Disclaimer: This post may contain affiliate links, meaning we get a small commission if you make a purchase through our links, at no cost to you. For more information, please visit our Disclaimer Page.
As a data analyst, you may find that you want to transition into a software engineering role. Although it sounds like both professions are similar, a data analyst and a software engineer are two very different creatures. A data analyst uses data to solve problems, while a software engineer uses coding and other computer skills to solve problems. Both have different job responsibilities, but the question is, can a data analyst transition to become a software engineer?
A data analyst can become a software engineer, as skill sets tend to intertwine. Even though software engineering requires deep coding abilities, a data analyst is familiar with machine learning, multiple programming languages, and database languages that form a basis for software engineering.
Table of Contents
There are more differences between these two fields than there are similarities. Starting from their methodologies in achieving their work goals, approaches to projects, skills required, and the tools they use to meet their goals.
While the roles of a data analyst and those of a software engineer differ, there are general similarities, you can expect in the two careers. Some of these similarities are:
1. They use programming languages, mainly Python, for coding – Software engineers code all the time to create software applications and programs for solving problems or meeting the needs.
Data analysts use these programming languages to design algorithms and predictive models useful in aggregating complex data.
2. The end goal for both is to solve problems in business models – A software engineer will use his coding skills to create programs that fix a problem, and a data analyst uses his statistical skills to provide data-based insights to improve the business’s financial health.
3. They both work with data – Data is a product for software engineers as they use it to plan, build, test, and maintain their software systems.
For data analysts, data forms the basis of their work as they use it to extract valuable information for the business.
4. Both fields use AI features and automation – As technology advances, both software engineers and data analysts embrace AI and use it to automate some of the roles in their work.
For example, a software engineer will use AI for recognition in the applications they build, and a data analyst may use AI to automate and run algorithms that manipulate data.
5. They both require similar technological skills – Both data analysts and software engineers should have strong technical skills in machine learning software like Jenkins, database languages like SQL, and programming software tools like R and Python.
For a data analyst mainly working with coding and less with statistics, a day in the office will make them feel like a software engineer.
As more software-related solutions are getting incorporated into data analysis, more data scientists are inclining to software engineering to find better solutions for their data quests.
The transition from a data analyst to a software engineer is not common because most of the skills that make a good data analyst play a minor role in making a good software engineer.
For example, a data analyst is equipped with excellent statistical skills, algorithms, and data structuring, but these skills are not relevant to a software engineer.
On the flip side, a software engineer must be proficient in writing clean code, testing, and building software applications and programs.
A data analyst may only be good with basic coding and machine learning only helpful in generating algorithms for data analysis.
It’s always a good idea to start with what you already know before advancing to new knowledge, especially when learning programming languages.
The switch may not be a quick, easy one as it will involve a lot of learning, especially in coding using different programming languages. Here’s a step-by-step guide you can follow to achieve success in your intended career switch.
Step 1 – Choose a backend web framework and learn it.
A backend (server-side) web framework like Django, Spring Boot, Rails, Express, etc., will form the core where you’ll develop and build applications.
For a start, choose one and go deep into it, preferably one that has been around for some time. This will help you familiarize yourself with what companies are already using.
Step 2 – Familiarize with basic syntax as you learn your framework’s programming language.
Every framework presents a different programming language. For example, Django uses Python, and Spring Boot uses Java.
Therefore, learn the language presented in your chosen framework and try to solve some algorithm questions with it.
You’ll master syntax with more practice, but in the beginning, learning basic syntax will do. You can always refer whenever you forget.
Step 3 – Learn React
React is a frontend (client-side) web framework which you can use to implement design patterns for website and application layouts.
It’s the most used framework across companies, so it’s safe to learn it first. You can learn from others in the future if you choose to.
In the beginning, you don’t have to master the front-end web framework. What’s important is the basics and the knowledge to integrate it with the backend framework.
Step 4 – Learn HTML and CSS for styling.
Your style will determine how your product sells. When starting, you can check what other developers are doing and draw inspiration from there. With time, you’ll know your style and make beautiful designs that resonate with it.
Most companies know what they are looking for, so they have their style preference already defined. Yours is to follow the style and create the design.
Step 5 – Learn a database language to use on the web apps.
As a data analyst, you’re probably well familiar with SQL. Different frameworks use predefined API instead of using SQL directly to interact with databases.
For example, if Python is your chosen programming language, you’ll use SQLAlchemy to execute database transactions.
As a beginner, a good choice would be PostgreSQL because of its suitability when a database is not specified. It also has many valuable features which prepare you to work with other related databases.
Step 6 – Learn infrastructure.
These include cloud computing services like AWS, where you can deploy the applications you build.
There’s no rush with this step, but you can learn the basics of how to use PaaS platforms to deploy your apps.
This stage is easier learned on the job, and the preferences vary with the company you’re working with.
Step 7 – Use the knowledge you have so far to build a portfolio.
Follow through the things you have learned so far and build some apps to showcase your capabilities. No one will care how well you can answer algorithm questions unless you have something tangible to show for it.
Since you’ll use these to show how good you are, it’s important to give them your best shot and make the apps’ layouts beautiful.
Data analysts are in demand across all industries, with the US Bureau of Labor Statistics predicting a 31% growth rate between 2019 and 2029.
Some sectors with the highest demand for data analysts in relation to the amount of data they handle are Finance, healthcare, insurance, and information technology.
Software engineers expect a growth of 22% between 2019-2029 as more industries require innovative software for their businesses. The increased concerns in cybersecurity are also contributing to the rising demand for software engineers.
A graduate data analyst receives a median pay of about $98,230 per year, with some companies paying slightly higher. A software engineer at the same level earns a median salary of about $110,140 per year.
Companies consider several factors, including skills and location determine the remuneration for their employees in different positions.
With the changing times in technology and the increased need for software solutions in data analysis, more data analysts choose the path of software engineering.
Data analysts aspiring to pursue software engineering must acquire skills in object-oriented programming, full-stack development for both frontend and backend, database languages, and cloud computing.