Connect with us


Python for Scientific Computing: Courses for Researchers and Scientists



Python for Scientific Computing: Courses for Researchers and Scientists

If we are to go down to the terminologies, scientific computing consists of two terms: science and computing. Basically, scientific computing is a method that deals with the development and applications of numerical methods and algorithms to solve complex engineering and scientific problems. And when we are having a conversation about scientific computing, there are two vital steps. 

The first one would be to catch a scientific problem and solve it using different computing methods utilizing prebuilt software and programming language. It is very obvious that we require a programming language for scientific computing at the first run. 

Since we are talking about programming languages, just like many other sectors around the world, Python has its prominence in scientific computing as well. Therefore, you can simply say that a Python certification is a must for everyone who is looking to delve into Python for scientific computing. 

Basic understanding of Scientific Computing

There are many other scientific names, such as technical computing and computational science. It basically involves computers to solve an issue rather than human brains. From performing simulations to data analysis, many tasks can be done by scientific computing. It allows us a better understanding and helps us predict complex systems’ behavior in various fields like engineering, physics, and many more. 

These days, scientific computing has proven to be more important than ever. For example, scientific computing is the power with which weather forecasts are made. Precise weather forecasting requires a better and deeper understanding of the complex interactions among land, atmosphere, ocean, and the sun. 

The traditional manual arithmetic methods and calculations are not at all sufficient to predict and model these interactions. Therefore, the computer simulations are highly required. 

Why scientists should use Python for Scientific Computing

Generally speaking, scientific computing requires a better understanding of mathematics and the ability to write programs that are efficient. Here are some of the arguments why scientists should prefer Python for scientific computing:

  • Scientists usually work with various systems ranging from data analysis packages, visualization tools, databases, and simulation codes. Each of them presents the user with a distinct set of file formats and interfaces. Due to this, a scientist might spend considerable time accumulating all the components to work in the same manner.
  • And if you are looking for something that can interoperate with the majority of the tools that you would ever require in scientific research, then Python is the key. 
  • It is common knowledge that Python is generally easy to write, especially relative to languages such as C. In Python, you can simply write what you wish to happen, and it will happen. For some, it might seem like writing a pseudo-code that is executable.
  • It is generally considered a language that is a powerful tool for developers. However, it is also accessible for astronomers. Because of tools like Jupyter and IPython notebooks, there are great opportunities for exploratory and dynamic coding and iterative data analysis on the fly. It results in low-barrier entry as scientific coding is itself explanatory and nonlinear. 
  • Open-source software generally comes with an ethos that is well-suited to the needed openness of scientific research. The ethos of the Python ecosystem and open-source software grants a framework that will help many individuals out of the crisis of scientific reproducibility. 
  • Another reason why the scientists have shown their preference for Python is that it comes with a lot of “batteries included,” and for those for which there are not any, there is Scipy available. 

Learning Python can be highly beneficial for anyone who is looking to work in the field of scientific computing and machine learning. If you are looking for a well-rounded ML course, then check out

Lesson 11 | Machine Learning Basics – Understanding Random Forest | Simplilearn        

Python Libraries For Scientific Computing

Now, let us take a look at the different Python libraries that are available for scientific computing:

  • SciPy

It is known to be the library that builds on top of NumPy (which we will mention later) and grants additional functionality for scientific computing. It includes models for optimization, linear algebra, integration, signal processing, and many more. Oftentimes, Scipy is utilized in engineering applications and scientific research.

  • Matplotlib

Matplotlib is popular as it is known to be the library for creating animated, static, and engaging visualizations in Python. It includes a broad array of plotting functions, including scatter plots, plots, and histograms. It is often utilized for engineering and scientific visualizations, analysis, and data explorations.

  • NumPy

It is a fundamental library that is specifically for scientific computing with Python. It grants a strong N-dimensional array object in addition to many other functions for performing mathematical operations on arrays. NumPy is known to be the base of many scientific libraries in Python that are for scientific computing.

  • Pandas

If you are looking for something that grants you high-performance data analysis tools and has data structures that are very easy to use, then Pandas is the Python library that you have been looking for. It comes with strong and powerful capabilities for data manipulation like grouping, merging, and filtering data. This Python library is often used for visualization, data cleaning, and data analysis. 

  • Tensorflow

For everyone who has a keen interest in deep learning and machine learning, Tensorflow is the ideal Python library for you. It grants a framework for training and building the neural networks and tools for developing models. It has wide utilization in academia and industry for a wide variety of applications of machine learning, such as NLP, computer vision, and many more.

  • Seaborn

It is a library for developing statistical graphics in Python. It develops on top of matplotlib and grants a high-level interface for developing sophisticated visualizations. It includes functions for regression models, visualizing distributions, categorical data, etc. It has its uses in both machine learning and data science fields.

Wrapping up

So there you have it! It is true that Python is one of the most popular languages today. In addition to being a valuable asset to many industries, Python is impactful in the field of scientific computing. With the information mentioned above, it would be easier to understand the correlation between Python and scientific computing.