Video description
Python has become the language of choice for most fields in the scientific community, and this is expanding to businesses investing in analytics resources. Fundamentals of Data Analytics in Python LiveLessons is a coherent, narrative tutorial that strikes the right balance between teaching the "how" and the "why" of data analytics in Python. This video begins with an abbreviated primer on Python, and then proceeds to cover open source Python tools relevant to solving day-to-day scientific and engineering programming problems.
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About the Authors:
Peter Wang is co-founder and president of Continuum Analytics. Peter holds a B.A. in Physics from Cornell University and has been developing applications professionally using Python since 2001. Before co-founding Continuum Analytics in 2011, Peter spent seven years at Enthought designing and developing applications for a variety of companies, including investment bankers, high-frequency trading firms, oil companies, and others. In 2007, Peter was named Director of Technical Architecture and served as client liaison on high-profile projects. Peter also developed Chaco, an open-source, Python-based toolkit for interactive data visualization. Peter's roles at Continuum Analytics include product design and development, software management, business strategy, and training.
Aron Ahmadia is a research scientist at Continuum Analytics. Aron holds a Ph.D. in Applied Mathematics from Columbia University and has been working with Python for technical computing since 2003. Aron is one of the lead developers of PyClaw, an open-source, Python-based toolkit for modeling wave propagation at large scale. His software is used in academia, industry, and government, and runs on workstations, the cloud, and 65,536-core supercomputers. Aron has extensive experience teaching Python to industrial, academic, and government users, and has taught it as part of the curriculum for Software Carpentry courses across four continents.
Table of Contents
Introduction
Introduction to Fundamentals of Data Analytics in Python LiveLessons
00:05:25
Lesson 1: Getting Set Up with the Analytical Python Ecosystem
Learning objectives
00:00:55
1.1 Obtain the software
00:03:25
1.2 Explore the Python command line from IPython
00:03:22
1.3 Experiment and chronicle in the IPython Notebook
00:04:56
Lesson 2: Basic Data Analysis with Python
Learning objectives
00:00:42
2.1 Retrieve data from the web
00:05:25
2.2 Load, access and modify dictionaries of data
00:08:45
2.3 Analyze data in Python dictionaries
00:07:30
2.4 Understand advanced Python analytics
00:03:14
2.5 Visualize Python dictionary data with matplotlib
00:07:03
Lesson 3: Numerical Analysis with NumPy
Learning objectives
00:00:42
3.1 Understand NumPy
00:03:22
3.2 Create arrays of data in NumPy
00:07:47
3.3 Access and modify array elements
00:04:36
3.4 Compute on arrays
00:08:16
3.5 Understand advanced NumPy
00:25:35
Lesson 4: Advanced Analytics with SciPy and sci-kit learn
Learning objectives
00:00:36
4.1 Understand SciPy
00:00:59
4.2 Compute means, medians, quartiles, and other statistics
00:03:22
4.3 Fit data and interpolate
00:08:40
4.4 Understand sci-kit learn
00:03:44
Lesson 5: Tabular Data Analysis with Pandas
Learning objectives
00:00:58
5.1 Understand Pandas
00:02:18
5.2 Use series objects
00:04:21
5.3 Understand dataframes objects
00:04:28
5.4 Handle missing data
00:03:06
5.5 Perform input and output
00:04:43
5.6 Understand advanced Pandas
00:09:15
Lesson 6: Overview of Python Visualization Tools
Learning objectives
00:00:41
6.1 Understand Python’s graphical data exploration tools
00:11:52
6.2 Embed Python charts into graphical applications
00:06:12
6.3. Create web-based graphics from Python
00:10:46
6.4 Visualize 3D datasets using Python
00:04:07
Summary
Summary of Fundamentals of Data Analytics in Python LiveLessons
00:02:44