A productive day as a data scientist | day in the life of a data scientist vlog #2

TL;DR

A productive day as a data scientist combines time-blocking, focused analysis, strategic breaks, and help from colleagues when needed. The creator starts work after an 8:30 a.m. morning routine, builds custom datasets, codes with SQL and Python, walks after lunch, completes company training, and begins a self-evaluation. Read on for a grounded look at the habits, tools, and challenges shaping the workday.

Transcript

hi friends as the title says this is a rather productive day in my life as you will later see it is primarily because it's the holidays and most people are not working which means i don't have to sit through a bunch of meetings that really messes up my flow we'll get to all the details later but now it is time to wake up i start the day by laying t... Read More

Key Insights

• The absence of meetings during the holidays allows for increased productivity and better focus on tasks.
• Starting the day with a calm morning routine, including reading, can set a positive tone for the day.
• Time-blocking helps prioritize important tasks, though flexibility is needed for adjustments throughout the day.
• Challenges in data analysis often require creating custom datasets and seeking assistance from senior colleagues.
• Using SQL and Python is essential in the data scientist's toolkit for efficient coding and analysis.
• Taking breaks, such as walks, can help maintain energy levels and improve productivity after meals.
• Fidgeting with toys can aid concentration during company trainings and prevent drowsiness.
• Self-evaluations are difficult but necessary, highlighting the importance of keeping detailed notes on projects.

Questions & Answers

Q: What makes this day in the life of a data scientist productive?

The holidays mean fewer meetings, allowing the data scientist to maintain focus and complete a difficult analysis. Time-blocking, asking a senior data scientist for help, taking refreshing walks, and recording tasks for the next day also support the productive workday.

Q: How does the data scientist start the day?

The day begins with enjoying the calm early morning and reading The 80/20 Principle by Richard Koch on a Kindle. After getting up around 8:30 a.m., the data scientist does a few things around the house and takes a long bike ride, which provides time for thinking.

Q: How does the data scientist organize daily work?

The data scientist time-blocks the day before starting work so the most important task gets done. The schedule frequently changes, so tasks are rearranged as new priorities emerge.

Q: What challenges arise during the data analysis?

The analysis cannot use the established tools, and finding the right datasets is its most difficult part. The data scientist creates several datasets from scratch and, after trying for about an hour to obtain the final data, asks a senior data scientist for help.

Q: Which programming languages does the data scientist use at work?

The finished analysis combines SQL and Python. These are the two programming languages the data scientist uses most at the job.

Q: How does the data scientist avoid feeling sluggish after lunch?

After eating lunch around 2 p.m., the data scientist goes for a walk. This adds exercise and provides a refresh after the sluggishness that follows eating.

Q: How does the data scientist stay focused during company training?

The data scientist continually handles a fidget toy to stay stimulated and avoid falling asleep. Although the training is well done, fidgeting helps with personal difficulty concentrating.

Q: Why are self-evaluations difficult for the data scientist?

Although the data scientist feels busy all the time, recalling and explaining exactly what the work involved is difficult. The experience leads to a plan to keep better project notes and make the unfinished self-evaluation the next morning's most important task.

Summary & Key Takeaways

• The absence of meetings during holidays allows the data scientist to focus on important tasks, enhancing productivity. The day begins with a calm routine, including reading, and involves a mix of work and leisure activities.

• Time management is crucial, with time-blocking used to prioritize tasks. The day includes analysis work, which requires creating custom datasets and collaborating with senior colleagues for assistance.

• Breaks, such as walks after meals, help maintain energy levels. Fidget toys aid concentration during trainings. Self-evaluations are challenging, highlighting the need for better project documentation in the future.