IPython and Jupyter Notebook In Practice: 3-in-1

Course Curriculum

Chapter 1: Learning IPython for Interactive Computing and Data Visualization

Lecture 1: The Course Overview

Lecture 2: Installing Python with Anaconda

Lecture 3: Introducing the Notebook

Lecture 4: A Crash Course on Python

Lecture 5: More on Python Functionalities

Lecture 6: Ten Jupyter/IPython Essentials – I

Lecture 7: Ten Jupyter/IPython Essentials – II

Lecture 8: Exploring a Dataset in the Notebook

Lecture 9: Manipulating Data

Lecture 10: Complex Operations

Lecture 11: A Primer to Vector Computing

Lecture 12: Creating and Loading Arrays

Lecture 13: Basic Array Manipulations

Lecture 14: Computing with NumPy Arrays

Lecture 15: Choosing a Plotting Backend

Lecture 16: matplotlib and seaborn Essentials

Lecture 17: Image Processing

Lecture 18: Accelerating Python Code with Numba

Lecture 19: Distributing Tasks on Several Cores with IPython.parallel

Lecture 20: Creating a Custom Magic Command

Lecture 21: Writing a New Jupyter Kernel

Lecture 22: Displaying Rich HTML Elements in the Notebook

Chapter 2: Interactive Computing with Jupyter Notebook

Lecture 1: The Course Overview

Lecture 2: Introducing IPython and the Jupyter Notebook

Lecture 3: Getting Started with Exploratory Data Analysis in the Jupyter Notebook

Lecture 4: Introducing the Multidimensional Array in NumPy for Fast Array Computations

Lecture 5: Creating an IPython Extension with Custom Magic Commands

Lecture 6: Architecture of the Jupyter Notebook

Lecture 7: Converting a Jupyter Notebook to Other Formats with nbconvert

Lecture 8: Mastering Widgets in the Jupyter Notebook

Lecture 9: Creating Custom Jupyter Notebook Widgets in Python, HTML, and JavaScript

Lecture 10: Configuring the Jupyter Notebook

Lecture 11: Evaluating the Time Taken by a Command in IPython

Lecture 12: Profiling Your Code Easily with cProfile and IPython

Lecture 13: Profiling Your Code Line-by-Line with line_profiler

Lecture 14: Profiling the Memory Usage of Your Code with memory_profiler

Lecture 15: Understanding the Internals of NumPy to Avoid Unnecessary Array Copying

Lecture 16: Processing Large NumPy Arrays with Memory Mapping

Lecture 17: Using Python to Write Faster Code

Lecture 18: Accelerating Pure Python Code with Numba and Just-In-Time Compilation

Lecture 19: Accelerating Array Computations with NumExpr

Lecture 20: Accelerating Python Code with Cython

Lecture 21: Releasing the GIL to Take Advantage of Multi-Core Processors

Lecture 22: Writing Massively Parallel Code for NVIDIA Graphics Cards (GPUs)

Lecture 23: Distributing Python Code Across Multiple Cores with IPython

Lecture 24: Interacting with Asynchronous Parallel Tasks in IPython

Lecture 25: Performing Out-of-Core Computations on Large Arrays with Dask

Lecture 26: Using Matplotlib Styles

Lecture 27: Creating Statistical Plots Easily with Seaborn

Lecture 28: Creating Interactive Web Visualizations with Bokeh and HoloViews

Lecture 29: Creating Plots with Altair and the Vega-Lite Specification

Chapter 3: Statistical Methods and Applied Mathematics in Data Science

Lecture 1: The Course Overview

Lecture 2: Exploring a Dataset with pandas and Matplotlib

Lecture 3: Estimating the Correlation Between two Variables

Lecture 4: Fitting a Probability Distribution to Data with the Maximum Likelihood Method

Lecture 5: Estimating a Probability Distribution Non-parametrically

Lecture 6: Analyzing Data with the R Programming Language

Lecture 7: Getting Started with scikit-learn

Lecture 8: Learning to Recognize Handwritten Digits

Lecture 9: Using Support Vector Machines for Classification Tasks

Lecture 10: Using a Random Forest to Select Important Features for Regression

Lecture 11: Detecting Hidden Structures in a Dataset with Clustering

Lecture 12: Finding the Root of a Mathematical Function

Lecture 13: Minimizing a Mathematical Function

Lecture 14: Fitting a Function to Data with Nonlinear Least Squares

Lecture 15: Finding the Equilibrium State of a Physical System

Lecture 16: Analyzing the Frequency Components of a Signal

Lecture 17: Applying a Linear Filter to a Digital Signal

Lecture 18: Computing the Autocorrelation of a Time Series

Lecture 19: Manipulating the Exposure of an Image

Lecture 20: Applying Filters on an Image

Lecture 21: Segmenting an Image

Lecture 22: Finding Points of Interest in an Image

Lecture 23: Applying Digital Filters to Speech Sounds

Lecture 24: Creating a Sound Synthesizer in the Notebook

Lecture 25: Plotting the Bifurcation Diagram of a Chaotic Dynamical System

Lecture 26: Simulating an Elementary Cellular Automaton

Lecture 27: Simulating an Ordinary Differential Equation with SciPy

Lecture 28: Simulating a Partial Differential Equation-Reaction-Diffusion Systems

Lecture 29: Simulating a Discrete-time Markov Chain

Lecture 30: Simulating a Poisson Process

Lecture 31: Simulating a Brownian Motion

Lecture 32: Simulating a Stochastic Differential Equation

Lecture 33: Manipulating and Visualizing Graphs with NetworkX

Lecture 34: Drawing Flight Routes with NetworkX

Lecture 35: Resolving Dependencies in a Directed Acyclic Graph

Lecture 36: Computing Connected Components in an Image

Lecture 37: Manipulating Geospatial Data with Cartopy