What A Year Of Writing About Data Analysis Taught Me - Issue 54
Celebrating one year of writing. Journal stats, updates, and confetti.
Good morning analysts, and welcome to a free edition of the Data Analysis Journal newsletter, where I write about data analysis, data science, and business intelligence.
This month marks one year since I started writing my Data Analysis Journal 🎉🎉🎉. A year! With a published newsletter every week. Through the sun, rain, pandemic, and deadlines. A year was put into 54 publications, 32 deep dives, 9 interviews, and 1 scoliosis. And more to come.
While I am pouring champagne to celebrate, here’s what you missed this month if you’re not a paid subscriber:
When To Use Client-Side Or Server-Side Events For Analytics - the difference between client-side and server-side events and how to figure out which events to use for reporting and analysis. Which KPIs and metrics should be reported from the client or server-side data, and why.
How To Install And Set Up Python - a step-by-step guide on how to get started with Python basics and its environments for data science and analysis, including homebrew installation, Pycharm, and virtual environments. The difference between Python and iPython.
Currency Conversion In SQL and Python - how to approach exchange rates for multiple currencies in SQL for revenue reporting. I offered you 2 solutions (SQL and Python+SQL) on how to generate an exchange rate table that will allow you to convert different local currency values into one target currency for your analysis.
On this newsletter’s special occasion, I decided to change things a bit and share with you what I’ve learned through one year of writing about data analysis, its purpose, what I use to get it done, and a curated selection of its content published over last year.
⏳ A little history
When I was little, I had a dream to become a journalist. Growing up, I developed a strong passion for data and analytics that led me to technology and information. I’m glad I chose this route, as I now find it the most fulfilling and exciting. But writing and blogging always stayed an integral part of my life (although moving between countries and adopting a new language made it way harder). I have occasionally published articles on different blogs since 2015, but it took me almost 5 years to learn English prepositions and get the confidence to start my own thing.
In July last year, I wrote my first newsletter. Little did I know that it would lead to a whole new era of writing with over a thousand readers around the world.
✨ Mission
I started writing this journal for 2 reasons:
Bring classic data analysis back.
You are less likely to meet an analyst today who has an old-schooled true understanding of analysis. Every day there are more and more people with decent Python and SQL knowledge who can perform data manipulations or develop models but are still absolutely lacking the basics and foundation of analysis, BI, and statistics.
Bridge the gap between academic knowledge and industry requirements. I want to show what data analysis is or does “in action”.
I had a rough start transitioning into the industry after finishing my MS. It took me years of trial and error to figure out what type of analyst I wanted to be, which framework to use, conversion to pick, or analysis to run. I was lucky to have an opportunity to work at companies that allowed me to grow. I had amazing managers and mentors who encouraged me to learn and develop in my industry and skillsets. Not everyone is that lucky. Now I want to give back and create a resource I wished so badly to have for myself years back.
📖 Learnings
Expectation and reality. When I first started writing, I saw my publications as a quick roundup of the most important events in the data analysis world. I didn’t expect my weekly newsletters to become long and content-heavy as they did. The newsletters were initially meant to be light and drive the traffic to the Journal site which stores all the content - deep dives, research materials, code. The reality was that my readers didn’t care much about news, and wanted to see more case studies, tutorials, and guides. So in a few months, I had to pivot and shift my strategy from light news to 3-4 case studies per month. And now I don’t sleep.
Diversity of analytical roles. I believe that analytics is the least specialized function. My intention was (and still is) to target mostly data and product analysts, but my audience ended up as a wild mix of product managers, data scientists, data engineers, data architects, business analysts, financial analysts, researchers, and colorful trolls. Which is great! From one side. Every time I publish a Python article, I see a wave of unsubscribes from less technical roles (I get it, you don’t like Python). When I publish about the product or business analysis, I see unsubscribers from DBAs. It’s expected, and it is a real challenge to create interesting and helpful content for everyone.
Content and feature requests. Besides requesting case studies, my readers ask for more SQL publications and tutorials. Makes sense, given that SQL is a must-know language for every role that touches data. Many users ask for more One-Pagers or Cheat-Sheets. Something super short that you could quickly pull during your meeting to brush up on the metric formula or description. So far, I’ve published a few, and am aiming to create more. Quite a few users messaged me asking for datasets to replicate a study I posted. A few asked for my help with SQL or Python challenges - either getting the right DATE formats, transforming data frames, or building a query. I am happy to help if I can! And I hope these were not the homework or take-home challenges because that’s just not cool 🚷
🛠️ Behind the scenes
All the content is written by me and is solely my perspective as an analyst on a problem, tool, solution, or book. There are no reposts from other bloggers (yet), no paid promotion, and no sponsorships.
It takes a lot of work to generate and deliver original content in a week. Every week. I learned to be fast, productive, and consistent. This wouldn’t happen without coaching, support, and help from family, friends, mentors, and an espresso machine. Meet me and my squad:
🚀 What is next
My end goal is to create a community of analysts who will collaborate, share, learn and support each other. I dream of a platform (or a hub) where readers can download materials, access datasets, practice SQL live, express frustration, see examples of case studies, learn visualizations best practices, adopt frameworks, or find mentors.
While I cannot get there now, I hope this newsletter can help its readers find a path for professional growth, point to the right sources and documentation, and inspire themselves or others to learn and love data analysis.
Over next year, the newsletter format might slightly change. I want to offer more studying materials and become more accessible for questions and helping with challenges. I also will be writing more about leadership and management in analytics, and scaling the impact data teams generate.
😍 What readers say
“In addition to being full of resources, Olga's newsletter provides a crucial framework for understanding the practice of data analysis. Read it to gain insight into real-world analytics problems and find ways to improve your own skills.” - Lira, Analytics Manager
“Olga's newsletter is a one-stop-shop for all of your data-related resources, whether you're starting out new in the industry or a seasoned data enthusiast - you'll find great information that'll help expand your data world! I love the wide variety of topics this newsletter covers that you'll be sure to learn something new from it. I'm so glad to have this newsletter as a resource when I transitioned into the industry last year. I call this my e-mentor! “ - Helen, Data Scientist
“I’ve been reading Olga’s newsletter for as long as I can remember. As someone new to the industry her newsletter felt like having a personal data mentor showing me the do’s, don’ts, and what’s new in the industry. It’s a must-read for anyone starting out in the industry or wanting to keep up in the industry i.e everyone!” - Adaobi, Analyst
Thank you to everyone who shared their feedback, it means a lot!
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Checklists:
Onward to the next year!