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i was head of events in a student activity called aces and it was a magnificent experience as i gathered a team wich i chose by myself and my partner as they all were hardworkers and creative thinkers of course we had some trouble with them as some of them were being lazy about doing there tasks or procranstinating but we were able to motivate them each time so they would give their best

Thank you for the question. I have been working as a data engineer for a combined total of three years, working closely with business leaders and engineers at different enterprises. where my main focus and speciality was in building reliable data pipelines that supports business decision making. I also have experience running a solo consultancy firm, where I have had the opportunity to consult and build end to end data products for businesses, which has allowed to be explore and hone my leadership, and communication skills on top of my technical expertise. Above all. in addition to the skills  I have learnt over the last three years, my experience in academia, especially as a consequence of my PhD research, I have a strong preference and a proven track record  in delivering complex analytical tasks.  All of the above essentially package me up as  an ideal candidate for this role.

This is a product which we release recently it would check the recent breaches that happened exploiting certain vulnarability in the recent past and also check the vulnarabilties in the infrastructure and then provide a score based on severity. the higher the severity the higher the chance of getting breached. This was a great hit but two of our customers came back with a specialised request, the wanted to quantify the severity in terms of revenue loss so it would help them prioratize fixes on the infrastructure. This not only required heavy research but also defining the scope as well that where it fits in. The reason it was important to think about this feature because it was a big contract and our company wanted close it before the end of Q2. I had a planning sessions with the stake holders and product team, we defined the scope of the feature and then communicated with the customer and got all the buy ins, there had to be some repriorization required to imediatle start working on the project, I had a chat with my team and transperantly explained why this was such a high priority for the company and how it aligns with the company goals. This gave boosted their morale and also they saw the advantages of adding this feature and the moral was high.

Bootstrapping is a statistical resampling method used to estimate the distribution of a sample statistic (like the mean, median, variance) by repeatedly sampling from the original dataset with replacement. The primary idea is to generate "new" samples (called bootstrap samples) by randomly selecting data points from the original sample, allowing some points to be selected multiple times while others may not be selected at all.

Steps of Bootstrapping:

1. Original sample: Start with a dataset of size n.

2. Resampling: Generate multiple new datasets (bootstrap samples) of the same size n by sampling with replacement from the original dataset.

3. Statistic calculation: For each bootstrap sample, calculate the statistic of interest (e.g., the mean).

4. Aggregation: After many resamplings (typically thousands), aggregate these statistics to estimate properties like confidence intervals, standard errors, or the distribution of the statistic.

Efficacy in Augmenting Sample Size:

While bootstrapping doesn’t actually create new, independent data, it is effective at enhancing statistical insights from small samples by simulating variability and giving a better approximation of the underlying population’s distribution. Its efficacy is most pronounced when:

• Small samples: Bootstrapping is especially useful for datasets where traditional parametric methods may not be applicable due to the small sample size or assumptions (like normality).

• Non-parametric nature: It does not require assumptions about the distribution of the data, making it versatile.

• Uncertainty Estimation: It helps estimate confidence intervals, standard errors, and biases for small samples when direct analytical solutions are difficult.

However, since bootstrapping is based on the assumption that the original sample is representative of the population, its effectiveness can be limited when the original sample is biased or unrepresentative. It’s not a substitute for truly increasing the sample size but is a powerful technique for making the most of available data.