Computing Reviews
Today's Issue Hot Topics Search Browse Recommended My Account Log In
Review Help
Search
Fundamentals of data analytics in Python LiveLessons
Wang, P.; and Ahmadia, A.Addison-Wesley Professional,03:00:00,published onOct 18, 2013,informIT,http://www.informit.com/store/fundamentals-of-data-analytics-in-python-livelessons-9780133599459.Type:Video
Date Reviewed: Feb 8 2016

Python is a dynamically typed interpretive programming language created by Guido van Rossum in the late 1980s. It has been in extensive use for scientific computing, but is now gaining popularity among big data enthusiasts for its powerful libraries for data manipulation and analysis.

“Fundamentals of Data Analytics in Python LiveLessons” is a 185-minute video tutorial with six lessons, each varying between 12 to 50 minutes in length. The lessons are based on Python version 2.7.3, although there is nothing in the videos that is version-specific and the content should easily carry forward to a more recent release. The first lesson helps the viewer set up the Python development environment including many of its open-source data and scientific analysis libraries. Two freely available independent sources are explored, Anaconda from Continuum Analytics [1] and Canopy from Enthought [2]. Both sources are straightforward to install on Linux, OS X, and Windows platforms. A third option is also available where no software installation is needed, and the interested viewer can use a cloud-based environment called Wakari from Continuum Analytics [3]. Around 8 minutes into the video, the presenter shows the most useful way of experimenting with and chronicling one’s data analysis using IPython Notebook. The notebook allows one to capture Python code, its execution, and visualization results along with documentation into a nicely formed presentation that can be shared with others.

The focus of lesson 2 is capturing, manipulating, analyzing, and visualizing data from a remote source, such as a website, using standard Python libraries. The data is captured in JavaScript Object Notation (JSON) format and visualized with Matplotlib, a plotting library for the Python programming language. Lesson 3 is a tutorial on NumPy, Python’s numerical analysis library, and discusses how data can be manipulated using the array data structure. This library is a foundation for the core scientific computing packages in Python, which include SciPy, Pandas, and Matplotlib. SciPy and Sci-Kit are the focus of lesson 4. SciPy is a library for scientific computing, whereas Sci-Kit is a library for machine learning. The presenters barely scratch the surface of these two and only give very basic examples of statistics including fitting the data, interpolation, and machine learning. Lesson 5 is on Pandas, a library for analysis of tabular data using Series and DataFrame data structures. Lesson 6 gives a broad survey and overview of 2D and 3D data visualization tools available in Python.

This video is a self-paced tutorial available for download from InformIT.com, a Pearson Education website, for $199.99. Much of the presentation is done using IPython Notebook with concepts explained by stepping through and executing Python code. The presenters, however, occasionally switch to static PowerPoint slides and move through the content at a much faster pace. The first three and the fifth lessons are more detailed, but lessons 4 and 6 are at best an overview of the topics covered.

The tutorial is designed more as a broad-brush presentation. Those looking to get an overview of the Python ecosystem and its capabilities in solving scientific and engineering problems would benefit the most from these lessons. However, those looking to get a good grounding in the fundamentals of data analytics in Python will find themselves wanting more. The videos are designed more as an informational rather than an instructional resource with very few opportunities to delve deeper into a topic, or pause for self-exploration through exercises.

Video

Reviewer:  Raghvinder Sangwan Review #: CR144152 (1607-0517)
1) Anaconda from Continuum Analytics. Continuum Analytics, https://www.continuum.io/downloads (01/24/2016).
2) Download Canopy. Enthought, https://www.enthought.com/downloads (01/24/2016).
3) Web-based Python Data Analysis. Continuum Analytics, http://wakari.io (01/24/2016).
Bookmark and Share
  Featured Reviewer  
 
Python (D.3.2 ... )
 
 
Data Mining (H.2.8 ... )
 
Would you recommend this review?
yes
no
Other reviews under "Python": Date
Practical Python
Hetland M., APress, LP, 2002.  648, Type: Book (9781590590065)
Mar 28 2003
Python programming: an introduction to computer science
Zelle J., Franklin B, 2003. Type: Book (9781887902991)
Dec 2 2004
Foundations of Python network programming
Goerzen J., APress, LP, Berkeley, CA, 2004.  512, Type: Book (9781590593714)
Dec 26 2004
more...

E-Mail This Printer-Friendly
Send Your Comments
Contact Us
Reproduction in whole or in part without permission is prohibited.   Copyright 1999-2024 ThinkLoud®
Terms of Use
| Privacy Policy