Top Ten Quick Takes From The Past Month - Issue 24
A recap of my newsletters and publications over the past month.
Hello analysts! While you are wrapping up the work for winter break, I am celebrating 6 months of this newsletter (wow, time flies!). Thank you for reading the Data Analysis Journal. Hope it helps you to learn, develop, and share the love for data analysis.
This month I covered common types of data analytics positions, shared an interview with a data expert who let us take a peek behind the scenes of data analysis, covered new SQL, Python, and ML training and courses, and shared some public datasets for research and analysis. This issue is a recap of my past month’s newsletters.
🏆 Nailed It
Be prepared for your next interview
Working with timestamps and data formats in SQL can be tricky. Often dates are loaded and stored as strings, var, or else in databases. And yet, date-time values are the most common data manipulations in SQL. Recently I wrote a quick cheatsheet (or checklist) about using 3 important date functions like DATEPART(), DATEADD(), DATEDIFF(). They are often asked during SQL coding interviews. Also, I shared some handy date extraction solutions or formattings that I applied in my SQL for data analysis over last month.
Let me know if you stuck with SQL or need help with formatting or special case data extraction. I have a big love for SQL 😍
✨ 10 Quick Takes From Last Month Newsletters:
Being a data analyst is the most challenging role of all. You have to represent the beauty of both technology and business within a single role. Read about common types of analytics roles and expectations of analysts’ responsibilities in What exactly data analysts do.
Read an insightful interview with Trenton, who is the Director of Data at Vida Health. Previously, he had roles at tech companies such as Zynga, as Head of Analytics at Life360, and as an Instructor and Speaker at General Assembly. He shares his view on common analytical projects, expectations from candidates during hiring interviews, team values, personal learnings, wisdom, and more.
Databricks launches SQL Analytics, a new tool that makes it easier for analysts to run SQL queries directly on data lakes. It means you can connect your BI tools like Tableau or Microsoft’s Power BI to these data repositories and query them directly. Way to go, Databricks! You’re making the world of data analytics just a bit easier to navigate through.
Follow a step by step Astronomical Data tutorial on how to use Python for data analysis and feature engineering. Open notebooks to learn how to extract, process, transform, analyze, and visualize data.
A new free 4 hours course is launched on Kaggle: Intro to Deep Learning. Sign up to learn how to use TensorFlow and Keras to apply neural networks to real-world datasets.
Read this comparison of Amazon Timestream vs DynamoDB for time series data storage. “DynamoDB is faster for targeted queries, whereas Timestream is better for analytics that includes large amounts of data.”
Check out Observable, a free platform for creating, sharing, and tweaking data visualizations. Get inspired by amazing interactive and sophisticated graphs and charts along with the code on how to re-create them for your analysis.
Listen in this episode to Chris Miller, the Director of Product Growth at Hubspot about his take on product growth and marketing and learn more about user segmentation analysis - how to use advanced segmentation to serve a diverse audience.
Check out the new 2020 Expansion SaaS Benchmark report from OpenView Venture Partners to compare your KPIs performance against your SaaS peers or competitors. Maybe your team is performing better than you think!
For any data work, you have to find your place of zen first. Calm yourself with Vancouver Aquarium webcams to watch cute otters (and baby otters!!), jellyfish, and penguins.
🍸 Drink and Mingle
Upcoming free events, meetups, talk, and webinars.
Jan 4, Galvanize: Intro to ML
Jan 6, Galvanize: Intro to Python 1
Jan 6, InfluxData: Monitor Your Homebrew Using InfluxDB Cloud, Telegraf, and Raspberry Pi
Jan 11, Galvanize: Intro to NLP
Jan 13, Galvanize: Intro to Python: Part 2
Jan 18, Galvanize: Intro to SQL and RDBMS
Jan 21, Galvanize: How to Approach a Python Coding Challenge
🙏 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 December newsletters, here are the links:
Thank you all again for your support and for sharing this ride with me ❤️
Until next Wednesday!