Information Technology or IT has been a major part of our lives.
The Digital Revolution enables everyone to understand and access technology and virtual connectivity. This has led to the development of new software and technologies that even allows us to experience scenarios via VR (virtual reality) augmentation and even be part of the metaverse.
The broad definition of Information Technology includes all the aspects of technology, from developing software to deploying and maintaining the software.
This field also includes working on developing the correct hardware for each software. It also expands to providing security for various cyber and data domains.
There are various career options in the IT field. You can choose to be a developer or a software tester. You can also be a cyber security analyst, a data scientist, or a machine learning expert.
The Information Technology field has similarities in terms of the subjects taught for each domain, but switching fields requires knowledge, dedication, and up-gradation of skills.
One of the most lucrative and in-demand career choices in data science. It is a broad domain and allows the learner to enter any data-related field. But, how easy or difficult will it be for a software tester to become a data scientist?
What is Software Testing
Software testing is part of software development. The main job of a software tester is to check if the software is working as it is supposed to.
While software testing is also part of the skill requirement for becoming a software developer, specializing in just software testing is also available. The first instance of software testing was debugging the program, which was predominantly the only software testing required during the 1950s through to the late 1970s.
Today, software testing is a continuous process that enables further software development. Software testing is also considered a separate role, and many developers and programmers opt just to launch and maintain the software testing instances.
There is also a process known as continuous testing that is managed by the DevOps team. This is a collaboration between the development and the operation teams that maintain the entire life cycle of the software/product.
Coming back to software testing, this aspect of software development is perhaps the most critical step as it helps the software to have a longer life and offers users a glitch-free use experience. A software tester is responsible for detecting and providing feedback on the following factors:
- Security issues
- Scalability limitations
- Design flaws
- Architectural flaws
- Functionality deficiencies or issues
What is Data Science
Data science is an interdisciplinary field that combines algorithms and scientific processes to make sense of the data that is collected. Data is a vital part of many industries.
Be it customer data or a company’s business and daily activity data, all these data are collected and stored for further analysis, and reports are generated based on this information.
But, if the data does not make sense, the reports generated and the analysis of the same will not be possible. To resolve this issue, data science is brought into the equation. A data scientist is primarily tasked with creating programs that will help segregate the data to retain the essential ones and then streamline the program to generate actionable reports.
Data science helps develop predictive models based on machine learning techniques and algorithms. There are various aspects of data science, and each aspect is usually handled by an expert in a particular field.
For example, a data mining expert focuses solely on the processing of data and finding relevant data for developing the program to do the same without manual overrides. The daily activities of a data scientist entail:
- Formulating questions that tackle the issues with the data collection process.
- Determining the variables that will correctly address the issues.
- Gathering all the data—structured and unstructured—from all available sources.
- Processing the raw data so that it can be converted into readable sets.
- Feeding the processed data to the system (machine learning algorithm or statistical model) for analyzing and identifying patterns.
- Rendering data to highlight trends for growth.
- Preparing the reports on these findings and forwarding them to the correct departments.
Here is a table to compare both the job role based on multiple aspects:
| Aspect | Software Tester | Data Scientist |
|---|---|---|
| Role in Business | Ensures software quality by testing functionality | Helps organizations make data-driven decisions |
| Required Education | Typically requires a bachelor’s degree | Usually requires at least a master’s degree in a related field |
| Technical Skills | Proficient in programming languages, testing tools | Proficient in programming languages, statistical analysis tools |
| Data Skills | Basic understanding of data | Strong understanding of data |
| Analytical Thinking | Follows test cases and reports issues | Analyzes complex data sets and draws insights |
| Communication Skills | Communicates test results to the team | Presents data findings to stakeholders in a clear way |
| Business Acumen | Little exposure to business strategy | Works closely with stakeholders to understand business needs |
| Career Advancement | May move into software development or management | May advance to management or specialized data science roles |
| Job Demand | Moderate demand for testers in some industries | High demand for data scientists in a wide range of industries |
| Average Salary Range | $50,000 – $100,000 per year | $90,000 – $140,000 per year (can be higher in some industries) |
| Tools | Testing frameworks like Selenium, JUnit, and TestNG | Data visualization tools like Tableau, Power BI, and D3.js |
| General Skills | Attention to detail, critical thinking, problem-solving | Creative thinking, problem solving, adaptability |
| Time to Learn | Several months to learn testing frameworks and tools | At least a year to learn statistical analysis and machine learning |
| Scope | Tests software in various environments and conditions | Analyzes and interprets data from various sources and contexts |
What Would You Need to Learn to Get Into Data Science
Software testing requires knowledge of coding and software development. Software engineers and developers can opt to focus solely on the testing part of the product life cycle.
