Monthly Edition
September Edition: Data Science for Business Impact
How to help your company become truly “data-informed”
Data science may have matured a lot as a discipline in the past decade, but many companies still struggle to align their goals with the insights data practitioners produce. The reasons can vary: traditional businesses might be hesitant to try new approaches; young startups sometimes don’t have a robust infrastructure in place to collect and organize their own data.
Fortunately, data scientists and data analysts have the power to educate their colleagues about the benefits of making data-backed decisions. Before they can do that, though, they themselves need to know how to connect the right dots and communicate their knowledge effectively.
This month, we’ve selected some of our favorite recent articles geared specifically at industry data professionals. They go from high-level strategy to nitty-gritty execution, and reading them might help you break out of the fancy-algorithm silo in which data teams sometimes find themselves.
Happy reading, and thank you for your support of our authors’ work,
TDS Editors Highlights
- The What, Why, How, Who, and When of Data Strategy
Does your company have a plan for making the most of its data operation? Bahar Salehi believes it’s crucial to have one, but recognizes that “bringing data into the heart and culture of a business is not easy.” Her helpful overview covers the building blocks of a thriving data culture. (July 2022, 5 minutes) - Want to be Valued as a Data Scientist? Ask the Right Questions
Data scientists can’t singlehandedly convince every stakeholder of the importance of their work, but structuring conversations in a productive way can go a long way. For Genevieve Hayes, PhD, that means asking a lot of “why?” questions when you engage with colleagues from other teams. (July 2022, 7 minutes) - A Gentle Intro to Causality in a Business Setting
“Disentangling cause-effect relationships is typically overlooked in business and is a largely poorly understood practice,” argues Giovanni Bruner — but that deficit can become an opportunity for data professionals who know how to interpret causality accurately. (May 2022, 7 minutes) - The Joy of A/B Testing: Theory, Practice, and Pitfalls
One of the most common applications of data science in industry settings is the (by now) ubiquitous A/B test. Its popularity also generated more than a few misconceptions, but Samuel Flender’s deep dive will help set data practitioners on the right path. (August 2022, 10 minutes) - A Simple Interpretation of p-values
The gap between a well-executed A/B test and a smart business decision is full of statistical danger: your results won’t be of much use—and can even hurt the bottom line—if you misinterpret them. Dina Jankovic explains what p-values are and why they’re crucial for drawing the right conclusions from hypothesis testing. (November 2021, 7 minutes) - Marketing Mix Modeling 101
Ariel Jiang’s non-technical introduction to MMM is both accessible and thorough; it’s a great resource for data scientists who want to learn more about the potential contributions they can make to their company’s success. When you’re done, head right over to part 2 in Ariel’s series, which further explores the intersection of data science and marketing. (April 2022, 10 minutes)
Original Features
Here are our best original features and reading recommendations from the past month.
- Making Good Decisions: An Art, a Science, or a Bit of Both? Our Q&A with Cassie Kozyrkov, Google Cloud’s Chief Decision Scientist, is full of insights on data career paths, the value of data analysts, and public writing.
- Data Skills Can Make a Big Difference in Non-Data-Science Careers. We chatted with sustainability and energy analyst Himalaya Bir Shrestha on the benefits of improving your data and coding skills for a variety of non-data-science roles.
- Data Visualization: Going Beyond Charts. Tired of using the same plots over and over again? We selected some of our recent favorites on advanced and niche approaches to data visualization.
Popular Posts
Find out which articles your data science peers read and shared the most in the past month—here are some of August’s most popular posts.
- Top 5 Python Programming Books for Data Scientists by Benjamin Nweke
- 99 Lessons on Data Analysis from Placing Top 5 in 5 Kaggle Analytics Challenges by Leonie Monigatti
- Understand BLOOM, the Largest Open-Access AI, and Run It on Your Local Computer by Cristian Arteaga
- 5 Less-Known Python Libraries That Can Help in Your Next Data Science Project by Frank Andrade
- Data Documentation Best Practices by Madison Schott
- Creating a Cover Letter Generator Using Python and GPT-3 by Amber Teng
- 3 Underappreciated Skills to Make You a Next-Level Python Programmer by Murtaza Ali
We’re thrilled to welcome new and talented authors to the TDS community every month, and August was no exception—our most recent cohort includes Thu Dinh, Rohit Choudhary, Dustin Liu, Паша Дубовик, Shaefer Drew, Sabrina Göllner, Praveen Nellihela, Huong Ngo, Julio Marco A. Silva, Martina Giron, Ryan Xu, Garrett Kinman, Nimrod Berman, Luhui Hu, Matt Biggs, Artem Dementyev, Sergey Kotlov, Kyle O'Brien, Mike Clayton, Andrew Bush, Suvaditya Mukherjee, Ryan Posternak, Damien Azzopardi, Alberto Tamajo, Sara Tähtinen, Shambhu Gupta, Mihir Garimella, Dinesh Rai MD, Caroline Zaborowski, Anton Lebedev, David Sweenor, Morgan Green, Giovanni Organtini, Zvonimir Boban, and Caitlin Ray, among others. If you’d like to share your work with us as well, now’s a great time to take the plunge.
Until next month!
