ARCHIVE: A week of Data Science and Deep Work

Posted on September 18, 2017

Opportunity Knocks:

Though I had planned a vacation for this week to visit spots old and new in Barcelona, life has taken a turn for the better that I’m eagerly embracing. Instead of Spain, I find myself in Sacramento helping someone I care deeply about to take some important first steps in life. Rather than strolling and sightseeing, I’m diving deep into Data Analysis, Statistics, and SQL in ways that I’ve wanted to for over a year now. My weekly goals are below and I’m treating this as my own “MIT challenge” for the next 7 days, ending with a Data Analyst interview at my company the day afterwards.

Weekly Goals:

  1. Solve 20 Data Science and analysis problems provided by friends, colleagues, and an industry-leading guide. Deep work and true learning requires the practice of solving real-world problems, not merely doing toy problems from textbooks, and certainly not listening to lectures.
  2. Bridge specific gaps to solving these real-world problems by covering the relevant sections of Introduction to Statistical Learning in R and R for Data Science. I’ll start with exercises, then concepts and examples, and cover full chapters as needed. This will be more effective use of time and more targeted learning than simply following a set syllabus, reading, and then leaving the real work of exercises (or solving new problems!) for the end.
  3. Master SQL and Periscope, by doing all training cases in the Mode Analytics tutorial and publishing 3 new dashboards and 1 view in Periscope. I start each day working on SQL exercises, problems, or real-world requests.


In support of these weekly goals, I’m setting daily goals the night before each day. I’ll need to solve 3 problems most days, complete 3 sections of about equal length in the Mode SQL Tutorial, and still have time for diving into exercises, examples, and concepts in  Today’s goals, for Sunday 9/17/2017, are to benchmark my speed of work and inform goals for the week.

Daily Goals for today:

  1. Redo take home problem #1 (Conversion), applying best practices from prior work I did in grad school on this Driven Data challenge.
  2. All exercises for Chapters 1 and 2 of ISLR. Start them for Chapter 4 as well if needed. Coverage (reading) is only to bridge gaps encountered in practice (exercises and examples), and always comes after.
  3. Mode SQL Basics 100% done, and Intermediate exercises begun. I use SQL daily, do complex queries weekly, and must become even better.

Do what it takes.


January 2018 Update: Not much to add here, except that I’m glad I took this time to refresh my statistical thinking, R, SQL, and problem solving skills. It helped tremendously for getting the Data Analyst role at Premise and has been a foundation for me to continue learning ever since.