Why I Chose Gyansetu for Data Analytics and AI: My Experience as a Business Intelligence Analyst
By Sangeeta Tanwar | Business Intelligence Analyst: I was working with PNB Bank when I made up my mind to improve my knowledge about Data Analytics & Artificial Intelligence.
I wasn’t looking for a certificate just for the sake of having one.
I was searching for something which would be more useful for me.
I needed to get stronger. I needed to become more confident with data and learn how new AI skills could be used with the tasks that I already performed.
It is what pushed me to explore various educational options.
Ultimately, I enrolled myself in Gyansetu.
Now, after having gone through the whole process, I believe that what I valued the most was not just the courses themselves, but the learning atmosphere and lifetime access to all the course materials, as well as a pragmatic approach to education along with support from the training and placement team.
This is what my experience gave me.
Why I decided to upgrade my Data Analytics and AI skills
Professional experience shapes how you perceive data.
Not only do you perceive figures.
You begin to see behind those figures – reports, structures, processes, and decisions.
This realization gave me an understanding that I needed to build on my technical knowledge base.
I wanted to get more comfortable with analytics.
I wanted to learn more about artificial intelligence.
And I wanted to acquire skills that I could use rather than just another certification on my resume.
This was where I started.
What I was looking for in a training program
There were various things that I was looking for prior to deciding on the institute.
First, there should be a learning framework.
Second, there should be hands-on experience.
Third, I would like access to all learning material outside the class.
Finally, there should be help beyond just training since learning a technical skill and job applications are very different things.
This is one of the reasons why the Gyansetu model worked for me.
The latest Gyansetu data courses include topics such as Python, SQL, statistics, visualization, Power BI, machine learning, Agentic AI, Generative AI and AI skills, along with hands-on projects.
All these topics were interlinked in my opinion.
I learned that Data Analytics is about solving problems, not just learning tools
This was definitely one of the biggest shifts in my mindset.
Until you have a better understanding of analytics, it’s easy to perceive the discipline as nothing more than a list of software programs: Generative AI, Advanced Excel, Power BI, Python, SQL.
However, this is missing the bigger picture.
The technology itself is only one component of the process.
The question is:
What problem am I trying to solve with the data?
A typical workflow can look like:
Data → Cleaning → Analysis → Visualization → Insight → Decision
However, the ultimate aim of all this is to understand what the data is telling you.
This line of thought helped me develop my fundamentals.
Why practical learning mattered to me
There is one thing I think everyone learning analytics or data science needs to know.
You can watch countless videos.
You can mug up definitions.
You can do quizzes.
But when you are asked to analyze a real-world dataset, you struggle.
Why?
Because real-world problems don’t have an “Apply” button saying:
“Use this method.”
It is necessary for you to determine what you will do.
This is why practical learning became significant to me.
When you work on projects, you get an opportunity to link up different skills rather than treat them as independent subjects.
Practical learning projects and cases are included in the published data programs by Gyansetu.
This practical learning became meaningful to me.
The connection between Data Analytics and AI became clearer
Initially, when I began to explore the topic of artificial intelligence, I viewed it as distinct from everything else.
But now there is a lot more overlap.
Information is at the core.
Analytics makes sense out of the data.
AI can be used for processes like automation and prediction, which revolve around the data.
The point is:
AI doesn’t remove the need to understand data.
You also need to check if the data is trustworthy.
You also need to know about the problem.
You also need to challenge the output.
And you also need to find out if the answer makes sense.
That is why I liked learning AI along with the basics rather than taking AI as a substitute for the basics.
Gyansetu currently offers AI along with other learning topics like data analytics, programming, statistics and machine learning.
Lifetime course access was one of the things I valued most
This seems like a small thing.
For me, it was not a small thing.
Learning through technology may not occur in one session.
Today, you have learned something.
You practice what you have learned.
Then, you move on.
But three months down the line, you have to apply the same idea.
You find yourself struggling to remember how you did it.
With lifetime access, I would go back to what I had learned when I felt that I needed to revise it.
This was an important thing, because I was studying while working in my profession.
It did not mean that I had to remember everything immediately.
What about placement support?
It was a significant aspect of my complete experience.
Because ultimately every student ends up asking the following question:
“What will happen once I finish the course?”
Course completion is one landmark.
Being prepared to enter the job market is another.
And receiving suitable interview invitations is yet another.
Gyansetu at present provides 100% placement assistance as it covers a variety of aspects like resume and profile building, mock interviews, career workshops, job applications, and more. The Gurgaon Data Science page mentions guaranteed interview invitations upon completion of the training as well.
