Last week, our Python training courses went virtual! In addition to open courses and customizable on-site courses, Continuum’s new virtual training courses are more cost effective and flexible for individuals, as well as organizations.

Python is a powerful programming language with an intuitive syntax, making it a great tool for programmers, analysts, and novices alike. Designed for individuals of varying experience levels, our virtual course topics include Practical Python Programming, Python for Finance, and Python for Science, as well as the option for customized courses.

The best way to learn is by taking a deep dive into a topic and getting a lot of hands-on experience. Driving core concept learning and in-depth understanding, Continuum’s virtual courses are led by expert Python trainers skilled in the specific material being taught. Trainers teach course material from a provided manual via a live online meeting program – giving individuals who sign up the ability to get their hands dirty, learn best practices, and ask questions about specific problems from these seasoned experts in real time.

Sign up now and get 25% off any class with the codes: blog25practical for Practical Python Programming, blog25science for Python for Science, blog25finance for Python for Finance (limit: 25 discounts available per class).

Available Courses

Focusing on practical applications and common programming problems faced by actual Python programmers of today, Continuum’s trainers teach the following courses virtually, as open courses, or as on-site classes:

Practical Python Programming

Practical Python Programming is a three day course based on teaching the basics of solving problems in scripting, data analysis, and systems programming with Python. This course assumes no prior experience with Python, but does assume participants know how to program in another programming language. Highlights of the course include learning how to work with data in Python; exploring the most popular libraries; understanding program organization, functions, classes and objects; documentation and testing; debugging, iteration and generation; and using modules to create extensible programs.

Python for Finance

For quantitative analysts and technology staff, Python for Finance provides a strong foundation, which will enable you to work and prototype much more rapidly. This course covers open source Python tools relevant to solving your day-to-day financial programming problems. Specific topics addressed include: array computation and mathematics with NumPy; statistical computation with SciPy; working with tabular data in Pandas to generate summary statistics and rolling window calculations; and using libraries for numerical optimization.

In this example, an interactive data visualization application was created in Python using Chaco, the open-source plotting toolkit written by Peter Wang. The Python for Finance course covers several Python visualization toolkits, such as Matplotlib, Chaco, and Bokeh. Students learn how some common transforms of a financial time series (like exponentially weighted moving averages with various lags and moving average convergence/divergence) can be visualized – similar to what’s seen above.

Python for Science

The Python for Science course provides a strong foundation of best practices for doing array-oriented computing with Python and will help scientists and engineers understand the vast number of tools available for doing technical computing with Python. This course covers open source Python tools relevant to solving your day-to-day scientific and engineering programming problems. Topics that are covered in class include: array computation and mathematics with NumPy; statistics, linear algebra, optimization, interpolation, and advanced computation with SciPy; and working with tabular data in Pandas to generate summary statistics and rolling window calculations.

The tools covered in the Python for Science course are heavily used by scientists and engineers in industry and academia alike to solve a variety of complex problems. Efficiently finding solutions to partial differential equations is an example of a complex problem that comes up in a broad range of scientific disciplines. In the Python for Science course trainers walk through such examples, introduce the approaches available through different, available tools, and then apply those tools in hands-on exercises like the one depicted here.

Conclusion

All of Continuum’s courses embody our philosophy of in-depth coursework taught by Python experts, free of gimmicks. In addition to virtual courses, we partner with Dave Beazley to provide open Practical Python Programming courses in Chicago year round, and custom training curriculums are available by contacting [email protected].

Sign up today for one of our virtual classes, and learn from the comfort of your own home or desk about Practical Python Programming, Python for Finance, or Python for Science. Save 25% now on any class with the codes: blog25practical for Practical Python Programming, blog25science for Python for Science, blog25finance for Python for Finance (limit: 25 discounts available per class).


About the Author

Corinna Bahr received a B.B.A. in International Business and a B.A. in Government from the University of Texas at Austin. Prior to Continuum, Corinna worked in marketing for companies specializing in data center, VPN, and online storage …

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