Basic Programming for Analytics

Paper Code:

MBB 127

Credits:

Contact Hours:

90.00

Max. Marks:

100.00

Objective:

Course Outcomes (COs):

Courseoutcome

Learning andteachingstrategies

AssessmentStrategies

On completion of this course, the students will be able to;

CO 37.Analyze the mathematical concepts of data science to frame and compute an abstract of the business problem.

CO 38.Install and run the Python interpreter.

CO 39.Write python programs using programming and looping constructs to tackle any decision-making scenario.

CO 40.Identify and resolve coding errors in a program.

CO 41.Illustrate the process of structuring the data using lists, dictionaries, tuples and sets.

CO 42.Design and develop real-life applications using python.

Approach inteaching:InteractiveLectures, GroupDiscussion,Tutorials, CaseStudy,Demonstration

Learningactivities forthe students:Self-learningassignments,presentations,practicalexercise

Class test,Semester endexaminations,Quiz,Assignments,Presentation,PeerReview

18.00

Data Science and Python : Introduction to data science and analytics ,Why Python for analytics, Jupyter Installation for Python, Features of Python, Pandas and npumy library, Python Applications. Flowchart based on simple computations, iterations.
Data Analytics and Mathematical concepts: Sets and their representation, subset, type of set, matrix and its operations, Determinants and properties of determinant.

18.00

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

18.00

Functions and String: Functions & string manipulation. Introduction to list: Need, creation and accessing list. Inbuilt functions for lists.

18.00

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

18.00

File handling: 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. Descriptive statistics: mean, mode, median, standard deviation , missing values and outliers.

Essential Readings: