Scientific Python- Data Science Visualization Bundle 18 Hrs!

Scientific Python: Data Science Visualization Bundle 18 Hrs!, available at $39.99, has an average rating of 3.7, with 88 lectures, based on 83 reviews, and has 15530 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone with any background that interested in Data Science and Machine Learning or Who wants to perform computational computing with Python or Students who want to learn Scientific Python to improve their career prospects It is particularly useful for Anyone with any background that interested in Data Science and Machine Learning or Who wants to perform computational computing with Python or Students who want to learn Scientific Python to improve their career prospects.

Enroll now: Scientific Python: Data Science Visualization Bundle 18 Hrs!

Summary

Title: Scientific Python: Data Science Visualization Bundle 18 Hrs!

Price: $39.99

Average Rating: 3.7

Number of Lectures: 88

Number of Published Lectures: 87

Number of Curriculum Items: 88

Number of Published Curriculum Objects: 87

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

18 HRS OF AWESOME FIVE STARS &&&&& VIDEOS!

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 hourslong 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 路 Accessible to everybody, and reusable in various contexts 路 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.

All data sets included!

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.

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). 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.

MONEY BACK GUARANTEE IF NOT 100% SATISFIED!

When you enroll you will get lifetime access to all of the course contents and any updates and when you complete the course 100% you will also get a Certificate of completion that you can add to your resum茅/CV to show off to the world your new-found Python & Scientific Computing Mastery!Don’t forget to join our Q&A live community where you can get free help anytime from other students and the instructor. This awesome course is a component of the Learn Scientific Computing master course.

So What are you Waiting For?Click that shiny enrollbutton and we’ll See you inside 馃槈

Course Curriculum

Chapter 1: Scientific Python

Lecture 1: Welcome!

Lecture 2: Why Enrol this Course?

Lecture 3: Instructor

Lecture 4: Free Insteractive Shell for Practice Python

Lecture 5: Introduction

Lecture 6: Python Tools

Lecture 7: Scientific Python Install – Windows

Lecture 8: Scientific Python Install – Linux

Lecture 9: Scientific Python Install – MacOS

Lecture 10: Jupyter Notebook Install – Windows & Linux

Lecture 11: Load in Jupyter Notebook

Lecture 12: Datasets (CSV) – Download First!

Chapter 2: Python – Bootcamp

Lecture 1: Python – Bootcamp

Chapter 3: NumPy – Array Processing

Lecture 1: NumPy

Lecture 2: NumPy arrays

Lecture 3: NumPy – Create

Lecture 4: NumPy – Reshape

Lecture 5: NumPy – Index

Lecture 6: NumPy – Operations

Lecture 7: NumPy – Sort

Lecture 8: Numpy – Stack and Split

Lecture 9: NumPy – Broadcast

Lecture 10: NumPy – Date & Time

Lecture 11: NumPy – Linear Algebra

Lecture 12: NumPy – Load & Save

Lecture 13: NumPy – Notebook Download

Chapter 4: Pandas – Data Frames and CSV Data Processing

Lecture 1: Pandas

Lecture 2: Pandas – Read data

Lecture 3: Pandas – Create Dataframe

Lecture 4: Pandas – Columns

Lecture 5: Pandas – Functions

Lecture 6: Pandas – Plotting

Lecture 7: Pandas – Count Value

Lecture 8: Pandas – Indexes

Lecture 9: Pandas – Filter & Queries

Lecture 10: Pandas – Delete Rows and Cols

Lecture 11: Pandas – Grouping

Chapter 5: Matplotlib – Plotting & Visualisation

Lecture 1: Matplotlib

Lecture 2: Matplotlib – Line plots

Lecture 3: Matplotlib – Bar charts

Lecture 4: Matplotlib – Histograms

Lecture 5: Matplotlib – Scratter plot

Lecture 6: Matplotlib – Pie chart

Lecture 7: Matplotlib – Curve plot

Lecture 8: Matplotlib – Subplots

Lecture 9: Matplotlib – Notebook Download

Chapter 6: Python RE – Regular Expressions (REGEX)

Lecture 1: REGEX

Lecture 2: Python – Import RE

Lecture 3: Python RE – Example

Lecture 4: Python RE – Characters

Lecture 5: Python RE – Alteration

Lecture 6: Python RE – Quantifiers

Lecture 7: Python RE – Greedy and Non-Greedy

Lecture 8: Python RE – Boundary Matches

Lecture 9: Python RE- Split

Lecture 10: Python RE – Substitution

Lecture 11: Python RE- Compilation Flags

Lecture 12: Python RE – Grouping

Lecture 13: Python RE – Backreferencing

Lecture 14: Python RE – Named and Non-capturing Groups

Lecture 15: Python RE – Lookarounds

Lecture 16: Python RE – Notebook Download

Chapter 7: Scikit-Learn – Machine Learning

Lecture 1: Scitkit-Learn

Lecture 2: Scikit-Learn – Training

Lecture 3: Scikit-Learn – Compare Models

Lecture 4: Scikit-Learn – Cross Validation

Lecture 5: Scikit-Learn – Selection

Lecture 6: Scikit-Learn – Evaluate Classifier

Lecture 7: Scikit-Learn – Linear Regression

Lecture 8: Scikit-Learn – Real Examples!

Chapter 8: Seaborn – Statistical Plots

Lecture 1: Seaborn

Lecture 2: Seaborn – Bar Plot

Lecture 3: Seaborn – Box Plot

Lecture 4: Seaborn – Strip Plot

Lecture 5: Seaborn – PairGrids

Lecture 6: Seaborn – Violin Plots

Lecture 7: Seaborn – Clustermaps

Lecture 8: Seaborn – Heatmaps

Lecture 9: Seaborn – Facet Grids

Lecture 10: Seaborn – KDE Plot

Lecture 11: Seaborn – Joint Plot

Lecture 12: Seaborn – Regression Plot

Lecture 13: Seaborn – Pair Plot

Chapter 9: PyTorch – Tensor Flow Project

Lecture 1: PyTorch – Initializing Tensors, Math, Indexing, Reshaping

Chapter 10: Addtional Contents

Lecture 1: How to Get the Interactive Playgrounds for this Course?

Lecture 2: Interactive Playground – FREE!

Lecture 3: Interactive Shell

Instructors

Scientific Programmer& Team
ScientificProgrammer.me | Instructor Team

Scientific Programming School
Interactive Learning Platform

Rating Distribution

1 stars: 3 votes

2 stars: 4 votes

3 stars: 8 votes

4 stars: 26 votes

5 stars: 42 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!