How Raghav Started His Data Science Journey and Got Placed

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From Enrollment to Placement: Raghav's Data Science Course Journey

Starting a career in data science can be challenging when you are not sure what skills to learn, which tools to focus on or how to prepare for interviews. For Raghav, the challenge was not just learning data science. He wanted to understand how the skills he was developing could translate into an actual career opportunity.

After exploring different learning options, he decided to join the Data Science with GenAI Job Bootcamp by Coding Ninjas. What started with a conversation with a counsellor eventually became a structured learning and career preparation journey.

From the initial counselling session and enrollment to learning, project building, interview preparation and placement support, every stage played a role in helping Raghav become more confident about applying for data science opportunities.

This is Raghav's journey.

Data Science with GenAI Program Overview

Feature	Details
Best For	Working professionals, final-year students and fresh graduates looking to build careers in Data Science and related AI/ML roles
Duration	9 Months Intensive Job Bootcamp
Skills Covered	Python, SQL, Data Cleaning, EDA, Statistics, Machine Learning, Deep Learning, Generative AI and related Data Science technologies
Projects	Hands-on projects and real-world case studies
AI Tools & Technologies	ChatGPT, Replit, Notion AI and other AI tools and workflows
Learning Support	Live classes, Teaching Assistants, Ninja AI, mentors and industry experts
Career Support	Resume, LinkedIn and GitHub profile building, mock interviews, project reviews and interview preparation
Placement Support	Dedicated placement team, job opportunities, placement coaching and career guidance

The current Coding Ninjas program describes the Data Science with GenAI bootcamp as a 9-month learning program with placement support after the learning phase. It includes live classes, 1:1 doubt support, a relationship manager, industry expert sessions, hands-on projects and personalised placement assistance.

Before and After Enrolling into Coding Ninjas

Before	After
Unsure about which Data Science skills to learn	A structured learning roadmap
Limited exposure to Machine Learning and AI	Hands-on experience with Data Science and AI concepts
Learning from different online resources	One structured curriculum
Unsure how to build a Data Science portfolio	Projects and case studies to demonstrate practical skills
Limited interview preparation	Resume reviews, mock interviews and expert guidance
Unclear about the job search process	Dedicated placement and career support

Why I Decided to Learn Data Science

Before enrolling, I knew that Data Science was becoming an important field but I was not completely sure how to build a career in it. I had explored different online resources but the experience was fragmented. I could find tutorials for Python, Machine Learning or statistics but I wasn't sure how these skills connected or what I should learn first.

Another challenge was practical experience.

I didn't want to simply complete courses and collect certificates. I wanted to work on projects, understand how Data Science is used to solve real problems and eventually become confident enough to discuss my work during interviews.

That's when I started looking for a more structured learning path.

My Experience With the Coding Ninjas Counsellor

My first interaction with Coding Ninjas was through the counselling process. Instead of immediately making the conversation about enrolling, the counsellor helped me understand what the program covered, how the learning journey was structured and what kind of support would be available throughout the program.

We discussed my existing background, my career goals and what I expected from a Data Science program.

One thing that helped me was understanding that learning Data Science would require consistent effort. The program could provide the roadmap, mentors and support but I would still have to practise regularly and work on the projects myself. The counselling conversation helped me understand what I was signing up for before making the decision to enroll.

Why I Chose Coding Ninjas

I was looking for more than a collection of recorded courses.

A few things stood out to me:

• A structured curriculum covering Data Science fundamentals and advanced concepts
• Live learning and access to mentors
• 1:1 doubt support
• Practical projects and case studies
• Exposure to Generative AI and modern AI workflows
• Resume and profile-building support
• Mock interviews and career preparation
• Dedicated placement assistance

The combination of technical learning and career support was important to me because my goal was not simply to learn Data Science. I wanted to eventually use those skills to transition into a relevant job role.

Starting My Coding Ninjas Journey

After enrolling, the journey became more structured. The initial phase focused on building the fundamentals required for Data Science. Instead of jumping directly into complex Machine Learning concepts, I had to understand the basics first.

The curriculum covered areas such as Python, data cleaning and preprocessing, exploratory data analysis, statistics and Machine Learning.

As the learning progressed, the concepts became more practical. I started understanding how different skills fit together rather than treating Python, statistics, SQL and Machine Learning as separate subjects.

Getting Help Whenever I Got Stuck

Data Science has a steep learning curve and there were times when I got stuck while working through concepts or projects. This is where the support system became important.

Instead of spending hours trying to solve every problem independently, I could reach out to Teaching Assistants for help. Ninja AI also became another resource for resolving doubts and getting help while learning.

Having multiple ways to get support made the learning process less frustrating. When I couldn't understand something, I could ask questions, discuss the problem and continue working instead of remaining stuck for too long.

Learning Through Projects and Real-World Problems

One of the most useful parts of the journey was applying concepts through projects. Data Science becomes much easier to understand when you actually work with data instead of only studying definitions.

The program includes practical projects based on different business and analytical scenarios.

For example, projects can involve areas such as:

• Credit risk analysis
• Healthcare data analysis
• Retail analytics
• Meal plan analysis
• Sports analytics
• Predictive modelling

These projects required me to work with data, analyse patterns and think about how the results could be used to solve a practical problem. Working on these projects also changed the way I looked at interviews. Instead of saying that I had learned Python or Machine Learning, I could explain where I had actually used those skills.

Understanding Generative AI in Data Science

Another part of the program that stood out was the focus on Generative AI.

Data Science is changing as AI tools become part of everyday workflows. I wanted to understand not only traditional Data Science techniques but also how AI could be used to improve productivity. The program introduces AI tools and workflows alongside Data Science concepts.

This helped me explore how AI can assist with activities such as data preparation, analysis, documentation, model development and other parts of the Data Science workflow. For me, this was useful because I could see how traditional Data Science knowledge and newer AI capabilities could work together.

Preparing My Resume and Online Profiles

Once I started thinking seriously about job applications, I realised that technical skills alone were not enough. My resume needed to communicate my skills and projects clearly.

The career support team helped me work on my professional profile, including my resume, LinkedIn profile and GitHub presence. This was particularly useful because I initially focused heavily on learning and didn't pay enough attention to how I was presenting my work.

The profile-building process helped me organise my projects and experience in a way that was easier for recruiters and interviewers to understand.

Preparing for Data Science Interviews

The next stage was interview preparation. This was one of the areas where I felt my confidence improved significantly.

The placement preparation included mock interviews, project discussions and guidance from industry experts. Instead of only revising technical concepts, I started practising how to explain my projects and answer questions under interview conditions.

The mock interviews also helped identify gaps in my preparation. Sometimes I knew the answer but struggled to explain it clearly. Other times, I realised that I understood a concept only at a surface level. Practising these situations before actual interviews helped me become more comfortable with the process.

Placement Team Became Part of My Journey

After completing the required learning and meeting the relevant placement eligibility criteria, the placement team became an important part of my job search. The team helped me understand relevant opportunities and guided me through the application process.

The biggest difference for me was that I wasn't trying to figure out the entire job-search process alone. There was someone I could approach when I had questions about my profile, interviews or applications.

The Interview That Changed Everything

Eventually, I received an interview opportunity through the career support process. By this point, the preparation felt very different from when I had started the program.

I had projects that I could discuss, a more structured resume and experience with mock interviews. During the interview process, I was asked about my technical understanding as well as the projects I had worked on.

The project discussions were particularly important because I could explain the reasoning behind my approach rather than simply listing technologies on my resume. After completing the interview rounds, I received the news I had been working towards.

I had secured a job offer from a top company.

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