Class-I: Basic Data Structure
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List, Tuple & Dictionary, and Loops & Conditions
- Discussed property of List (e.g., immutability, append,indexing,list to array). Introduced Tuple.
- Discussed usefulness of Dictionary (e.g., key-valu pair paradigm of NoSQL database, search by key, involvement of list in dictionary, dictionary of dictionary, I/O data with json).
- Explored loops and conditions (e.g., for loop, while loop, zip, enumerate, break) and iterated over list and dictionary.
- Implemented loops and condition in creating Fern.
Class-II: Function & Class
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Object Oriented Programming
- Demonstrated data encapsulation and functionality in python object created by python class.
- Implemented data and function interaction with sample classes (e.g., Math, Circle, Newton).
- Implemented function and class to create (1) electric potential of lattice of charges (2) random walk in 1D and 2D (3) diffusion in 2D.
Class-III: Numpy
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Array, Grid, Algebra & Statistics
- Explored random array,heatmap of array, extraction of subarray and array manupulation (e.g.,reshaping, flattening, concatenation, row sum, column sum).
- Implemented arange and meshgrid techniques for creating contour and 3D plots.
- Explored basic functionalities in Algebra (e.g., vector and matrix manipulation), statistics (e.g.,central tendency, probability.
Class-IV: Pandas
Dataframe, Indexing, Groupby & Lambda functions
- Introduced Dataframe in Pandas and basic data visualization.
- Explored the data iteration and indexing in pandas (e.g.,iterrows, loc, iloc)
- Performed data exploration with different plottings (e.g., histogram, pairplot, barplot,factorplot,countplot,jointplot).
- Implemented groupby and lambda function methods to wrangling data in Dataframe.
Class-V: Scientific Computing
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Numerical Methods, Special Functions, Optimization & Simulation
- Introduced numerical methods in Python with Scipy package (e.g., Integration, differential equations).
- Explored Special Functions and visualized them.
- Explored Optimization (e.g., Gradient descent, Newton's Conjugate Gradient Method).
- Performed Monte Carlo Simulation of lattice system in Condensed Matter Physics (e.g., Ising Model).
Class-VI: Data Science
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Machine Learning, Reinforcement Learning & Deep Learning
- Introduced concept of supervised (e.g.,regression,classification) and unsupervised (e.g., clustering) learning.
- Explored basic ideas in Machine Learning Models with sample data (e.g., Titanic, Boston Housing).
- Explored Reinforcement Learning with learning agent in 2D world.
- Explored the basic idea in Deep Learning with image classification with neural network and convolutional neural network.