Software Tester
An organization always aims to maintain a product’s 100% quality. It takes time for engineers to make it well-tuned, but who will validate it before it is released to the public? Can a developer do that? Considering the bandwidth, he either can or cannot. Nonetheless, a dedicated tester who can test, be clear, and report is always helpful (if there are any issues). Testing is QA. Thus the quality of the product depends on the tester.
Data Scientist
Data scientist is someone who is responsible for Data collection, data framing, data modeling, and data reporting in order to improve business decision making. In other words, he is the one who works with a lot of data. If you are a tester, and want to transition to data scientist, you can take up the IBM-accredited Data science course in Delhi, and learn the top-notch tools.
Testing and Data Science
Modern software testing entails using software testing tools and data to determine whether or not software applications contain bugs.
On the other hand, the demand for data scientists has been increasing exponentially, which means that more jobs are available every day.
As a software tester with data manipulation experience, you cannot ignore data science’s role today. Several factors contribute to this, including:
If you notice, data scientists are also interested in quality. They work tirelessly to develop and test the code. In other words, we don’t necessarily require a distinct testing phase in the data life cycle.
We need testers who can test the results, analysts who can work on the reporting component with the help of tools like Tableau, business analysts, database administrators, and data analysts to guarantee that the data is simplified—Excel, Power Bi, etc.
Data modelers who work to frame the data in the proper shape; business analysts who are able to discern what the client requires and what needs to be provided.
To begin your career as a data scientist, follow these steps:
Step 1 – Gain a thorough understanding of the business model (which QAs frequently do), which should not be difficult if you are familiar with the client’s environment.
Step 2 – Get rid of SQL because — report generation is common for everyone, and to analyze and audit whether the data is correct or not — knowing SQL is more important than seeking help from others.
Step 3 – Automation testing requires a lot of programming, and data modelers in the data science field have a lot of programming to do. When you are proficient in any programming skill, it should not be a nightmare; you only need a clear understanding of what needs to be done.
Testing is always needed, and selenium will assist you in automating web browsers, which is another aspect of the data science journey.
But there are certain other things you must know:
1. Statistics, which are required in data science (eg, probability, linear algebra, mean and variance etc.,)
2. Types of machine learning
3. Different algorithms, including the most popular ones, and their effects
These three are required for anyone interested in working in data science to
study and master. Your work may not involve all of your previous learnings, but a 360-degree understanding is required. If you lack platform understanding — in data science, cloud computing, cyber security, or even testing — you have a title but not a subject.
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