Software Engineering Roadmap For Data Scientists

Data scientists are software engineers first and foremost. They may not be coding machine learning models or natural language processing algorithms on a day-to-day basis, but the work they do as data scientists requires software engineering and programming skills to be able to apply all the data science project life cycles on the data. In addition to that, they should have the ability to understand the needs of users and develop solutions for those needs is essential for any data scientist working in an organization.

In this blog, I will give you a roadmap to develop your software and programming skills as a data scientist. This roadmap is not for absolute beginners but it is mainly for people who already have strong programming skills and would like to take it to the next level.

Table of Content:

1. Writing Efficient & Clean Code
2. Object Oriented Programming
3. Developing Packages
4. Unit Testing
5. Bash Scripting
6. Command Line Automation
7. Version Control with Git
8. Cloud Engineering
9. DevOps
10. Databases & Big Data

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1. Writing Efficient & Clean Code

The first software engineering skill that is essential to learn is how to write efficient and clean code. As a data scientist, you should spend the majority of your time gaining actionable insights from the data not waiting for the code to finish running.

Clean code / Photo by AltumCode on Unsplash

Being able to write efficient code will help you reduce runtime and save as many computational resources as possible. In addition to that you should also be able to find the code bottlenecks and eliminate these bottlenecks and bad design patterns.

You will also work on teams and your code will be used by others that’s why you need to have a clean code that others can easily understand and follow along with it. Also in most of your projects, you will have to go back to your old code to reuse it, so having it written in a clean way will make it easy to understand and reuse it.

Learning Resoruces:

• Clean Code: A Handbook of Agile Software Craftsmanship
• Writing Efficient Python Code
• Writing Efficient Code with pandas

2. Object Oriented Programming

OOP is a programming paradigm that reduces development times and makes it easier to read, reuse, and maintain code. OOP is a widely used programming paradigm that shifts the focus away from thinking about code as a sequence of actions to looking at your program as a collection of objects that interact with each other.

A data scientist working on real-world software projects must understand OOP terms (such as objects, attributes, methods, and inheritance) to be able to work on them effectively.

Photo by Mohammad Rahmani on Unsplash

Object-oriented programming (OOP) can be useful in data science for organizing and structuring code. Some of the ways OOP can be used in data science include:

• Creating custom data structures: classes can be used to create custom data structures that are tailored to the specific needs of a data science project.
• Encapsulation: classes can be used to encapsulate data and methods, making it easier to maintain and update the code.
• Reusability: classes and objects can be reused across different parts of a data science project, reducing the amount of redundant code.
• Organization: classes can be used to organize related data and methods into a single, cohesive unit, making the code more readable and easier to understand.
• Abstraction: classes can be used to abstract away the details of how a certain process works, making the code more modular and easier to understand.
• Inheritance: classes can be organized in a hierarchy, where a subclass inherits the properties and methods of its parent class, making it easy to extend the functionality of existing classes.

Learning Resoruces:

• Object-Oriented Programming in Python

3. Developing Packages

Is it difficult for you to reuse and share your wonderful snippets because you have to copy and paste the same code between files? If so, you might benefit from package creation!

Photo by cottonbro studio

As a data scientist, you should know about package structure and the extra files required to turn free code into simple packages in the Python programming language, especially as you are almost always working with it. You should also know how to boost your package creation by generating templates and using cookiecutter to develop package skeletons.

Additionally, you should know how to use setup tools and twine to publish your packages to PyPI, the world’s largest Python package repository.

Learning Resoruces:

• Developing Python Packages

4. Unit Testing

An efficient unit testing process cuts down on development and maintenance time, improves documentation, increases user trust, and saves time on productive systems. Because unit testing is a common practice in data science, it has become an important skill. Since almost every data science project that will go to market will need to be tested. Companies almost universally use it. As a result, unit testing is a must-have skill in the industry and is used by almost every company.

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Python’s most popular testing framework, pytest, can be used for unit testing as you will mostly use python in your projects. As a data scientist, you should be able to create unit tests for data preprocessing, models, and visualizations, interpret test results and fix buggy code. You should also be aware of advanced concepts such as TDD, test organization, fixtures, and mocking, so you can properly test your data science projects.

