Scientific Python: A-Z Data Science & Visualization 18 Hours

Scientific Python Masterclass Complete with REGEX, NumPy, Scipy, Matplotlib, Scikit-learn, Seaborn and Pandas + Notebook

What you'll learn

Python - Bootcamp
SciPy - Scientific Python Software Stack
NumPy - Numerical Array Processing
Matplotlib - 2D Plotting and Visualization
Pandas - Data Frames & CSV Files
Scikit Learn - Python Machine Learning
Seaborn - Statistical Plotting
REGEX - Python RE (Regula Expressions)
PyTorch - Python Tensor Flow
Python - Data Mining Pipeline


Requirements

Experience using Python is not necessary, because an easy to understand "Python Bootcamp Tutorial" is included!

Description

This is the Best and Most Complete Scientific Python Course on the Udemy platform that will walk you through the required skills for Data Sciences and useful Machine Learning (ML) libraries such as NumPy, Pandas, Scikit-Learn, Seaborn, Python RE (REGEX), PyTorch and Matplotlib. Furthermore, you learn how to work with different real datasets and use them for developing your models. All the Python code templates that we write during the course together are available, and you can download them with the resource button of each section.

WHAT YOU WILL GET & LEARN?

In this awesome 18 hours long course we will cover:

SciPy is a free and open-source Python library used for scientific computing and technical computing. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering. The SciPy library is currently distributed under the BSD license, and its development is sponsored and supported by an open community of developers.

NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Most of the Matplotlib utilities lies under the pyplot submodule, and are usually imported under the plt alias.

Pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license.

Scikit-learn: Simple and efficient tools for predictive data analysis · Accessible to everybody, and reusable in various contexts · Built on NumPy, SciPy, and matplotlib.

Seaborn: Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

Python REGEX Regular expressions (called REs, or regexes, or regex patterns) are essentially a tiny, highly specialized programming language embedded inside Python and made available through the re module.

Python is a great tool for the development of programs which perform data analysis and prediction. It has tons of classes and features which perform the complex mathematical analysis and give solutions in simple one or two lines of code so that we don't have to be a statistic genius or mathematical Nerd to learn data science and machine learning. Python really makes things easy.


INTERACTIVE LESSONS

Students purchasing this course will receive free access to the interactive version (with Scientific code playgrounds) of this course from the SCIENTIFIC PROGRAMMING SCHOOL (SCIENTIFIC PROGRAMMING IO).


LIVE CLASS SERIES

Based on your earlier feedback, we are introducing a Zoom live class lecture series on this course through which we will explain different aspects of Linux command line Python for Data analytics. Live classes will be delivered through the Scientific Programming School, which is an interactive and advanced e-learning platform for learning scientific coding.


Who this course is for:

Anyone with any background that interested in Data Science and Machine Learning
Who wants to perform computational computing with Python
Students who want to learn Scientific Python to improve their career prospects