About This Course
This 3-day intensive Python training class provides practical, hands-on experience and foundational working knowledge of Python for data analysis, science, engineering, and other technical applications. Whether you are new to Python or a long-time enthusiast, you’ll benefit from this focused series of topics and best practices taught by experts who create Python software for notable companies in finance, oil and gas, scientific research, aerospace, biotechnology, marketing analysis and more.
The Python Foundations Core Class will get you up to speed quickly on how to optimize your use of the Python standard language and key Python packages for data exploration, modeling, and analysis. You’ll leave with:
- Hands-on experience setting up a fully functioning integrated analysis environment and popular Python tools for scientific and numeric computing
- An understanding of how to use the Python standard library to write programs, access various tools, and document and automate analytic processes
- Orientation to some of the most powerful and popular Python libraries for modeling and analysis, including Pandas (data preparation, analysis, and modeling; time series analysis), NumPy (fast numerical computing), and matplotlib (data visualization)
Course Syllabus & Topics
What You’ll Learn
The class will give you the initial building blocks to effectively use Python in your daily work, while setting the foundation for additional skill building in areas of specific interest.
Experience with Python is helpful (but not required). However, programming experience in some language (such as R, MATLAB, SAS, Mathematica, Java, C, C++, VB, or FORTRAN) is expected. In particular, participants need to be comfortable with general programming concepts like variables, loops, and functions.
We kick off the class by exploring the functionality of the IPython Shell, an enhanced interactive science-centric console. Next we review the Jupyter Notebook, a cell-based environment that renders scripts, plots, and rich media in a web-like interface, making it ideal for sharing and publishing analysis with peers. You’ll leave with a mastery of these tools that will accelerate your productivity and facilitate collaboration.
1. Building a Solid Infrastructure to Go From Exploratory Analysis to Reproducible Workflows
A. Introduction and Setting Up Your Integrated Analysis Environment
- Canopy: Integrated Analysis Environment
- IPython Shell
- Custom environment settings
- Jupyter (IPython) Notebooks
- Script editor
- Packages: NumPy, Pandas, matplotlib, etc.
Next we move into an introduction to Python’s core language features that form part of your universal toolkit for tasks ranging from initial data exploration to extensible application development. We’ll introduce Python’s built-in data structures, including how and where each might be used and what trade-offs are present, and we’ll cover Python’s looping and control flow constructs. Along the way we’ll provide insight into Python’s design choices that will help you understand why Python works the way it does.
1. Using Python to Control and Document Your Workflow
- Data types and objects
- Loading packages, namespaces
- Reading and writing data
- Simple plotting
- Control flow
- Code profiling
There are a number of “must-have” packages for scientific computing and data analysis with Python. We’ll review three of these in this class that will give you the underpinnings you need to be able to expand your knowledge into additional packages that fit your area of specialization. If you are coming from a background in MATLAB®* or R, you’ll find these libraries essential.
Chief among these packages is NumPy, a tool for rapidly manipulating and processing large data sets. Whether you are a scientist writing short scripts to analyze and plot your analytical results or an analyst writing large-scale quantitative finance applications for Wall Street, NumPy should be part of your toolbox. We give you a jump start with the basics in the classroom, then provide you additional curated lectures to extend your understanding.
Once you’ve crunched your data, you’ll want to visualize it, which is where matplotlib comes in. Matplotlib is a versatile 2D plotting library that allows you to generate plots, histograms, power spectra, bar charts, error charts, scatter plots, and more with just a few lines of code.
Finally, we do a deep dive into the Python Data Analysis Library (Pandas), a powerful package for working with multi-dimensional datasets. Pandas’ powerful data aggregation and reorganization capabilities, including support for labeling data along each dimension, missing values, and time series manipulations, have made Python an indispensable tool for data exploration and analysis.
1. Numerical Analysis, Data Exploration, and Data Visualization with NumPy Arrays and Matplotlib
- The NumPy array
- Indexing and slicing arrays
- Array operations and manipulations
- 2D plotting with matplotlib
2. Data Wrangling, Exploration, and Analysis with Pandas
- 1D and 2D data structures: Series and DataFrame
- Pandas I/O
- Data visualization
- Data manipulation (alignment, aggregation, and summarization)
- Statistical analysis with Pandas
- Date and time series analysis with Pandas
Open Class Schedule
Open Class Schedule
Onsite corporate classes are also available. Discounts are available for 3 or more attendees and academics currently at a degree-granting institution. Contact us with the form to the right to learn more.
|Where||When||Price (per person)||Reserve a Seat|
|Albuquerque, NM||February 24-26, 2020||$1700||Register Online|
|Los Alamos, NM||March 2-4, 2020||$1700||Register Online|
|Albuquerque, NM||March 9-11, 2020||$1700||Register Online|
|Albuquerque, NM||April 20-22, 2020||$1700||Register Online|
|Albuquerque, NM||May 18-20, 2020||$1700||Register Online|
|Albuquerque, NM||June 8-10, 2020||$1700||Register Online|
|Los Alamos, NM||June 22-24, 2020||$1700||Register Online|
I am worried that your training is only useful to people who are committed to using Enthought software products. How much of your training is usable without Enthought software?
Our training teaches students how to write software with Python and solve problems using its scientific packages, not how to use proprietary software. Everything you will learn uses free and open source software.
We provide Enthought Canopy (our integrated analysis environment and Python distribution) to training participants to ensure they have all of the tools and Python packages they need to complete the training and that the tools are as easy as possible to install. While participants sometimes do use other editors, package managers, and Python distributions, we strongly recommend participants use Canopy during the training. With Canopy we can ensure that you can easily install everything you need for the course out of the box and we can provide technical support (which we unfortunately cannot provide for other tool sets).
I use / will be using Anaconda Python. Will I still benefit from this course?
Absolutely. Our training materials work with any Python distribution (such as Anaconda), as long as you also have all of the necessary packages, a text or code editor, package manager, interactive IPython shell, and Jupyter notebooks installed.
Is a class completion certificate provided?
Yes, a class completion certificate is provided for the Python Foundations class.
Have a question that isn’t answered here? Contact us or call 512.536.1057.
Python Foundations Attendee
Python Foundations Attendee
Python Foundations Attendee
National Research Laboratory
Sandia Research Laboratories