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PROGRAMMING FOR ANALYTICS [1]

Paper Code: 
DAC 233
Credits: 
4
Periods/week: 
60
Max. Marks: 
100.00
Objective: 

This module introduces students to Python and form foundation for further analysis of Datasets.

Course Outcomes (COs):

Learning outcome (at course level)

Learning and teaching strategies

Assessment Strategies

Students will be able to:

CO11. Install and run the Python interpreter

CO12.Write python programs using programming and looping constructs to tackle any decision-making scenario.

CO13. Identify and resolve coding errors in a program

CO14. Illustrate the process of structuring the data using lists, dictionaries, tuples and sets.

CO15. Design and develop real-life applications using python

Approach in teaching:

Interactive Lectures, Demonstrations, Group activities

 

Learning activities for the students:

Effective assignments, Giving tasks.

 

Assessment Strategies

Class test, Semester end examinations, Practical Assignments, Individual and group projects

 

 

12.00
Unit I: 

Data Science, Why Python for Data Science, Jupyter Installation for Python, Features of Python, Python Applications

Flowchart based on simple computations, iterations

 

12.00
Unit II: 

Basics of Python: variables, data types, operators & expressions, decision statements.

Loop control statements. 

 

12.00
Unit III: 

Functions & string manipulation

Introduction to list: Need, creation and accessing list.Inbuilt functions for lists.

 

12.00
Unit IV: 

Introduction to tuples, sets and dictionaries: Need, Creation, Operations and in-built functions

 

 

12.00
Unit V: 

Introduction to File Handling: need, operations on a text file (creating, opening a file, reading from a file, writing to a file, closing a file)

Reading and writing from a CSV file.

 

ESSENTIAL READINGS: 
  1. Albert Lukaszewski [2], “MySQL for Python”,Packt Publishing [3]
  2. Madhavan (2015),“Mastering Python for Data Science”,Packt

 

 

REFERENCES: 

SUGGESTED READINGS: 

  1. McKinney (2017). Python for Data Analysis. O’ Reilly Publication
  2. Curtis Miller,”Hands-On Data Analysis with NumPy and Pandas [4]” , Packt Publishing [3]

 

JOURNALS:

  1. https://epjdatascience.springeropen.com/ [5]
  2. https://vciba.springeropen.com/ [6]
  3. https://appliednetsci.springeropen.com/ [7]
  4. https://www.journals.elsevier.com/science-of-computer-programming [8]

 

E-RESOURCES: 

  1. https://www.w3schools.com/python/ [9]
  1. https://www.python.org/ [10]
  2. https://pythonprogramming.net/ [11]
  3. https://spoken-tutorial.org/tutorial search/?search_foss=Python+3.4.3&search_language=English [12]

 

 

 

Academic Year: 
2022-23 [13]

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Source URL: https://csit.iisuniv.ac.in/courses/subjects/programming-analytics-2

Links:
[1] https://csit.iisuniv.ac.in/courses/subjects/programming-analytics-2
[2] https://www.amazon.com/s/ref=dp_byline_sr_book_1?ie=UTF8&text=Albert+Lukaszewski&search-alias=books&field-author=Albert+Lukaszewski&sort=relevancerank
[3] https://www.packtpub.com/big-data-and-business-intelligence/hands-data-analysis-numpy-and-pandas
[4] https://ntguardian.wordpress.com/books/hands-on-data-analysis-with-numpy-and-pandas/
[5] https://epjdatascience.springeropen.com/
[6] https://vciba.springeropen.com/
[7] https://appliednetsci.springeropen.com/
[8] https://www.journals.elsevier.com/science-of-computer-programming
[9] https://www.w3schools.com/python/
[10] https://www.python.org/
[11] https://pythonprogramming.net/
[12] https://spoken-tutorial.org/tutorial%20search/?search_foss=Python+3.4.3&search_language=English
[13] https://csit.iisuniv.ac.in/academic-year/2022-23