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Programming For Analytics (Practical) [1]

Paper Code: 
24DAC233
Credits: 
4
Periods/week: 
2
Max. Marks: 
100.00
Objective: 

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

 

Course Outcomes: 

Course

Course outcome

(at course level)

Learning and teaching strategies

Assessment Strategies

Course Code

Course

Title

 

24DAC233

 

Programming For Analytics (Practical)

CO13. Install and run the Python interpreter

CO14. Create python programs using programming and looping constructs to tackle any decision-making scenario.

CO15.Identify and resolve coding errors in a program

CO16.Compare the process of structuring the data using lists, dictionaries, tuples and sets.

CO17.Design and develop real-life applications using python

CO18.Contribute effectively in course-specific interaction

Approach in teaching:

Interactive Lectures, Discussion, Demonstrations, Group activities, Teaching using advanced IT audio-video tools 

Learning activities for the students:

Effective assignments, Giving tasks.

Assessment Strategies

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

 

 

12.00
Unit I: 
Introduction to Python:

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: 
SQL commands:

SQL Process, SQL Commands – DDL, DML, DCL, DQL, SQL Constraints, Data Integrity, Data Types, SQL Operators, Expressions, Querying Database, Retrieving result sets, Sub Queries, Syntax for various Clauses of SQL, Functions and Joins.

 

ESSENTIAL READINGS: 
  1. Winston,” Microsoft Excel 2019 Data Analysis and Business Modeling”, PHI, 6th Edition 2019
  2. Denise Etheridge, “Excel Data Analysis”, Wiley Publication, 3rdEdition,2013
  3. Stephen L. Nelson, E. C. Nelson,” Microsoft Excel Data Analysis for Dummies “, Wiley,3rd Edition,2018
  4. Vinicius M. Grippa,” Learning MySQL: Get a Handle on Your Data”, O’Reilly Media, Inc, USA,2021
  5. Microsoft Excel 2016 - Data Analysis and Business Modeling PHI Publication 2017
  6. Denise Etheridge, “Excel Data Analysis”, WileyPublication,Third Edition
  7. R. Elmarsi and S.B. Navathe, “Fundamentals of Database Systems”, Addison Wesley, 7th Ed., 2015.
  8. James R. Groff & Paul N. Weinberg, “The Complete Reference SQL”, McGraw Hill Education, 3 Edition, 2017
REFERENCES: 

Suggested Readings:

  1. Abraham Silberschatz, Henry Korth, S. Sudarshan, “Database Systems Concepts”, 7th Edition, McGraw Hill, 2019.
  2. Bipin Desai, “An Introduction to Database Systems”, Galgotia Publications, 2015.
  3. Hector Guerrero ,“Excel Data Analysis - Modeling and Simulation”, Springer,2010
  4. Financial Analysis and Modeling using Excel and VBA: ChandanSengupta, Wiley
  5. Chris Newman ,”Sams Teach Yourself MySQL in 10 Minutes” , Sams Publishing; 1 edition (May 19, 2006)
  6. Alan Beaulieu,”Learning SQL”,O'Reilly Media, Inc,3rdEdition ,2020

 

e-Resources:

  1. https://spoken-tutorial.org/ [2]   
  2. https://www.tutorialspoint.com/management_information_system/classification_of_information.htm [3]
  3.  https://www.managementstudyguide.com/types-of-information-systems.htm [4]
  4. https://www.guru99.com/introduction-to-formulas-and-functions-in-excel.html [5]
  5.  https://trumpexcel.com/excel-functions/ [6]
  6. https://www.excel-easy.com/ [7]
  7. https://www.tutorialspoint.com/excel_data_analysis/index.htm [8]
  8. https://support.microsoft.com/en-us/office/excel-video-training-9bc05390-e94c-46af-a5b3-d7c22f6990bb?wt.mc_id=otc_home [9]
  9. https://www.w3schools.com/mySQl/default.asp [10]
  10. https://www.mysqltutorial.org/mysql-basics/ [11]
  11. https://www.javatpoint.com/mysql-tutorial [12]

Journals:

  1. https://www.journals.elsevier.com/international-journal-of-information-management-data-insights [13]
  2. https://journal-bcs.springeropen.com/ [14]
  3. https://jisajournal.springeropen.com/
  4. https://www.sciencedirect.com/journal/thinking-skills-and-creativity

 

Academic Year: 
2024-25 [15]

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

Links:
[1] https://csit.iisuniv.ac.in/courses/subjects/programming-analytics-practical
[2] https://spoken-tutorial.org/
[3] https://www.tutorialspoint.com/management_information_system/classification_of_information.htm
[4] https://www.managementstudyguide.com/types-of-information-systems.htm
[5] https://www.guru99.com/introduction-to-formulas-and-functions-in-excel.html
[6] https://trumpexcel.com/excel-functions/
[7] https://www.excel-easy.com/
[8] https://www.tutorialspoint.com/excel_data_analysis/index.htm
[9] https://support.microsoft.com/en-us/office/excel-video-training-9bc05390-e94c-46af-a5b3-d7c22f6990bb?wt.mc_id=otc_home
[10] https://www.w3schools.com/mySQl/default.asp
[11] https://www.mysqltutorial.org/mysql-basics/
[12] https://www.javatpoint.com/mysql-tutorial
[13] https://www.journals.elsevier.com/international-journal-of-information-management-data-insights
[14] https://journal-bcs.springeropen.com/
[15] https://csit.iisuniv.ac.in/academic-year/2024-25