Learnings from My First Year of Being a Data Analyst

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!

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I have divided my learning into three categories: data science, productivity, and people.

Data Science

• 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 “recollected” correctly.
• While using a statistical test, one must ensure that the data follows the assumptions made for the test. I remember using the Chi-Square test for a particular project, and my manager questioned me about it. I told him all the mathematics I knew about Chi-Square, but it was only later that I understood he was trying to make me assess whether the test’s assumptions were met, so that using the Chi-Square test actually made sense!
• There could be multiple ways to calculate the same metric. For example, the skewness of a distribution can be calculated using the classical formula. However, there are other ways to calculate it, such as Bowley’s coefficient or Pearson’s coefficient. Choosing the right metrics and selecting the right approach to calculate them for a particular problem is a skill you learn with experience.