Software Engineering for Data Scientists

4 min readMay 26, 2023

[Disclaimer: This post contains Github/book/article affiliate links]

A scalable solution can be developed through the flexible combination of several disciplines. To start this post, I want to start with the following question.

Why data scientists and analysts ought to have a working knowledge of software engineering in Python?

There are several good reasons why data scientists and analysts, particularly Python, need a solid grounding in software engineering ideas and techniques.

Coding Efficiency: Computationally intensive data science projects sometimes include working with enormous datasets and complicated algorithms. The performance and scalability of data scientists’ solutions can be enhanced using what they’ve learned in software engineering courses.
Data science projects rely heavily on the reproducibility of software engineering principles like version control, modular code organization, and documentation. To effectively share and validate findings, data scientists should adhere to best practices that allow them to keep track of changes, engage with team members, and provide analyses that can be reproduced by others.
Teamwork: Software developers, data engineers, and other stakeholders are typically required to work together on data science initiatives. Data scientists with a firm grasp of software engineering practices are better equipped to cooperate with their peers and ensure the seamless incorporation of their findings into bigger software systems.
The principles of software engineering encourage the creation of reliable and easy-to-maintain code. Data scientists can create less complicated code to understand, debug, and maintain if they adhere to coding standards, write modular and reusable routines, and include error-handling techniques.
The need to test code to verify its correctness and durability is emphasized in software engineering. Data scientists need to be well-versed in testing frameworks and procedures to ensure the quality of their code, find bugs, and have faith in the results of their models and analyses.
Deployment & Productionization: Deploying models & solutions into production environments are common in data science projects. Data science applications require software engineering expertise for packaging, containerization, and deployment. Data scientists fluent in concepts like application programming interfaces (APIs), web frameworks, and cloud services can better operationalize their work.
Data scientists need to be versed in CI/CD pipelines and techniques to ensure timely and reliable deployments of their work. They can improve the rate of iteration and the consistency with which data-driven solutions are delivered by integrating their code into continuous integration platforms, which automate testing, build processes, and deployments.
Large-scale data processing, machine learning models, and distributed computing are common tasks for data scientists. With their software engineering expertise, they can create scalable designs, take advantage of distributed computing frameworks, and fine-tune their code for optimal performance.

Tutorials on getting started with Python for studying software engineering implementation in the machine learning area:

Fundamentals of Python and ML:

Review the syntax and rudiments of Python. Codecademy has a Python tutorial available: https://www.codecademy.com/learn/learn-python-3.
Master the foundations of artificial intelligence. The “Machine Learning” course taught by Andrew Ng is available on Coursera at https://www.coursera.org/learn/machine-learning.

2. Scikit-Learn Tutorials:

The Python library scikit-learn is widely used in machine learning. For further information, check out their official tutorials and documentation at https://scikit-learn.org/stable/docs.html. Scikit-learn’s iris flower classification lesson (https://scikitlearn.org/stable/auto_examples/datasets/plot_iris_dataset.html) should be implemented.

3. Engineering of Features and Data Preprocessing:

Acquire knowledge of data preprocessing methods like data cleansing, missing value management, and feature scaling. Use scikit-learn to preprocess your data: https://scikitlearn.org/stable/modules/preprocessing.html.

4. Assessment of Model Performance:

Learn to use and implement various machine learning algorithms.
Scikit-learn provides a comprehensive framework for training and assessing models.

5. Applying Models Learned from Machines:

Test out various strategies for putting machine learning models into production.
Use Flask to release a model trained with Scikit-learn: https://towardsdatascience.com/productionize-a-machine-learning-model-with-heroku-8201260503d2

6. Standards for Software Development:

Learn the fundamentals of software engineering and how to apply it to machine learning initiatives.
Learn about clean coding approaches by reading “Clean Code: A Handbook of Agile Software Craftsmanship” by Robert C. Martin.

7. Deployment and CI/CD:

https://docs.pytest.org/en/latest/ Unit testing for your machine learning code with pytest: Learn about CI/CD and production-ready machine learning model deployment technologies and best practices.
Discover Docker’s containerization features: Implement Continuous Integration and Continuous Deployment using GitLab for your machine learning project: https://docs.gitlab.com/ee/ci/ .

There are links to Github projects below:

I believe they will be useful to you in your pursuit of professional development in the same way they were to me. Do you have any other suggestions? Post them below for discussion!

Software Engineering for Data Scientists

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Written by Swayanshu Shanti Pragnya