The Wagon Bootcamp: Diving into Data Science with Hansraj Ramjattan

Hansraj Ramjattan, a Data Engineer and New Technology Advisor at Alteo Agri, recently took on a new challenge by participating in The Wagon’s Data Science & AI Bootcamp. For several months, he balanced his job and this intensive online training, designed to quickly equip participants with essential skills in areas like machine learning and predictive model deployment. Driven by a hands-on approach based on real-world cases, the program allowed him to enhance his technical skills and enrich his daily practice. In this interview, Hansraj reflects on his experience, motivations, and how this skill enhancement benefits his work and Alteo.

1. What were the main topics covered in The Wagon’s Data Science & AI Bootcamp?

The program progressively covers all major topics related to data science and artificial intelligence, with direct applications in a professional context. From day one, participants strengthen their command of Python, the reference language in data science, integrating APIs and manipulating databases through SQL queries. The fundamentals of statistics and probability are reinforced to better interpret data and support decision-making.

Training continues with machine learning, which allows for data modeling, automation of strategic analyses, behavior prediction, or anomaly detection. The curriculum also delves into deep learning, an advanced branch of machine learning used for processing complex data such as images, text, or time series. Finally, the operational aspect is addressed through the deployment of models using professional tools like MLflow, Docker, or Google Cloud Platform.

Each module is based on concrete cases from the professional world, making the learning practical, grounded in reality, and immediately applicable. The goal is clear: to be operational by the end of each session.

2. How was the course delivered?

The Data Science & AI Bootcamp at The Wagon is a very intensive program: two months for the full-time format and seven months for part-time. The training was primarily conducted online, with sessions on Tuesday and Thursday evenings from 6:30 PM to 9:30 PM, and all day Saturday from 9 AM to 6 PM. Before these sessions, participants also dedicated time to reading resources recommended by The Wagon, watching video tutorials, and completing preparatory exercises.

I personally chose the part-time format to balance training and work. Saturday mornings were dedicated to theoretical sessions led by data science experts based in Geneva, Berlin, or Brazil, to deepen understanding of the more complex concepts.

In case of difficulties, a tutor was always available to provide guidance, but the approach remained focused on autonomy. The pace was consistently intense and deliberately demanding, designed to promote rapid progress and push us out of our comfort zones.

To give an idea of the commitment required, we started with seven participants, and two dropped out along the way.

3. Why did you choose to take this training?

Excel remains the daily tool for many, including myself. However, the volume of data I handle daily is increasing exponentially each year. Files are becoming larger, calculations slower, and the time taken for repetitive tasks was impacting my efficiency.

I needed to automate certain recurring analyses, reduce processing times, create more flexible and personalized solutions, and, above all, no longer be limited by the standard functionalities available in Excel or Power BI — especially since add-ins can be expensive.

I had also developed several models and was looking for ways to refine their accuracy and reliability. I needed a versioning and modification tracking system for the models I designed — a challenge now resolved with GitHub, which we learned to use during the Bootcamp.

Additionally, I wanted to better understand the architecture and functioning of the models integrated into CanePro, to identify concrete improvements, contribute to their evolution, and ultimately design new models better suited to our needs.

These goals led me to The Wagon Bootcamp, recognized for its practice-oriented pedagogy based on real cases, clear structure, and a program designed for rapid acquisition of key skills.

The part-time format allowed me to attend the training while fulfilling my professional responsibilities — an essential balance in my context.

4. What technical skills did you develop during the Bootcamp?

The Bootcamp enhanced my technical skills around data, from analysis to modeling, and deployment. I solidified my command of Python for manipulating, analyzing, and visualizing data, while integrating SQL for effective database interaction. I also deepened my understanding of applied statistics, which is crucial for rigorously interpreting data.

In terms of modeling, I learned to design, train, and evaluate supervised and unsupervised machine learning models, and then integrate them into robust pipelines. The Bootcamp also allowed me to explore deep learning, from simple neural networks (ANN) to more complex architectures (RNN, LSTM), as well as model deployment using MLflow, Docker, and Google Cloud Platform. I also became familiar with professional tools like GitHub and Jupyter, essential for collaborative work and project traceability.

Each module included practical exercises on real datasets, often derived from business problems, which allowed me to confront field constraints early on.

The fast-paced environment and teamwork also strengthened my ability to collaborate effectively, learn quickly, and adapt to demanding technical environments.

All these skills are directly applicable for automating analyses, designing predictive tools, and supporting data-driven decision-making.

5. How does this training help you in your current job?

This training has transformed my approach to data in my daily work. I now apply Python to automate processes that previously took me several hours in Excel. This not only saves time but also reduces the risk of errors and improves traceability. Thanks to SQL and best practices for structuring data, I can query complex databases more quickly and effectively. The skills acquired in machine learning also allow me to explore more advanced modeling avenues, particularly in optimizing yields or detecting anomalies.

Beyond the tools, the training has given me a clear methodology for approaching data issues: formulating a hypothesis, structuring the approach, testing, and validating with reliable data.

All of this makes me more autonomous, more rigorous, and especially more responsive to business demands — with more relevant analyses, more dynamic reports, and better-supported proposals.

6. What benefits do you see for the company from this skill enhancement?

This skill enhancement directly benefits the company at several levels. First, it allows for faster, more reliable, and reproducible data processing, which improves the quality of analyses and decision-making. Repetitive tasks can be automated, freeing up time for higher-value work.

Secondly, it promotes better communication with technical teams, particularly our colleagues in the IT department, by speaking a more common language and making clearer, better-structured requests. This smooths exchanges and accelerates project implementation.

Finally, this internal data competence reduces reliance on costly or rigid external solutions and allows for the development of tailored tools better suited to our field realities.