacher would require
like: teaching aids like practical demonstration,
audio-video, charts and any other supporting device
etc. (for facilitating learning among students)?
PREPARED BY MEENAKSHI GUPTA

39. Pedagogical supports
Conventional resource materials:
Textbooks:
oData Science from Scratch: First Principles with Python 2nd Edition
by Joel Grus (Author)(available in library)
HTTPS://WWW.AMAZON.COM/DATA-SCIENCE-SCRATCH-PRINCIPLES-PYTHON/ DP/1492041130

40. Course materials
PREPARED BY MEENAKSHI GUPTA

41. CASE STUDIES
Use real-world examples in lectures to explain practical applications of
the concepts. Example uber , Swiggy and Netflix case study
HTTPS://WWW.AMAZON.COM/DATA-SCIENCE-SCRATCH-PRINCIPLES-PYTHON/ DP/1492041130
https://youtu.be/u2aOTtOwOkc?si=Hx_05qT5RukayH2u
https://youtu.be/O1hZBHCCous?si=0HG4fSfBT5ccbibR
https://youtu.be/b7Kd0fLwgO4?si=05_XFxYDFRxTJqPy

42. 42
Online Learning Platforms & Courses
• Introduction with data analytics
• Data analyst IBM Certification
course
• Graduate Certificate
Programme in Data Science
& AI
• Introduction with python
libraries
• Understanding data science
• Certification course in Data
science for Engineers
• Python for data science

43. Assignments
PREPARED BY MEENAKSHI GUPTA

44. Group project list in class DS-4
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45. 6.
List the tools and techniques required in practical
transmission of specific subject to the students.
PREPARED BY MEENAKSHI GUPTA

46. 1. Integrated Development Environments (IDEs):
 Examples: Jupyter Notebooks, PyCharm, VSCode.
 Usage: Provide a user-friendly environment for writing, testing, and
debugging Python code.
For the download purpose:
downloading Anaconda Jupyter :
https://www.anaconda.com
downloading PyCharm :
https://www.jetbrains.com/pycharm/download/download
downloading VScode:
https://code.visualstudio.com/download
PREPARED BY MEENAKSHI GUPTA

47. Source -https//www.google.com

48. Source - Https;//www. Google.com

49. 7.
Specify all possible criteria, sources and methods to
be used by teachers for assessment of the students’
performance.
PREPARED BY MEENAKSHI GUPTA

50. PREPARED BY MEENAKSHI GUPTA

51. Theory vs Lab work
PREPARED BY MEENAKSHI GUPTA
Theory class
• Attendance
• Class test
• Assignments
• Quiz
• Power point presentation
• Sessional Exam
• University Exam
Laboratory class
• Attendance
• Hands -on Session Practice
• Assignments on lab work
• Project work individually
• Project work in groups
• Power point presentation
• Internal practical's & viva
• External practical's & viva

52. Resources for preparing Document
Discussion | What does a Data Scientist do? (huawei.com)
https://youtu.be/NNHBEYXbJC8?si=DtD9geaJM2pXqZFr(VIDEO LINK)
(
PDF) Non-Cognitive Skills and Factors in Educational Success and Academic Achie
vement (researchgate.net)
www.amazon.com ( for textbook details)
https://www.upgrad.com/ blog/libraries-in-python-explained
http://mdurohtak.ac.in/info/syllabi_btech.html (Syllabus of the subject )
PREPARED BY MEENAKSHI GUPTA

53. Resources for students
Online Websites platform for learning of the subject
o https://swayam.gov.in/nd1_noc19_cs59/preview
o Coding Projects for Beginners (codecademy.com)
o https://www.w3school.com
o Python Tutorial | Learn Python Programming (geeksforgeeks.org)
o https.pythontuturial.com, https://www.python.org/
o https://swayam.gov.in/nd1_noc19_cs59/preview
o https://youtu.be/XGUcXTe2MhM?si=zTaW4hN2s7BMoYhP (nptel link)
Book in library for subject (Python from scratch)
o Data Science from Scratch: First Principles with Python 2nd Edition
by Joel Grus
Textbooks:
1. 2. 3. Charles Dierbach., Introduction to Python using Computer Science, Wiley Publications, Second Edition, 2015
2. Mark Lutz , Learning Python, O’Reilly publications , Fifth Edition, 2015 Jake Vandar Plas, Python Data Science Handbook, O’Reilly ,
2016
PREPARED BY MEENAKSHI GUPTA

54. Prepared by Meenakshi Gupta
Thank you