Learning Resoruces:

• Unit Testing for Data Science in Python

5. Bash Scripting

Using the command line during your job will be crucial since you will utilize scripting in a production setting. You will be able to create routines that automate common tasks, as well as handle a lot of operating systems more quickly if you use command-line scripting.

Photo by Photo by Negative Space

Bash is a data and file manipulation language that is both fast, concise, and robust. It can be used to construct analytics pipelines in the cloud, particularly by Linux users who use data across multiple files. You must have hands-on expertise with Bash scripting in terms of input arguments and outputting results. It’s also critical to understand data structures such as variables and arrays, as well as control statements, such as loops and conditionals. Finally, you should be able to write Bash functions and have them run automatically with cron.

Learning Resoruces:

• Introduction to Shell
• Introduction to Bash Scripting

6. Command Line Automation

The ability to run commands and scripts in the background can save time and automate tedious tasks. There are several different tools and techniques that can be used to accomplish this, including programmatic automation, scripting, and command-line automation.

Photo by Andrea De Santis on Unsplash

There are a number of advantages to using command-line automation. First, it is possible to automate complex tasks that may otherwise require a lot of scripting or programming. Second, it is possible to automate tasks without having to install software or create custom scripts or templates. Finally, it is possible to do so without having to explicitly document the commands that need to be run.
One drawback of using command-line automation is that there needs to be visual feedback. This means that it is not possible to see what commands are being executed or how long they take to complete. It also means that errors cannot be easily detected.
Command line automation can be helpful for data scientists who have automated tasks such as data cleaning, data loading, and data integration and who want more control over the environment in which their code run

Learning Resources:

• Command Line Automation in Python

7. Version Control with Git

Working in production demands a data scientist to know version control. Since you will be working in cooperation with other data scientists, data engineers, and software engineers. Therefore it is important to be able to share your code and update it and also to follow up on their updates in a professional way. Also, if you are working on personal projects, using version control will allow you to have better versioning of your codes. Also since in the field of data science, you can have many attempts to find the right solution for your problem, it will be better to be able to version all the attempts and their results.

Photo by Roman Synkevych 🇺🇦 on Unsplash

You should be able to create a new Git repo, commit changes, and review the commit history of an existing repo. You should be able to keep your commits organized using tags and branches and you’ll master the art of merging changes by crushing those pesky merge conflicts and also how to edit commits, revert changes, or even delete commits.

Learning Resources:

• Introduction to Version Control with Git
• DVC and Git For Data Science

8. Cloud Engineering

Companies can access a range of computing services from the internet. Nowadays the greatest data centers in the world such as Amazon Web services are accessible at a minimal cost allowing firms to operate their applications. Because small businesses or countries with emerging economies can utilize this technology for ambitious and complex endeavors that might otherwise be quite costly if they had to host them on their own servers, cloud computing is an important component in data science applications.

Data analytics, data management, and model deployment have all been simplified thanks to cloud computing. Because servers cost a lot to produce, particularly for smaller organizations as they’d require sizeable space to house them, constant maintenance and backups in case anything goes wrong, and extensive planning and research, firms may obtain more or fewer servers than they need according to their data needs.

Photo by engin akyurt on Unsplash

Here are the most famous cloud computing platforms used by data scientists:

1. Amazon Web Services

Amazon’s cloud computing business was created in 2006 and is currently one of the most popular cloud computing platforms for data science. Amazon Web Services provides data analytics products including Amazon QuickSight (business analytics service), Amazon RedShift (data warehousing), AWS Data Pipeline, AWS Data Exchange, Amazon Kinesis (real-time data analysis), Amazon EMR (big data processing), and Amazon SageMaker (model deployment). Amazon Web Services provides database solutions including Amazon Aurora (relational database) and Amazon DynamoDB (non-relational database). AWS is utilized by organizations such as Netflix, NASA, and more.