For me personally, knowing that there is a separate team which takes care of the career aspect of the journey meant extra peace of mind.
The placement process matters because the job search is different from learning
Consider this.
In training, you are supposed to learn.
In placement, you are supposed to show what you have learned.
That is why you require:
A professional resume
A strong job profile
Projects you can explain
Interview preparation
Technical confidence
Access to relevant opportunities
The selection process at Gyansetu revolves around these topics. The company’s information highlights profile building, mock interviews, interview preparation, job applications, and networking.
This is vital as the end of your course should not represent the end of your learning experience.
Instead, it should mark the beginning of your efforts to apply what you have learned.
How the placement-pool approach can help a learner
A structured placement process can be easier to understand when you look at the journey as a sequence:
Learn
↓
Build practical skills
↓
Complete projects
↓
Prepare your resume
↓
Build your professional profile
↓
Enter the placement process
↓
Access relevant interview opportunities
↓
Prepare for interviews
↓
Speak with employers
The advantage of being systematic is that the learner does not have to work out every step of the process by himself/herself.
However, there is one very important reality about this situation.
Placement assistance does not mean the learner can skip the interview process.
Gyansetu makes this difference explicit in their published information by stating that interviews and career guidance are offered, but final selection is based on the candidate’s knowledge and performance.
This is how I would prefer to define placement help.
The institute creates the path for the student.
The student must traverse it.
Why interview preparation made a difference
Another thing that I learned is that knowing and explaining are not the same thing.
Picture yourself being interviewed and asked:
“Why did you use this method?”
“Why did you go for this particular visualization?”
“How did you clean your data?”
“What would you have changed in your analysis?”
In all cases, you will need to justify and explain your choices.
That’s why interview practice is useful.
Gyansetu’s placement support service offers mock interviews and feedback.
The goal isn’t simply to memorise answers.
It’s to become comfortable explaining what you actually know.
Why I would recommend Gyansetu to another working professional
The case of my situation was different from that of a person who had not been to work before.
I was already employed in PNB Bank.
Thus, there was a need for learning that would be able to fit into my schedule as an employee.
There was a need for me to enhance my skills without having the feeling of learning coming to an end with classroom learning.
This was possible through the use of both training and lifetime course accessibility.
There was also a need for a training and placement team in the next phase.
What I would look for if I were choosing a Data Analytics course today
If someone asked me to evaluate a data analyst course in Gurgaon, I wouldn’t start by asking:
“Which institute claims to be the best?”
I would rather ask some other questions.
What all subjects does the syllabus cover?
Will I do some practical assignments?
Will I learn software like AI, Advanced Excel, SQL, Python, and Power BI?
Am I allowed to revise the course material?
Who will teach me?
What will be the procedure for placements?
How will my CV be prepared?”
What happens after the course?
Those questions tell you much more.
And if I wanted to learn Data Science?
I would use the same approach when comparing a data science course in Gurgaon.
Data Science is not just Python.
It is not just Machine Learning.
And it is definitely not just AI.
You need the foundations.
Python
SQL
Statistics
Data Analysis
Data Visualization
Machine Learning
Predictive Analytics
AI
And you need to understand how they connect.
Gyansetu’s current Data Science curriculum includes these areas alongside practical projects and career support.
What made Gyansetu valuable to me?
When I look back at my experience, I wouldn’t reduce it to one feature.
It was the combination.
- Learning environment: It helped me build stronger fundamentals.
- Practical learning: It helped me connect concepts with applications.
- Lifetime access: It allowed me to revisit the material whenever I needed it.
- Data Analytics and AI exposure: It helped me broaden my understanding of modern data workflows.
- Training support: It gave me a learning environment where I could continue developing my skills.
- Placement support: It gave me a structured career-support process rather than leaving the job search entirely to me.
That’s what I value.
My biggest takeaway
If you are currently employed and considering learning Data Analytics, Data Science or AI, do not enroll yourself in a course just because the institute’s website claims to be offering a long list of technologies.
Consider the whole process.
- What would you learn?
- What would you practice?
- What would you build?
- Would you be able to revise your learnings?
- How would you get ready for interviews?
- What happens after completion of the course?
These questions matter a lot.
For me, Gyansetu offered me an environment where I could improve my knowledge on Data Analytics and AI while continuing my career path.
The lifetime access enabled me to keep learning.
The practical nature of the training enabled me to improve my fundamentals.
While the training and placement assistance gave me the extra confidence to take the leap from learning to professional opportunities.
This is why I have referred Gyansetu to my colleagues.
This is why I am grateful to the Gyansetu training and placement team for their help during my learning process.
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