Day 102 of Job Hunting
Job Applications
My day kicked off with the familiar buzz of my alarm at 7:30 AM. In the quiet of the morning, I found comfort in the ritual of making breakfast - a traditional delight of warm tandoori naan, spread with rich, creamy malai and a dollop of jam. It’s these simple pleasures that set the tone for my day.
With a satisfied palate, I dove straight into the task at hand - job applications. The process can be daunting, but today, I was on a roll. By the time noon arrived, I had already sent off 30 applications, a personal record that brought a sense of accomplishment. In between, I squeezed in a heartwarming call to my family back home, their voices a reminder of the support and love that fuels my ambitions.
Taking a much-needed break, I reflected on the morning’s progress. It’s these small victories and moments of connection that keep me grounded and motivated in the midst of life’s relentless pace.
Interview Preparation
At 3 PM, I resumed my day with a focused mindset, turning my attention to interview preparation. In my search for effective resources, I discovered an intriguing book, ‘Ace the Data Science Interview’ by Nick Singh. The book, along with its accompanying course, immediately caught my attention due to Singh’s impressive background at tech giants like Facebook, Google, and Microsoft.
His insights on resume building were particularly enlightening – emphasizing the power of concise bullet points, the strategic use of numbers (even hypothetical but contextually relevant ones), crafting attention-grabbing cold emails, and leveraging projects to build a compelling portfolio. Inspired by his approach, I promptly ordered the book to delve deeper into his methodologies.
Furthering my day’s educational journey, I turned to Chapter 3 of ‘Hands-On Machine Learning with Scikit-Learn’. Despite covering familiar ground, the chapter served as a valuable refresher on several key concepts:
- The significance of precision/recall curves in skewed datasets and understanding their trade-offs.
- Interpreting ROC curves and their associated Area Under the Curve (AUC) for model evaluation.
- The utility of Confusion Matrices and Error Analysis in improving model accuracy.
- Approaches to tackle Multi-Class, Multi-Label, and Multi-Output problems using both binary and non-binary models.
While much of this material revisited my previous work, it reinforced my understanding and offered fresh perspectives on these fundamental data science concepts.
Update: Spent too much time fixing the recent post feature on front page.