Python for Data Science - Laboratory

Python for Data Science

Basics of Python:

Practical:

1. Programs using conditional and looping constructs

2. Programs using different data frames like list, tuple, set and dictionary.

Functions and Files:

Practical:

1. Programs using functions and classes.

2. Programs using strings and files

Foundations of Data Science:

Practical:

1. Data Creation and Mathematical operations.

2. Graphs and Plotting.

Descriptive Analytics:

Practical:

1. Statistical description of data without libraries

2. Generation of correlation coefficient.

3. Linear regression model.

Numpy and Pandas Libraries:

Practical:

1. Creation of 1D, 2D, and 3D NumPy arrays

2. Array Slicing and Indexing operations

3. Reindexing, and aligning data across multiple Data Frames.

Data Visualization:

Practical:

1. Line plot, bar plot, histogram, and box plot.

2. Seaborn plots, plot styling and customization