Insights on dealing with statistics, interacting with people, and maximizing productivity at the workplace
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In August last year, I joined Google as a Data Analytics Apprentice. It was the start of my working career. Crossing the year mark made me ruminate about what I had learned in different dimensions of my job and work-life during this time. I don’t think there’s ever been a period where I have undergone a more rapid metamorphosis. It’s been a challenge but a fun one!
I have divided my learning into three categories: data science, productivity, and people.
- In real-world data science problems, high accuracy will be obtained simply because the dataset is extremely skewed and not because the algorithm performs well. You can have a dataset with a negative-to-positive class ratio of 1000:1 (like for spam classification), and this imbalance will lead to a high accuracy greater than 99% if we classify all points as negative. Hence, it matters to choose the right metric for evaluation, which is recall in this case. A high recall score would indicate that the positive classes are being…