How can you easily switch from software testing to data science and analytics?

ART-0022 AU IOD Switching from software testing to data science and analytics

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If you’re looking to switch careers from software testing or testing engineering into data science, the times are ripe with opportunities. You only have to scan job websites to see that data scientists are currently a hot commodity. 

There is no sign that demand is abating. The US Bureau of Labor Statistics projected that data science careers will experience high growth until at least 2030. Similar growth is expected across the globe, as is a pay increase. According to Teleport, whose website focuses on quality of life, data scientists’ salaries rank highly among professionals. As of 2022, in Sydney, data scientists rank 7th; 3rd in Singapore; 4th in New York; and 3rd in Auckland. You can expect good pay in any country deploying systematic data analysis.

Given the skyrocketing career opportunities, it is clear why those from a software testing background frequently decide to upskill to data science and analytics. 

For anyone curious about making such a change, here are the answers to questions you might have.

1. I am working in software testing. What are the benefits of upskilling to data science and analytics?

  • Career opportunities

After a few years of working in software testing or as a testing engineer, your work may become mundane. Upskilling to data science and analytics may be just what your career needs. 

Making the switch to data science opens the door to a multi-faceted career with boundless opportunities. Given its rapid evolution, new specialisations continue to arise. Data warehouse architect, data analyst, machine learning engineer, enterprise architect and business intelligence developer are just a few of the positions available.

  • Job security 

Demand for data scientists with the right qualifications is growing exponentially, while the supply for skilled applicants is not, widening the supply and demand gap. The good news for you is that, if you choose to upskill and become a part of the valuable data science industry, companies will be keen to acquire your services. 

  • Increased salary 

Jobs in high demand offer great monetary rewards, and data science is no exception. 

According to PayScale, the entry-level salaries of a data scientist in 2022 in Australia is around A$76k and a software tester, A$52k. Average salaries for senior positions are A$84k for software testers and A$120k for data scientists. 

2. Will my skills in software testing be transferrable to a career in data science and analytics?

Most definitely. Software testers have many overlapping skills that will make the career transition smooth, such as:

Questioning – This is an essential skill in both data science and software testing. What do we need to predict or estimate? How are we going to acquire the data? What data is relevant? Are there any patterns in the data? Can we tell a story from the data? 

Programming methods – These questions are answered by applying tools. As with software testing, data scientists adopt programming languages such as R, Python, JavaScript and methods such as data mining and web scraping. 

3. What data science skills will I need to pick up on making the switch from software testing?

After switching to data science and analytics, you may need to fill some skill or knowledge gaps. Here’s where the two differ:

Data science Software testing/engineering
Approach Process-orientated – algorithms, finding patterns, data visualisation and analysis Methodology orientated – Software Development Life Cycle model (SDLC) – Waterfall, Spiral, Agile
Platforms/Environments Hadoop, Spark, Storm, Ceph User-interface development: automation tools such as Selenium, TestingWhiz, KatalonStudio
Roles and responsibilities Data scientist, data analyst, machine learning engineer, statistician, data architect, big data specialist Usability test engineer, manual testing engineer, automated test engineer, network test engineer, test library and configuration specialist
Data sources Business apps, machine log data, social media data, sensor data Import from a production environment, duplication from prior customer systems














4. After upskilling, what type of jobs can I apply for?

The career prospects for data scientists, particularly in Australia, are promising. Major organisations in the APAC region are hiring data science professionals with a software testing/engineer background. Here’s a snapshot of just some of the positions you can apply for and their roles in the data science industry: 

Data analyst 

  • Most data analyst positions are junior or entry-level.
  • The role is concerned with mining insights and performing statistical analysis on data to help answer business questions. Once patterns are identified, the results are communicated to decision-makers within the company. 

Data scientist 

  • Generally, the next step up from a data analyst; similar tasks are completed.
  • Builds machine learning models to make predictions on future events based on past data. 

Data engineer 

  • Requires more software development and programming skills as the role manages the company’s data infrastructure. 
  • Responsible for building data pipelines. 
  • Many software testing skills are transferrable to the role of data engineer.

Machine learning engineer 

  • Uses the analysis of a data scientist or data analyst and turns it into functional software.
  • The role is often advertised as a data scientist with a specialisation in machine learning. 

Data warehouse architect 

  • The role is to manage the company’s data storage systems.
  • If you have experience or a strong interest in data engineering, this role is perfect for you.

5. How do I upskill from software testing to data science?

The Institute of Data’s full-time and part-time programs will help you achieve your career goals and connect you with thousands of industry partners seeking professionals with skills in data science. Talk to a career consultant now and take the first step.

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