Coming soon
PROGRAMMING FOR ANALYTICS
Course Objectives:
The course will enable the students to
- Understand the existing working environment and acquire an in-depth technical knowledge of the domain.
- Study and learn programming concepts using PYTHON
- Design and develop real-life applications using python
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Learning outcome (at course level) |
Learning and teaching strategies |
Assessment Strategies |
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Students will be able to: CO10 Install and run the Python interpreter CO11 Write python programs using programming and looping constructs to tackle any decision-making scenario. CO12 Identify and resolve coding errors in a program CO13 Illustrate the process of structuring the data using lists, dictionaries, tuples and sets. CO14 Design and develop real-life applications using python
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Approach in teaching: Interactive Lectures, Demonstrations, Group activities
Learning activities for the students: Effective assignments, Giving tasks.
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Assessment Strategies Class test, Semester end examinations, Practical Assignments, Individual and group projects
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Data Science, Why Python for Data Science, Jupyter Installation for Python, Features of Python, Python Applications
Flowchart based on simple computations, iterations
Basics of Python: variables, data types, operators & expressions, decision statements.
Loop control statements.
Functions & string manipulation
Introduction to list: Need, creation and accessing list. Inbuilt functions for lists.
Introduction to tuples, sets and dictionaries: Need, Creation, Operations and in-built functions
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
- Albert Lukaszewski, “MySQL for Python”, Packt Publishing
- Madhavan (2015),“Mastering Python for Data Science”,Packt
- McKinney (2017). Python for Data Analysis. O’ Reilly Publication
- Curtis Miller,”Hands-On Data Analysis with NumPy and Pandas” , Packt Publishing
