Today marks 3 months since I launched a newsletter for my Data Analytics Journal.
As you know, I started writing this journal to bridge the gap between academic knowledge and industry requirements. I want to show what data analysis is or does “in action”.
Why landing an analyst job is difficult nowadays?
Today, data analysis means many different things, and every company is expecting something else from it. That’s why, it is challenging for analysts to adapt, meet the expectation, and understand what they have to do or learn to excel.
For smaller companies or early-stage startups, data analysts often become product analysts + data scientists + and data engineers all in one role. You will need to know SQL, Python, Statistics (heavy experimentation), data modeling, and data architecture. And how to play in beer pong. As the company grows, more and more differentiation develops between these roles and responsibilities.
For enterprises, a data analyst becomes more a business analyst with a strong focus on data systems and data validation knowledge and less on engineering or data design skills. You will need to know SQL, Excel, and Excel again, and the right printer settings at your office. (Okay, you also can shoot for SPSS if you feel more creative. Depending on the domain though).
Your value position, interview, transition, and professional growth for big or small (size, stage, profit) companies will be very different.
Anyway, today, I wanted to summarise some quick takes from the past month from my previous newsletters and published stories.
✨ 10 Quick Takes From Last Month Newsletters:
Refresh your reading list with these 15 must-read books on data analysis.
The best way to practice SQL:
Launching a new section - Ask Away! I aim to periodically post some common questions I receive from my subscribers. My first Ask Away post covers SQL interview challenges, A/B testing, and Portfolio questions.
Learn about how data analysis influences and develops cancer research enriching it with visualizations. Scroll down here and watch Cancer Moonshot leaders and data experts speak about cancer data visualization at National Cancer Institute.
How to choose the right ML algorithm? There are many different ML algorithms with different levels of complexities, and it can be challenging to figure out which model to choose for your analysis. For supervised ML, check my recent publication to decide which ML algorithm to pick for which business problem.
Did you know there are 715 total database systems? Here is an online encyclopedia of all databases, and it has the best name - Database of Databases.
The top 5 essential data engineering concepts data analysts should understand:
Types of data structures (how to work with structured and unstructured data)
The basics of data architecture or model (know the difference and purpose for a data warehouse, data lake, data mart)
Types of ETL (batch upload or streaming)
Concept of memory and cost in RDBS (!)
Version control (Git) and CI/CD pipeline
Want to be a data engineer? Start with a roadmap of data engineering in 2020 - a modern data engineering landscape covering must-know tools and frameworks.
Watch Live Coral Reef Cam from California Academy of Sciences in San Francisco. It’s not related to data analysis, but it’s calming and, if you are lucky, you can spot a diver!
🍸 Drink and Mingle
Upcoming free events, meetups, talk, webinars
Oct 1, JMP Statistical Discovery: Demystifying ML and AI
Oct 5, Nvidia GTC: GPU Technology Conference
Oct 7, At Internet: Data Model Strategy – Align Analytics with Your Business
Oct 14, Tableau: Mitigating Bias in Analytics
Oct 20, Neo4J: NODES 2020 Neo4j Online Developer Expo and Summit
Oct 28, Anaconda: Working with Data in the Cloud
If you missed my previous newsletters, here are the links to the last 3 months of publications:
Thank you all again for your support and for sharing this ride with me ❤️.
Until next Wednesday!