The 3 Most Important Programming Languages for the Scientific Research Field
Starting research in the sciences can be a difficult and evolving journey. That being said, coding is essential for any life science research. It can be applied to signal process, data analysis, and more.
Nearly all research papers could be applied to machine learning and other programming languages. They can be formatted into apps and websites that benefit the community in an accessible way as well.
Python Programming
Python is said to be the easiest programming language for most beginners in the programming field. I would say that the most applicable use for Python in science, is machine learning.
Science research comes in the form of numbers, images, and even audio (which can be translated to graphs and numbers using the Fast Fourier Transform). These two main forms of data is the playhouse for machine learning.
Linear Regression is when a trained machine learning model is able to process different variables that affect the groups that are being experimented.
For example, an experimental group the part of an experiment that receives the variable being tested. The control group is the part in an experiment that does not receive the change.
In a research project, there are likely more than one changing variable between the control and experimental groups. If numbers are used to describe the changes between these variables, or classes, we can code a python linear regression model to predict the test variable.
For example, suppose we are given a dataset for patients with diabetes, variables or columns are glucose, blood pressure, diabetes — yes-or-no, etc. The variable we test for would be diabetes — yes-or-no, and we would pass in the other columns like glucose, blood pressure, etc.
Finally, there are train and test datasets. For instance, suppose we have 500 patients in the diabetes — yes-or-no dataset. You would split the dataset into test variables that you pass into the machine learning model a certain number of times.
Basically, the model iterates over, say, 300 patients and analyzes what variables affect the test (diabetes — yes-or-no) variable the most. The model does this process how many times we pass through (epochs).
Suppose we pass in 16 epochs, the model goes through each individual patient of the train dataset (300 patients), 16 different times, each time random, so each instance is different than the last one.
Convolutional Neural Networks work the same manner, but they are given images which they then turn into numeric data by analyzing the grayscale at each pixel, on a scale from 0–255.
These are the two main machine learning models for scientific research with python programming.
MATLAB Programming
MATLAB is an essential software language that can assist with data signal processing. It helps to make figures for publications as well as processing different types of data.
In addition, MATLAB also has a variety of machine learning additions that can help with implementing AI into your research.
The best part about this language is its simplicity and ability to easily work with large amounts of data.
Also, MATLAB offers many different functions for different parts of scientific computing.
For instance, there are filtering functions like lowpass, notch filtering, and bandstop/bandpass.
There are also filters that can help with actual data acquisition like thresholding for Burst Suppression Ratio.
For someone to get started with MATLAB, I think you should begin with the Onramp course that they offer, and it is not as difficult as it may seem.
R
From what I have heard and seen online R is very useful for various parts if data analysis upon acquiring the data.
R is purely used for data statistics and analysis which can be helpful when you are trying to find significance with your research.
R can also interface with many other databases like SQL, NoSQL, and more. R has more libraries specifically made for data analysis and stats than Python does.
This makes it possible to run complex statistic tests and receive an output with a neater looking graph for publication.
Whether your research involves numeric or image data, I think these three languages will definitely help with publications.
Using these three programming languages would be a seamless integration of machine learning (MATLAB or Python), statistics (R), and data processing (MATLAB).
Conclusion
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