Data Science is a popular field of the 21st century. Everyone from data analysts to Ph.D. students wants to work in this field. If you are a software engineer; you must have felt the same inkling of exploring data science and what the hype is all about. However, what experts have seen is that as we move to the end stages of the hype cycle; engineering and data science are asymptotically moving closer. The skills needed by data scientists are less statistics-based and visualization and more in line with computer science. Concepts like continuous integration and testing have found their way in everyday jargon.
But what most software engineer experience is a lack of knowledge on leveraging your experience. If you are one of these, you might have some questions like:
Will my current skills carry over to the data science field?
Are the best tools and practices different for data scientists?
What should I learn first?
In this article, we will be providing you details on the journey path from a software engineer to a data scientist.
Let's start by discussing the difference between these two roles. While both of them are responsible for handling machine learning models; their nature of work and the interaction with the models vary widely.
As a data scientist, you will be involved in the machine learning workflow and perform statistical analysis for determining what machine learning approach should be used. After this, you can start prototyping and developing these models. Data engineers work with the data scientists before and after the modeling process. They build data pipelines for feeding data into the models and creating an engineering system that can serve the models and ensure continuous health.
In most of the data science and machine learning courses, you won't learn about the best practices and concepts from software engineering such as unit testing, CI/CD, version control, and writing modular reusable code. Even most advanced machine learning teams don't use these practices to code their machine learning systems that lead to a disturbing trend known as 'The Machine Learning Reproducibility Crisis'. As per this crisis, the system of rebuilding models from scratch and tracking changes is so bad that it feels like we are stepping back in time when we coded without source control.
Even though these software engineering skills are not explicitly stated in the job description for a data science role, having a good understanding of these skills during your role as a developer will ease your job. Plus, you will be able to answer all the programming questions asked in your data science interview.
Even if you have a strong foundation in computer science with your background in software engineering, you will have to work hard to become a data scientist. If you are interested in making a career in the field of data science, there are four aspects you have to work on:
Building Data Science and Machine Learning specific language
You should start by building a combination of applied skills in training models on GPUs/distributed compute or data wrangling and theory-based knowledge of statistics and probability. The best way to get started on this is through a certification program; that will help you get acquainted with all the basic concepts of data science. There are also several resources available online that you can use.
Getting industry-specific language
If you want to work in a specific industry like financial services, retail, healthcare, consumer goods, etc., it is important to catch up on the developments and pain points of the industry. You will find the application of this knowledge as it relates to machine learning and data. You can try scanning the websites of AI startups with specific verticals; and see how they position their value proposition and use machine learning. Here are some steps to help you approach next:
It is not recommended that you apply to an organization you find through while searching, but see what are their value propositions, customer's pain points, and the skills that they listed in their job descriptions.
Learning about ML modern tools might seem daunting at first as the space is constantly evolving. You can start by breaking the learning process into several small and manageable pieces. Tactically, the most common programming languages used by data scientists are Python and R. You must also learn about the add-on packages that have been created for data science applications like matplotlib, SciPy, and NumPy. These languages are not compiled but interpreted so that the data scientists don't have to worry about the nuances of the language and can focus on the problem. In order to understand how to implement data structures as classes, you have to learn object-oriented programming.
While you are learning about the ML frameworks such as PyTorch, Keras, and Tensorflow, read their documentation and implement their tutorials. At the end of the day, it is more important to implement what you have learned in a project that involves data collection, wrangling, modeling, and machine learning experiment management.
This aspect is geared towards preparing you for the data science interview. Here are a few topics that you need to focus on:
If you enroll yourself in a data science course in Pune, you will be able to get a headstart in your data science career.
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