Honest Data Analytics with GenAI Course Experience (2026)

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At Coding Ninjas, every learner comes with a different starting point and a different reason for learning. For this learner, the goal was to build a stronger foundation in data analytics and work towards a Data Analyst role while continuing with their existing work.

“I wanted to understand what I should learn, how to practise it properly and how to prepare for interviews. I did not want to depend entirely on self-learning, so having structured learning and guidance was important to me.”

The learner chose the Coding Ninjas Data Analytics with GenAI Program, which covers core skills such as Python, SQL and Power BI, along with Generative AI tools and practical projects. The program also includes mentorship, interview preparation and placement assistance.

This is their experience of the journey from deciding to learn data analytics and getting started with the course to working through projects, using the available support and preparing for the next step in their career.

How It Started For Ravi

Before joining the program, I had 4 years of experience and I wanted to move towards Data Analytics. I had an interest in the field but I did not have a clear learning path.

I had access to plenty of online resources. The problem was deciding what to learn first and how much of what I was learning would actually be useful for a Data Analyst role.

I wanted a more structured approach where I could learn the fundamentals, practise them and gradually start working on projects.

How I Came Across Coding Ninjas

While looking at different Data Analytics courses, I came across Coding Ninjas. I also went through Coding Ninjas data analytics reviews to understand what other learners had to say about the program.

Before making a decision, I wanted to understand things like the curriculum, learning process, projects, fees and career support. I spoke with a counsellor to get a better understanding of the program and what the overall learning experience would involve. The counsellor helped me with each question about the program and explained how the curriculum and support structure work. The onboarding experience was really smooth as well.

What mattered to me was having a clear path instead of continuing to jump between different tutorials and resources.

Why I Decided to Join

After looking at the program structure, I felt that a guided learning path would work better for me than continuing with completely unstructured self-learning.

The combination of technical topics, practical projects, mentorship and career support was one of the main reasons I decided to join.

I also liked the fact that the program included GenAI as part of the learning experience. Since AI tools are becoming more common in data-related work, I wanted to understand how they could be used alongside the core skills I was learning.

Getting Started With the Course

The initial part of the course was about building the basics and getting comfortable with the core concepts.

I worked through topics such as Excel, SQL, Python and Power BI. Some concepts were easier to pick up than others and I had to spend more time practising areas where I was not confident.

One thing that helped was having a structured sequence to follow. Instead of constantly wondering what I should learn next, I had a curriculum to work through and projects that gave me a reason to apply what I was learning. Along the way, there were also 10X sessions hosted by various top figures in the industry. The most inspiring of these was led by Suyash Aditya (Head of Data Systems at BlinkIt).

Learning Python, SQL and Power BI

SQL was one of the areas where regular practice made a noticeable difference. Understanding individual commands was one thing but putting them together to solve a problem was different.

Whenever I got stuck, I could use the available support, including Ninja AI, to understand where I was going wrong and continue with the problem.

Python and Power BI were also important parts of the learning process. As I worked through exercises and projects, I became more comfortable connecting the concepts instead of treating each topic as something separate.

Getting Comfortable With GenAI

The GenAI part of the course was something I was particularly interested in.

I got to explore tools such as ChatGPT and OpenAI APIs and understand how they could be used while working with data. Instead of looking at GenAI only as a chatbot, the projects helped me think about where these tools could actually be useful in a data workflow. We were taught how to maximize the efficiency and output of an answer through a well defined prompt.

For me, the important part was learning how to use AI as a support tool while still understanding the underlying concepts myself.

The Projects and Case Studies That Made the Learning Practical

What was more amazing is the way the projects and case studies were designed. Many of them were based on real-world situations and working through them made me feel like I was actually doing the kind of work a Data Analyst would do in a company.

It was detailed enough to make me think about the data, understand what it was telling me and figure out how to present the insights. At times, it even felt like I was working on problems that could come up while working in a large organisation. That made the learning much more interesting because I could connect the concepts I was learning with practical situations.

While working on the E-commerce Dashboard, I also used Ninja AI and reached out to the Teaching Assistant whenever I had doubts. Having someone to help me understand where I was going wrong made it easier to keep moving forward instead of getting stuck on one part of the project.

Overall, the projects and case studies were one of the parts of the course that made the learning feel more relevant to the kind of work I wanted to do as a Data Analyst.

The Support I Used Along the Way

One of the useful parts of the program was having different types of support available at different stages of learning.

Ninja AI was useful when I needed help with a quick technical doubt or wanted to understand why something was not working. Mentorship and learning support were more useful when I needed guidance beyond a simple technical question.

I also found the interview preparation useful because knowing a concept is not always enough. You need to be able to explain your approach clearly when someone asks you about it.

Preparing for Interviews

Interview preparation was one of the areas where I had to work on myself the most.

I was not always comfortable explaining my answers, even when I knew the concept. Mock interviews gave me an opportunity to practise answering technical and SQL questions and understand where I needed to improve.

With practice, I became more comfortable with the interview format and better at explaining how I arrived at an answer.

That preparation was important because it helped connect the learning I had done during the course with the kind of conversations I would have during an actual interview.

My Job Search and Placement Experience

Once I was ready to start looking for opportunities, the placement support became another part of the journey.

The support helped me identify relevant opportunities and prepare for the application and interview process. I still had to apply, prepare and perform in the interviews but having career support made the process easier to navigate. The Placements team helped me refine my resume, scheduled mock interviews, helped me shortlist and appear in the interviews.

Eventually, I was able to make the transition from my previous basic role into a Data Analyst position at a top company.

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