| Course Status : | Completed |
| Course Type : | Elective |
| Duration : | 6 weeks |
| Category : |
|
| Credit Points : | 3 |
| Level : | Continuing Education |
| Start Date : | 29 Jan 2024 |
| End Date : | 30 Apr 2024 |
| Enrollment Ends : | 29 Feb 2024 |
| Exam Date : | 19 May 2024 IST |
| Exam Shift : | Shift-1 |
Note: This exam date is subjected to change based on seat availability. You can check final exam date on your hall ticket.
|
swayam@nitttrc.edu.in, swayam@nitttrc.ac.in
|
Sr No. |
Units and
Lessons/Sub-Units |
|
1. |
Structured
Instruction – Relevance to real-world problems – collaborative learning
activities for solving the problem – Application oriented teaching – feedback
and support |
|
2. |
Introduction
to Programming and Python - Installing Python and Setting Up Development
Environment - Basic Syntax, Variables, and Data Types - Input/Output
Operations – Learning Activities: Overview lectures on programming
concepts and Python fundamentals - Hands-on exercises to practice basic
syntax and data types - Coding tasks involving input/output operations. |
|
3. |
Data Structures: Lists, Tuples, Dictionaries, and
Sets - Creation and Manipulation String Manipulation and Formatting - Reading
from and Writing to Files in Python Handling Exceptions and Errors. Learning Activities: Exercises and coding challenges to practice working with lists,
tuples, dictionaries, and sets - String manipulation tasks and formatting
exercises - list comprehensions - File manipulation exercises: reading,
writing, and handling different file formats - Error handling exercises to
manage exceptions |
|
4. |
Functions and Modules: Basics and Usage - Built-in
and Standard Libraries - Pandas and NumPy Libraries - Loading and Displaying
Data: CSV, Excel, JSON, and other formats8 - Data Manipulation with Pandas -
DataFrame and Series: Creation and Basic Operations - Indexing, Slicing, and
Filtering DataFrames -Grouping and Aggregation - Creating and Importing
Modules Learning Activities: Coding exercises to create and use functions and modules - Exploring
built-in libraries and their functionalities - Coding tasks to introduce
basic Pandas and NumPy operations - Hands-on exercises on creating DataFrames
and Series, performing basic operations - Coding tasks involving indexing,
slicing, and filtering of data - Practical exercises demonstrating grouping
and aggregation - Hands-on tasks to create and import custom modules. |
|
5. |
Importance and Principles of Data Visualization-
Matplotlib, Seaborn, and Plotly - Basic Plotting with Matplotlib: Line Plots,
Scatter Plots, Bar Plots - Customizing Plot Appearance: Colors, Labels,
Titles, and Legends - Multiple Plots and Subplots in Matplotlib - Working
with Different Plot Types: Histograms, Pie Charts, Box Plots - Seaborn for
Statistical Visualization: Heatmaps, Pair Plots, Violin Plots - Facet Grids
and Categorical Plots in Seaborn Learning Activities: Explanation of data visualization concepts and libraries through
lectures - Hands-on exercises on basic plotting using Matplotlib - Coding
tasks to create various types of plots (line, scatter, bar) for data
representation - Practical exercises on customizing plot attributes and
appearance - Hands-on tasks to create multiple plots and subplots - Coding
challenges for generating histograms, pie charts, and box plots - Hands-on
exercises to create advanced plots using Seaborn |
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