Coming soon
Basic Programming for Analytics
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Course outcome |
Learning andteaching strategies |
Assessment Strategies |
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On completion of this course, the students will be able to; CO 1. Analyze the mathematical concepts of data science to frame and compute an abstract |
Approach in teaching: Interactive Lectures, Group Discussion, Tutorials, Case Study, Demonstration
Learning activities for the students:Self- learning assignments, presentations,practical exercise |
Class test, Semester endexaminations, Quiz, Assignments, Presentation, Peer Review |
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of the business problem. |
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CO 2. Install and run the Python |
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interpreter. |
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CO 3. Write python programs using |
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programming and looping |
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constructs to tackle any |
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decision-making scenario. |
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CO 4. Identify and resolve coding |
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errors in a program. |
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CO 5. Illustrate the process of |
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structuring the data using lists, |
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dictionaries, tuples and sets. |
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CO 6. Design and develop real-life |
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applications using python. |
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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.
Basics of Python: variables, data types, operators & expressions, decision statements. Loop control statements.
Functions and String: Functions & string manipulation. Introduction to list: Need, creation and accessing list. Inbuilt functions for lists.
Tuples: Introduction to tuples, sets and dictionaries: Need, Creation, Operations and in-built functions.
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.
*Case studies related to entire topics are to be taught.
Essential readings
- Madhavan, “Mastering Python for Data Science”, Packt, 2015.
- McKinney, Python for Data Analysis. O’ Reilly Publication, 2017.
- Curtis Miller, ”Hands-On Data Analysis with NumPy and Pandas",Packt, 2015
- (Latest editions of the above books are to be referred)
Suggested readings
- Curtis Miller, ”Hands-On Data Analysis with NumPy and Pandas",Packt, 2015
- (Latest editions of the above books are to be referred)
