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Python for Data Analysts and Quants Training Course

20 January 2020

How Python is helping Data Analysts and Quants

Python continues to be the world’s most popular programming language (according to IEEE Spectrum rankings) and it’s easy to understand why. It’s a highly productive language, is relatively easy to learn because of its readable programming syntax and has a rich set of libraries and frameworks. Some of those tools are extremely helpful for data analysts and quants – and make their lives much easier. Let’s look at some of the most important ones in a bit more detail.

Python Data Science Tools

NumPy, which is short for Numerical Python, is one of the main building blocks for scientific and statistical computing in Python. It provides high-speed manipulation of multi-dimensional arrays allowing you to create data structures and perform fast mathematical operations on them. The NumPy library has a wide range of complex mathematical operations including random number generation (RNG) and Fourier Transform (FT).

NumPy has been around since the mid-2000s and its longevity means that most data analysis packages in Python leverage it in some way. Pandas, for example, is a higher-level library that uses NumPy to support sophisticated analytics with high-speed computation.

Pandas is probably the next most important Python tool for data analysts and quantitative analysts after NumPy. Its name is derived from “Python and data analysis” and the econometrics term “panel data” (which are multi-dimensional data involving measurements over time). The clue is in the name, and pandas is all about data analysis and manipulation.

Pandas can be thought of as your data’s home and you can use it to clean, transform and analyse them. You can bring them in from multiple sources and in multiple formats, and then create Series and DataFrame objects in pandas. Once the data are in the right shape, you can analyse them to extract useful insights. Pandas also lets you visualise your complex data structures to make them easier to understand, analyse and communicate.

SciPy is another Python library used by data professionals. It again builds on the NumPy array object and provides a package of helpful numerical and scientific algorithms. SciPy is not required by pandas but is considered an extremely useful add-on (an “optional dependency” according to the pandas project).

Other Benefits of Python

There are of course other programming languages for data analysis and modelling. C++ and R are both very popular and run faster than Python. But Python has a number of key advantages for data analysts and quants:

· High Productivity – it has a clean object-orientated design and concise programming syntax, meaning it requires less time and effort to write the code

· Support Libraries – Python provides a general approach to data analysis and lets you do most tasks with just a few libraries (NumPy, pandas and SciPy)

· Large Community – as the most popular programming language today, Python has a massive global community that is driving development


If your job involves data analytics, you really should learn Python.

Here at JBI Training, we provide a range of cutting-edge Python training courses that include:

· Python for Data Analysts and Quants training course (3 days) where you learn to use Python and its statistical computing libraries to analyse and visualise your data and gather actionable insights – See our Python for Data Analysts & Quants training course outline

· Python training course (3 days) where you gain a comprehensive introduction to Python and see how it is used for data analytics and rapid application development – See our Python training course outline

· Python for Financial Traders training course (3 days) where you learn to use Python and its statistical computing libraries to analyse and visualise your financial data and gather valuable insights – See our Python for Financial Traders training course outline




About the author: Craig Hartzel
Craig is a self-confessed geek who loves to play with and write about technology. Craig's especially interested in systems relating to e-commerce, automation, AI and Analytics.

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