Scientific Computing 101 using Python

With growing interest in Data Science using Python, a solid understanding of Scientific Computing libraries has become necessary, specifically the numpy, pandas, matplotlib and sklearn libraries. These libraries are at the center of Data Science, from data cleaning to data transformation and data analysis to data visualization.

In this 101 workshop, we will look at the ndarray, usually referred to as the numpy array. We will understand it's properties, what distinguishes it from Python's list & array objects and learn how to use it to perform fast math operations.

We will then move on to the pandas' DataFrame, understand how it leverages the numpy ndarray and the advantages it provides over the ndarray.

Finally, we will look at the matplotlib library, the de-facto plotting library for data visualization in Python.

We will learn about the numpy, pandas and matplotlib libraries using Jupyter Notebooks, which are popular tools to perform and share exploratory analysis in Python.

This is a paid workshop, to book your ticket click here (http://imojo.in/brloch)

Numpy - https://docs.scipy.org/doc/numpy/
Pandas - http://pandas.pydata.org/pandas-docs/stable/
Matplotlib - http://matplotlib.org/contents.html
Jupyter Notebook - http://jupyter-notebook.readthedocs.io/en/latest/

About the speaker :
Poruri Sai Rahul is a Scientific Software Developer at Enthought. He has over 3 years of programming experience in Python. He has conducted workshops on a variety of Python concepts during SciPy India at IIT Bombay and at IIT Madras. You can find out more about him here - https://rahulporuri.github.io/