A Snapshot of My Recent Progress in Data Analytics

This article might be a little dry to read, but I keep it as a personal reference.

Yes, I am still fascinated by data analytics. Five weeks have passed since I last posted on this blog. This post is about steady accumulation rather than breakthroughs.

Below are some updates on my progress in my studies.

Recent Progress

  • 2 IBM Data Analyst Professional Certificate courses completed
    • Course 4: Python for Data Science, AI & Development
    • Course 5: Python Project for Data Science
  • 2 Google Advanced Data Analytics Professional Certificate courses completed
    • Course 3: Go Beyond the Numbers: Translate Data Insights
    • Course 4: The Power of Statistics
  • 70 SQL problems solved (mostly medium, solved independently)
  • 6 Python algorithm problems solved (for fun)
  • Learned basic Tableau skills and created 7 files on Tableau Public

Habits I’ve Managed to Keep Consistently

  • Focus on steady, daily progress rather than one-time intensity.
Some days are less productive, yet I’m learning to stay patient.
  • Take notes while taking courses.
I’ve written around 80 pages of Word notes over the five weeks.
  • Redo labs as practice to become more comfortable with coding.
  • Discuss questions with ChatGPT to dive deeper into the course material.
  • Review frequently.

These habits have mattered more than any single “productive” day.

A Small Experiment with Word Clouds

I tried to create a word cloud of all my class notes in Python. However, it returned mostly auxiliary words (such as column, data, print) instead of meaningful theme words (such as Binomial, Poisson, histogram).

This experiment reminded me that frequency does not equal importance. Just like in real data analysis, meaningful signals often require deliberate feature selection rather than raw counts.

Because of this, I decided to manually select the keywords and concepts as a summary of my recent studies. The keywords are summarized manually, and the word clouds are autogenerated. Most of them represent tools I can now actively use, not just terms I’ve seen once.

Key Concepts Learned

SQL

Since many SQL concepts involve long phrases, this section is shown as text rather than a word cloud.

  • coalesce
  • date_format
  • dense_rank
  • sum() over (order by …)
  • date_sub
  • sum(a = 'b')
  • avg(a = 'b')
  • trim
  • cast(… as signed)
  • range between 2 preceding and current row
  • recursion
  • least / greatest
  • gaps and islands problems
    • method 1: lag + case when + sum
    • method 2: id - row_number
  • lead

Python

Data Analysis

Looking Ahead

I am very much looking forward to Course 6, The Nuts and Bolts of Machine Learning, in the Google Advanced Data Analytics Professional Certificate. I’m only three learning modules away from starting the course, and I will keep going!

From Threads to Data: My Journey of Reinvention

It’s November 2025. I am on my journey to becoming a data explorer. It all started during a long vacation I took to recover from a health condition.

At first, I was just too weak mentally to pick up any new knowledges, so I turned to simple manual work. That’s when sewing quietly entered my life. I didn’t expect that my two paths would intertwine.

Stitch by Stitch

While recovering, I began watching videos on craftsmanship. One day, I came across a tutorial on making a fabric book cover. I followed along and created one of quiet warmth.

The fabric I picked was handwoven by my grandmother on an old-style loom. The cover didn’t look particularly striking at first, yet as I keep it, my love for it grew. Every stitch was made by hand, and the process, though slow, was deeply comforting.

“How fascinating fabric work is! I wish I had a sewing machine,” I thought.

A week later, I bought a very good one — my very first sewing machine. Having never used one before, I read the manual word by word, learning patiently how to thread, adjust tension, and start sewing. Then came an unstoppable flow of projects: tissue box covers, handbags, Roman curtains, scissor cases, tissue bags, coasters, pincushions, and even water bottle covers.

Sewing is like a charm, and I just could stop exploring its new possibilities. My hobby became irresistible addiction.

Becoming a Self-Taught Dressmaker

Eventually, I decided to take on a bigger challenge: making clothes.

It wasn’t easy at first. I watched many more videos. I bought patternmaking paper, beautiful fabrics from Liberty and Merchant & Mills, and tools like pins, rulers, water-erasable pens, a dress form, a thread color sample book, and matching threads, feeling fully equipped.

I was not yet able to design my own pattern, so I decided to copy existing garments. I studied every detail — how the stitches were done, and in what order. Sewing pattern could be drawn by putting the patternmaking paper above the shirt and tracing the shirt’s figure. My first clothes made was a colorful shirt for my mum, which looks not too bad! Along the way, I learned how to sew neck bindings, attach sleeves, make and install clasps, and finish hems.

Sometimes I was so intrigued by a topic and I searched and watched video one after another, couldn’t stop. Sometimes I was with the health condition, so I had to take some time off.

Gradually, I became a self-taught dressmaker – a hobbyist, not a professional of course. I made pajama sets, shirts with various patterns, a wrap skirt, shorts, pants, and a jinbei, a traditional Japanese loungewear set. Through these projects, I gained some basic ideas about sleeve shapes and how they fit with the clothes, and I became able to make some modifications myself. I learned how to add pajama piping and facing. I grasped and figured out how to make a button fly, which can be quite challenging. Not to mention many other skills. I also gained more confidence and happiness.

My sewing journey continues. Looking back, I realize how far I’ve come.

A year ago, I didn’t even know how to use a sewing machine, and now I’m making my own clothes!

All the process is driven by passion and achieved by accumulating skills little by little.

Turning Toward Data

As my health improved, I felt ready to focus again on something professional. I’ve always been drawn to data analysis and sometimes regret not pursuing a master’s degree in analytics. Northwestern has very prestigious program. I got a 3.94 GPA (even though my final GPA ended lower last quarter) at Northwestern the time in Senior year when I applied graduate school. So, I probably had a fair chance getting in.

Reflecting on my background, I realized I already had a strong foundation:

  • solid SQL knowledge
  • several statistics courses from college and grad school
  • backgrounds in math, economics, and engineering
  • Python experience
  • work in data services and risk management consulting
  • attention to detail
  • and, most importantly, a passion and an inquisitive mind

If driven by passion, I could become a tailor from zero to one, why can’t I learn data analytics on my own pace?

Building My New Toolkit

I researched and enrolled in two Coursera programs:

  • IBM Data Analyst Professional Certificate
  • Google Advanced Data Analytics Professional Certificate

Together they include 19 courses, both heavily focused on Python. The Google track also covers statistics and machine learning — topics I’m eager to master — while the IBM track includes web scraping and Tableau, both useful for data projects.

To strengthen my logic and SQL fluency, I started solving LeetCode database problems daily. After finishing these certificates, I would like to get to know more about A/B testing (I’ve already found two Udacity courses) and participate in Kaggle projects to apply my skills.

So far, six weeks have passed. I am doing okay:

  • ✅ 3 IBM courses completed
  • ✅ 2 Google courses completed
  • ✅ 90 SQL problems solved (easy + medium)
  • ✅ 15 algorithm problems solved (for fun!)

I review often using the Ebbinghaus memory curve, since my working memory tends to outperform my long-term memory. I hadn’t coded in Python or SQL for a while, but now my skills are warming up again — and I can almost feel neurons reconnecting in my brain. 😊

Sewing and Data — Patterns of the Same Thread

Sewing and data analysis might seem unrelated, yet they share the same spirit. Both require precision, creativity, patience, and curiosity. Whether I’m stitching a sleeve or writing a loop, I find joy in creating something meaningful — one step, one line, one stitch at a time.

I will keep posting my progress on this blog along the way.