Hello dear readers, and please join me in celebrating 4 months since I launched the Data Analysis Journal! Time flies!
A little history:
Many years ago when I got my Masters, I had no idea what to do it with. I knew statistics but was failing interviews miserably. I had no idea how to position myself or “get my foot in the door”. It was frustrating and scary. I was lacking terminology, key-words interviewers were looking for, and was speaking a language that people didn’t understand (literally).
10 years later, after many written notebooks, A/B tests, ETLs, deep-dives, forecasts, vlookups, mentors, and misspelled and mispronounced terms like “ROI” or “Acquisition”, I now have the experience, expertise, and an added bonus of countless data set visualizations and charts permanently burned into my retinas from my many late-night research sessions. Now, when I'm getting the chance to interview some of the smartest candidates in similar fields, I can see that same journey in their eyes, as well. I feel that I'm in a position to help anyone who's interested in this subject, hence this journal.
I started this journal because I want to bridge the gap between academic knowledge and industry requirements. I want to show what data analysis is “in action” - what industry leaders expect from analysts. I write about practical examples, interesting solutions, helpful tips, upcoming events, and relevant news for analysts.
Next month, I’ll be launching a new section dedicated exclusively to interviews, where data analysts experts will share their expectations for candidates, day-to-day projects, challenges, and values. So stay tuned!
In October I focused more on visualizations, SQL, SaaS metrics, and shared helpful resources, events, and materials on data analysis, data science, and BI. This issue is a recap of my newsletters over the past month.
📚 Weekend Longreads:
Product analysis. If you work with user acquisition, expansion, conversion, and retention, you should check a product-driven methodology from The Product-Led Growth Collective, where all your metric is primarily driven by the product itself, and not sales or marketing.
Data analysis. If you enjoy data storytelling and want to get inspired by different visualization projects around the world, check this Reddit which gathers together all topics related to visualization, the design of graphs, charts, maps, etc.
Data Engineering. Read this article from the AWS Big Data blog to learn how to steam data from Amazon S3 to Kinesis for performance analysis, production monitoring, or supply chain optimizations. It describes a solution for converting batch processing to near real-time using AWS DMS.
✨ 10 Quick Takes From Last Month Newsletters:
Curious how much data scientists earn for developing ML algorithms? Here are statistics of industry job offers in AI at FAANG.
Congratulations to Regina Barzilay, a professor at the Massachusetts Institute of Technology for becoming the first recipient of the $1 million AAAI Squirrel AI Award (it’s like a Nobel Prize, but way cooler). She received recognition for outstanding work developing ML algorithms for detecting cancer and designing new drugs.
In case you missed, Twillio acquired Segment for $3.2 billion. Segment is, in its own way, a very sophisticated CRM. Its main draw is the capability to unify multiple data pipelines. I’m definitely curious to see how Twillio+Segment will work together to improve customer analytics.
Last month I published my second One-Pager (or a Checklist) - SaaS Growth Metrics. It covers the most important SaaS KPIs you will be working with: MRR (new MRR, churned MRR, net new MRR), Churn (customer and revenue churn), LTV, CAC, months to recover CAC, and Expansion Revenue. I also provided some estimations to give you an idea of how to determine your product health and covered some analysis specifics for churn, LTV, and MRR.
Want to learn Python but don’t know where to start? PyData Global is running a free online Python beginners workshop at PyData on November 7th. Hurry up, as numbers are limited, and the preference goes to applications from under-represented minorities in tech. Register here: https://humbledata.org/event/pydataglobal2020.html
Check one of my previous newsletters on the top 10 must-read blogs on data analysis.
If you work with PostgreSQL and want a quick way to evaluate and optimize your SQL queries, check out SQLbench. It measures and compares the execution time of one or more SQL queries.
Read my recent deep-dive into types of visualizations to know how to pick the right chart for analysis and learn how to make plots and graphs in Python or R.
If you need a little kitten therapy, you must watch this Kitten Academy Live Stream in Fox Lake, IL. It’s not related to data analysis, except in the sense that extensive research and subsequent data analysis results have proven that, in fact, kittens are cute.
🍸 Drink and Mingle
Upcoming free events, meetups, talk, webinars
Oct 28, Anaconda: Working with Data in the Cloud
Oct 28, DSS: Credit Risk - Why Model Fairness Is Needed
Oct 28, Grid Dynamics: Connecting the Missing Link: From Data to Insights
Oct 28, WIA: Virtual Career Fair (analytics and data science)
Nov 2, RMDS: Innovative Methods with Data Conference
Nov 12, DSS: Become a Data Science Superhero with Python
Nov 18, Anaconda: Data Exploration and Dashboards Using Familiar APIs
Nov 20, SWD: Accelerating ETL for Recommender Systems
🙏 Do Some Good
It pays back.
I have a favor to ask. As my audience grows, I’d like to better understand how this journal can stay interesting and help you to become a better data analyst, data scientist, and progress in your career. I’d appreciate it if you could take a moment to fill out this brief survey. (And thank you to those who have already done it!)
If you missed my previous newsletters, here are the links:
Thank you all again for your support and for sharing this ride with me ❤️.
Until next Wednesday!