How can a tester become a data scientist | by Madhu Vadlamani | March 2022

How can a tester become a data scientist?
Is it a neat job?
What are the chances of a manual tester to master and what are the advantages – if you are an automation tester.

Hi everyone, this is Madhu Vadlamani and in this article I try to lay out a simple and clear roadmap of how a software tester can become.

Before we start, let’s understand 2 what the role of tester and data scientist is, but before we read anything — remember that IT is always a field of transformation. Today’s update is outdated tomorrow

Who is a software tester
Maintaining 100% quality in any product is always a goal for an organization. It takes time for developers to properly adjust it, but before it is made public, who will validate? Can a developer do this – Can/cannot consider bandwidth. But a dedicated tester is always an asset who can test – clear and report (if there are any issues). A tester is a QA, which means the QUALITY rests on him.

In fact, we do a lot of tests in our daily life, for example: if there are unexpected guests visiting the house and you should prepare food that is good for everyone, including you.

What is the role of a data scientist
Collect the data — Frame the data — Model it — Report it. In simple terms, it is he who processes tons of data”

Here are some common skills in my opinion that are common to both tester and data scientist

If you observe – Data scientists are also concerned about quality. They develop the code and test it in an unstoppable process. In other words, we don’t need to separately test a separate segment in the data lifecycle.

Here are some tips for a tester to be a good data scientist
Data science is not a one-man job and the data scientist is never an individual contributor role.

To ensure data rationalization, we need:
→ Data Analyst, Data modeler, Business analyst, Database administrator, to be more precise —
we need testers able to test the results, analysts able to work on the reporting part using tools like Tableau. power bi, ms excel….

→ Data modelers who work to frame the data in the right shape, business analysts who can understand the customer’s needs and what needs to be delivered.

→ In fact, there are 36+ roles in a data science journey

1. Get a full understanding of the business model (which QA often does) which shouldn’t be hard to ask when you know the customer’s environment

2. Get rid of SQL because – report generation is common for everyone and to analyze and check if the data is correct or not – it is more important for you to know SQL before asking others for help

3. Automation testing involves a lot of programming, and data modelers in the field of data science have a lot of programming to do. It shouldn’t be a nightmare when you master any programming skill – all you need is a clear understanding of what to do.

4. Testing is always necessary and selenium will help you automate web browsers, which is again part of the data science journey.

1. The statistics needed for data science
2. Different types of learning
3. Different and most popular algorithms and their impact

These 3 are mandatory to learn and master for anyone who wants to enter the field of data science. Now, your job may not involve all the learning you have done, but a 360 degree level of understanding is mandatory. If you are not good at understanding platform – in data science/cloud/cybersecurity actually even in the tests, which means you have a title but not a subject

What are the best sources to start with?
This is always a difficult question where the answers can be skewed, but to start with the basics of statistics, for different reasons I like the book “A cartoon guide to statistics” and other than that – it’s all your choice and i have done my best in terms of writing the most complicated topic simple and here are some of my articles

1. What is Data Science vs Artificial Intelligence vs Machine Learning
2. What is analysis and what is analysis
3. +++ other articles also in http://madhuvad.medium.com/

Ffinally — Data science is not rocket science. It can be transformed by a lot of thought/understanding and practice. Also, this article will never be fulfilled unless I thank my fellow testers and friends Mr. Siva Kumar Vuritla and Mr. Chandraa Mamiduri

Good luck and good learning
Yours

Madhu
Vadlamani

Sean N. Ayres