But the knowledge about coding and program development does help a software tester to become a data scientist with little upskilling. These skills are primarily technical, such as:
Programming
The very first thing that you need to understand is that data science heavily relies on programming. You need to know various coding and programming languages to develop algorithms that will aid with creating data sets based on the variables you feed the system.
Some of the commonly used languages for coding are Python, R, SQL, MATLAB, Java, and Scala. Data science is primarily coding, and you can only create a smart algorithm when you code it to meet the exact needs.
Programming also includes knowledge about the various tools and libraries that can help create codes and reuse them for other projects.
Statistics and Probability
Data science is a discipline that uses mathematics along with algorithms. Since data scientists are often predicting trends and development opportunities, they need to be well-versed with statistics and probability.
This field will help data scientists to understand better the big data collected and find the relationships between variables. This understanding will further help identify the patterns and similarities.
This understanding is the main factor that will help develop the correct code for the program to predict future outcomes and profitable trends.
Data Wrangling
To extract actionable data, the collected raw data needs to be structured. This is done via data wrangling. This process involves analyzing the raw data to determine the underlying importance of the collected data.
Once the usable data is highlighted, it is extracted, cleaned, and rendered into a format that can be further analyzed by business analysts.
Data Visualization
Once the data is collected, cleaned, and sorted, it should be plotted in a manner that will be understood by all the departments concerned with the data and its reports. Data visualization aids with the generation of graphical representations of the data for easy readability.
This conversion of the data into actionable stories is not just limited to graphs. It also includes maps and images that will highlight the trends captured by the data scientist after reading the data.
Machine Learning
Along with data science, it is beneficial to know machine learning, especially if you are aiming to land a job at companies that deal with Big Data.
Knowledge of machine learning along with data science enables you to code programs capable of making decisions and feed it to a machine that will improve its thinking capabilities based on the data fed to it.
Being a subset of data science, machine learning will also help in creating better models and data interpretations based on very specific algorithms.
Database Management
Apart from developing programs that will structure data and generate reports that can be analyzed further, data scientists are also required to perform database management.
This is a crucial skill as without proper segregation and management of the various databases; it would be impossible to pinpoint the sets that would be essential for a particular task.
Database management requires defining all the collectorate data so they are sorted into sets and can easily be accessed when required. It also requires manipulating the data format to suit the needs of the business.
Tips to Switch From Software Testing to Data Science
While knowing about the theories and work done by data scientists is essential, having some practical knowledge will also help strengthen the knowledge gathered through courses and other learning materials. In order to make the switch from software testing to data science seamless, you can:
Keep Practicing
A data scientist has to be well-versed in programming languages and mathematics. Practicing both when you have some time to kill will help you train your brain to look at problems from different angles and look for objectives that can cater to the current business requirement.
Take Up Test Projects
Projects that are available via the free courses are also a great way to stay updated on the skills that a data scientist should possess. These projects will also help you analyze your own methodologies and rectify any shortcomings. Some of the free sites that offer projects are Data.gov, Kaggle, and Google Cloud Public Datasets.
Look Through Various Videos
Nothing can help you learn better than looking at various videos found on YouTube. There are dedicated channels to help learners not only understand the core concepts of data science but also build projects and models from scratch. These tutorials will help you gain the confidence to apply for jobs and utilize your skills.
Opt For Internships
The best way to make the transition will be to get on-field experience. This is provided by internships. Through these internships, you can understand and work on real-life use cases to further understand the scope of data science.
Final Thoughts
Data science is an interesting and challenging field. The subjects, though vast, are primarily skill-based and practical solutions that help with understanding the theories related to the subject.
Switching to data science is profitable as there are many career opportunities that pay well and provide a proper challenge to think out of the box. It is also one of the best ways to upskill from a software tester and design programs that will shape the future.
Switching will require dedication and proper certifications that can be attained online via various free and paid courses.
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