Python for Finance Training in Dublin, Ireland

Enroll in or hire us to teach our Python for Finance class in Dublin, Ireland by calling us @303.377.6176. Like all HSG classes, Python for Finance may be offered either onsite or via instructor led virtual training. Consider looking at our public training schedule to see if it is scheduled: Public Training Classes
Provided there are enough attendees, Python for Finance may be taught at one of our local training facilities.
We offer private customized training for groups of 3 or more attendees.

Course Description

This course begins with an abbreviated primer on Python (language syntax, data structures, basic data processing, Python functions, modules and classes). The remainder of the 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. To interface with the rest of your libraries, we teach ways of wrapping C/C++ and/or Fortran code in Python, along with ways to compile Python for dramatically improved performance. We will also cover how to call Python functions from Excel, manipulate spreadsheets and documents using Python, and expose your Python code over the web.
Course Length: 4 Days
Course Tuition: $1290 (US)


Geared toward quantitative analysts and technology staff, Python for Finance provides a strong foundation which will enable you to work and prototype much more rapidly.

Course Outline


The course is taught over four days and covers the Python language and critical library modules for finance and engineering, including data visualization.

    Introduction to Python.
    The first section of the course is an introduction to the Python programming language. It introduces the details of how to start and stop the interpreter and write programs, and explains basic data-types, files, functions, and error handling.

    Working with Data.
    Section 2 provides a detailed tour of how to represent and work with data in Python. It covers tuples, lists, dictionaries, and sets, as well as NumPy arrays. Students will learn the critical aspects of Python's underlying object model including variables, reference counting, copying, and type checking and how to effectively use Python's very powerful list processing primitives. More information about how to organize larger programs into functions is provided in Section 3. A major focus is placed on design functions and the technical details of functions, including scoping rules and documentation strings.

    Program Organization and Functions
    More information about how to organize larger programs into functions is provided in Section 3. A major focus is placed on design functions and the technical details of functions, including scoping rules and documentation strings.

    Modules and Libraries.
    Section 4 addresses how to organize programs into modules and use those modules as a tool for creating extensible programs. It concludes with a tour of some of the most commonly used library modules including those related to system administration, text processing, subprocesses, XML parsing, binary data handling, and databases.

    Classes and Objects.
    In Section 5, students are given an introduction to object-oriented programming in Python. Topics such as how to create new objects, overload operators, and utilize Python special methods will be explained, along with the basic principles of object oriented programming including inheritance and composition.

    NumPy is Python's array and mathematics library. Section 6 covers how one can and should use NumPy to simplify and speed up code. It starts with a brief overview of NumPy's memory model and teaches ways to slice and dice data using NumPy's sophisticated indexing operations. Mathematical use cases, various interpolation functions, matrix decompositions, computing eigenvalues, solving systems of equations and inverting matrices are also covered.

    SciPy is Python's scientific complement to NumPy. SciPy is quite broad and used in many different domains. Section 7 covers key areas relevant to finance, starting with SciPy's statistical functions, how to generate different distributions and perform statistical computations. It also covers some interpolation, linear algebra, optimizations, signal processing, and fourier transforms. These are useful in efficiently computing some moving window functions (moving averages, exponential moving averages, bollinger bands).

    Pandas is Python's answer to the R dataframe, which provides a very convenient way to work with tabular data. It helps organize your data by handling missing data and data alignment automatically. In Section 8, students learn about Pandas ability to do simple olap aggregations and drill downs without additional code, as well as executing efficient computations for statistical functions, like moving window functions and rolling regressions.

    Integrating with Native Code.
    Python is fantastic, but often we have to work with code in existing C/C++ libraries. One of the easiest ways to do this is with Python's built-in ctypes libraries, or if you can invest a little more, Cython - a Python to C compiler. Cython can be used to speed up Python code dramatically. In Section 9, students will examine several use cases that show how to write Python code first and optimize for speed later.

    Production Python in Finance.
    Python is fantastic for data analysis. One key feature allows users to go from quantitative analysis to a production application extremely rapidly. In Section 10, students will learn either how to wrap Python analysis code up in a web application for effortless deployment or to script in Excel using Python.

    Numerical Optimization. One reason that quants love Matlab is that it makes it very easy to solve numerical optimization problems. We cover the main optimization libraries so you know what's out there, as well as teach you how to use them to solve real world problems.

    Data Visualization.
    Matplotlib is a Python visualization library which makes it easy to generate static plots. Section 12 will show students how to use Matplotlib to create basic line plots, scatter plots, density estimation plots, as well as date handling. If you have any specific plotting requirements, let us know and we can cover those use cases as well.

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Python Programming Uses & Stats

Python Programming is Used For:
Web Development Video Games Desktop GUI's Software Development
Year Created
Easy to Learn:
The learning curve is very mild and the language is versatile and fast to develop.
Massive Libraries:
You can find a library for basically anything: from web development, through game development, to machine learning.
Do More with Less Code:
You can build prototypes and test out  ideas much quicker in Python than in other language

Speed Limitations:

It is an interpretive language and therefore much slower than compiled languages.

Problems with Threading:

Multi-threaded CPU-bound programs may be slower than single-threaded ones do to the Global Interpreter Lock (GIL) that allows only one thread to execute at a time.

Weak on Mobile:

Although, there are a number or libraries that provide a way to develop for both Android and iOS using Python currently Android and iOS don’t support Python as an official programming language.

Python Programming Job Market
Average Salary
Job Count
Top Job Locations

New York City

Mountain View

San Francisco

Complimentary Skills to have along with Python Programming
The potential for career growth, whether you are new to the industry or plan to expand your current skills, depends upon your interests:
  - For knowledge in building in PC or windows, phone apps or you are looking your future in Microsoft learn C#
  - For android apps and also cross platform apps then learn Java
  - If you are an Apple-holic and want to build iOS and MAC apps and then choose Objective C or Swift
  - Interested in game development? C++
  - Data mining or statistics then go with R programming or MATLAB
  - Building an operating systems? C

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