Celebrating 100th Newsletter 🎉- Issue 100
Celebrating 2 years of writing and 100 newsletters by sharing my favorite substack reads, gratitude, and wild blasts of confetti.
This week marks the 100th newsletter and almost 2 years since I started writing my Data Analysis Journal 🎉🎉🎉. Almost 2 years were put into over 100 publications with deep dives, interviews, recaps, roundups, checklists, code snippets, an unhealthy amount of sugar, and a broken espresso machine. 100 issues through the sun, rain, pandemic, deadlines, and the war happening in my home country now.
I want to thank all of you who are here and who read my newsletters. Thank you for supporting me in my writing, for loving data analytics, and for going on this journey with me. I couldn’t have done this without your support or the help of my content creation coach, Jared. It takes a lot of work to generate and deliver original and technical content in a week. Every week. With Jared's help, I learned how to be fast, productive, consistent, and transform my chaotic thoughts into structured and readable text.
8 years or so ago connecting minds on writing and analytics
Today I’m honored to have readers all around the world, all with different levels of knowledge and experience, ranging from experts to beginners, and people transitioning into the world of data from other fields and across all industries. Data analysis is a challenging field, still emerging and always transforming. Navigating through it is tough and exciting. It feels amazing to be connected with thousands of analysts all over the world.
📊 About This Journal
All the content is written by me and is solely my perspective as an analyst on a problem, tool, solution, or a tendency. There are no reposts from other bloggers, no paid promotion, and no sponsorships. While I do receive many requests to advertise and promote a product or a service, I decided not to launch sponsorships - I value the trust of my audience and want to keep my unique perspective on tools and solutions shared here. You might see occasional “Try It Out” chapters where I share an application, but they’re not sponsored in any way except that I fell in love with it and I’d like to recommend it personally. I also just added a Recommended Blogs tab with a list of my favorite Substack newsletters about data analysis, product, and data science that I highly recommend subscribing to Applied ML | Recommender systems, Terence Talks, Lenny's Newsletter, Meandering the Modern Data Landscape, benn.substack, and others.
❤️ Giving back
To celebrate the 100th newsletter, I am offering 3 months of free access to all paid content for my active readers. To redeem, follow this link:
If you enjoy what you read here, please forward it to a friend or colleague who you think might like it too. I’d be grateful for that.
Over the years, I’ve created a lot of checklists, playbooks, and code snippets. This journal is a place for me, first of all, to store, keep, and reread this type of content. My favorite articles across different domains, best frameworks, good examples, gotchas and caveats, reminders, calculations, and definitions - all of which are based on my own experience, learnings, or research of best practices and working solutions. I put it all up here for my subscribers to reuse and hopefully upskill and grow.
With my journal, I also want to 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, what conversion to pick, or which analysis to run. I was lucky to have opportunities 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. Not everyone is that lucky. Now I want to give back and create a resource that I wished so badly had existed for myself years ago.
🚀 What is next
As the journal audience grows, I want to make sure I offer relevant content for my readers that is interesting and helpful. Given that I have a wide array of subscribers who are analysts of all types and flavors along with data engineers, ML scientists, and product managers, it’s not an easy task. Over the next year, I aim for this journal to offer more studying materials, deep dives, checklists, and case studies, and become more accessible for questions and help. I’ll continue focusing often on SQL and Python. I don’t use R now and haven’t for a while, therefore I won’t be sharing R resources here.
My biggest obsession is product analytics, so I’ll stay under its umbrella. I might share occasional articles on ML and NLP and their resources, but I don’t work now with unsupervised learning and AI, so won’t be writing about it much.
Over the next year, I plan to start doing “guest posts” and sharing learnings from other analysts whom I admire and learn from.
I’ll keep the same newsletter frequency - paid subscribers get the weekly post, and non-paid subscribers get a monthly post.
I believe the best investment is an investment in your own skills, expertise, and growth. If you think my journal is helpful, consider supporting me by sharing my posts or upgrading to a paid subscription.
And if your company is interested in a multiple-seat subscription, please contact me for a customized subscription link.
I hope this newsletter can help its readers find a path for professional growth in data and analytics, point them to the right sources and documentation, and inspire themselves or others to learn and love data analysis.
And a picture of my cat Monet. Yes, it’s relevant and important.
Thanks for reading, everyone. Until next Wednesday!