English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 38m | — MB
Delve into the fundamentals of the platform: Python, IPython, and the Jupyter Notebook while exploring data analysis tasks on real-world datasets
Python is a user-friendly and powerful programming language. IPython offers a convenient interface to the language and its analysis libraries, while Jupyter Notebook is a rich environment, well-adapted to data science and visualization. Together, these open-source tools are widely used by beginners and experts around the world, and in a huge variety of fields and endeavors. This course is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this course, you will be able to perform in-depth analyses of all sorts of data.
This is a hands-on, beginner-friendly guide to analyzing and visualizing data on real-world examples with Python and Jupyter Notebook.
What You Will Learn
- Install Anaconda and code in Python in Jupyter Notebook
- Load and explore datasets interactively
- Perform complex data manipulations effectively with pandas
- Create engaging data visualizations with matplotlib and seaborn
- Simulate mathematical models with NumPy
- Visualize and process images interactively in Jupyter Notebook with scikit-image
- Accelerate your code with Numba, Cython, and IPython.parallel
- Extend the Notebook interface with HTML, JavaScript, and D3
Table of Contents
Getting Started with IPython:
01 The Course Overview
02 Installing Python with Anaconda
03 Introducing the Notebook
04 A Crash Course on Python
05 More on Python Functionalities
06 Ten Jupyter_IPython Essentials – I
07 Ten Jupyter_IPython Essentials – II
Interactive Data Analysis with pandas:
08 Exploring a Dataset in the Notebook
09 Manipulating Data
10 Complex Operations
Numerical Computing with NumPy :
11 A Primer to Vector Computing
12 Creating and Loading Arrays
13 Basic Array Manipulations
14 Computing with NumPy Arrays
Interactive Plotting and Graphical Interfaces:
15 Choosing a Plotting Backend
16 matplotlib and seaborn Essentials
17 Image Processing
High-Performance and Parallel Computing:
18 Accelerating Python Code with Numba
19 Distributing Tasks on Several Cores with IPython parallel
Customizing IPython:
20 Creating a Custom Magic Command
21 Writing a New Jupyter Kernel
22 Displaying Rich HTML Elements in the Notebook
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