2. Google Cloud

Google Cloud Platform is a Google-run data warehouse platform. It’s the same infrastructure that Google uses to power its internal services like Google Search, YouTube, Gmail, and so forth. Google Cloud offers a variety of data analytics services such as BigQuery (data warehouse), Dataflow (real-time analytics), Dataproc (running Apache Hadoop and Apache Spark clusters), Looker (business intelligence analytics), Google Data Studio (visualization dashboards and data reporting), Dataprep (data preparation), etc.

3. Microsoft Azure

Microsoft Azure was initially released in 2010 as a cloud computing platform. Microsoft Azure is popular data science and data analytics cloud computing platform. Azure Synapse Analytics (Data Analytics), Azure Stream Analytics (Streaming analytics), Azure Databricks (Apache Spark analytics), Azure Data Lake Storage (Data Lake), Data Factory (hybrid data integration), and other Azure products are available for data analytics. Azure Cosmos DB (NoSQL database), Azure SQL Database (SQL database), and other databases are also supported.

Learning Resources:

• Understanding Cloud Computing
• AWS Cloud Concepts
• Data Science on AWS

9. MLOps

MLOps, or Machine Learning Operations, is the practice of applying DevOps principles and techniques to machine learning projects. This includes automating the building, testing, and deployment of machine learning models, as well as monitoring and maintaining them in production. The goal of MLOps is to improve the collaboration and communication between data scientists, engineers, and other stakeholders, and to increase the speed and reliability of machine learning deployments.

Photo by vectorjuice / Freepik

MLOps for data science involves applying DevOps principles and practices to the process of building, testing, deploying, and maintaining machine learning models. This includes automating the entire machine learning workflow, from data preparation to model training to deployment, as well as monitoring and maintaining models in production. By doing so, data scientists can improve collaboration and communication with other stakeholders, such as engineers and business leaders, and increase the speed and reliability of machine learning deployments.

Some examples of MLOps practices that are commonly used in data science include:

• Version control for machine learning models and data sets
• Continuous integration and continuous deployment (CI/CD) for machine learning models
• Automated testing of machine learning models
• Monitoring and alerting for machine learning models in production
• A/B testing and canary releases for machine learning models.

Learning Resources:

• Ultimate MLOps Learning Roadmap with Free Learning Resources In 2023

10. Databases & Big Data

As a data scientist, you should be able to query and work with both structured and unstructured database technologies. These databases might include SQL databases like PostgreSQL or NoSQL databases like MongoDB. Since this is a standard component of any software engineering undertaking, you should be able to handle it, particularly in the data-gathering phase.

Photo by Joshua Sortino on Unsplash

Big data refers to extremely large and complex data sets that are too large or too complex to be handled by traditional data processing tools. In data science, big data can refer to data sets that are too large to fit into the memory of a single computer, or data sets that are too complex to be analyzed using simple SQL queries or spreadsheet programs. Handling big data requires specialized tools and techniques, such as distributed computing, parallel processing, and cloud computing.

Some popular big data tools and technologies used in data science include:

• Hadoop: An open-source, distributed computing system that can store and process large data sets across clusters of commodity hardware.
• Apache Spark: An open-source, distributed computing system that can handle both batch and real-time data processing.
• Apache Storm: A distributed real-time computation system that can process streaming data.
• Apache Kafka: A distributed streaming platform that can handle real-time data streams.
• NoSQL databases: These databases are designed to handle large amounts of unstructured or semi-structured data, and they include MongoDB, Cassandra, and HBase.

Big data can bring many benefits to data science by providing more accurate and precise results. It allows to uncover new insights and pattern from large data sets, and can improve predictions and decision-making. However, working with big data also poses some challenges such as data privacy, security, and data quality.

Learning Resources:

• Big Data with PySpark
• The Big Data Developer Course
• Introduction to Relational Databases in SQL
• SQL Fundamentals
• NoSQL Concepts

This is a roadmap to develop your software engineering skills as a data scientist which will make you a better data scientist that can handle big and real-world projects.

This roadmap is not for absolute beginners but it is more for aspiring data scientists who would like to improve their skills and stand out.