If a data scientist identifies a powerful new insight, but no one around her understands it, does it even matter? This question gets to the heart of why building a data-driven culture should be a core objective for your organization. While building a data science team is a strong start, it is only a piece of a puzzle.
Fatherhood is one of the big milestones in a man's life. From diapers to driving lessons, becoming a dad is a life-changing event. With Father's Day just around the corner, the best way we know to kick off the celebration is to dive into the data and learn more about fatherhood in the United States. You can also download data from United States Census, National Responsible Fatherhood Clearninghouse and NRF's Annual 2019 Father's Day Spending Survey to perform your own analysis and visualizations.
Last week, we dove into the Victoria’s Secret dataset and discovered that they had a huge variety of not only bra types, but also colors, sizes, and materials. Whenever you start working with a new dataset, always remember to explore it first so you have a good idea of the distribution of the data and you may even have new questions. Now that we have a better understanding of Victoria’s Secret’s inventory, we can ask more questions about their pricing strategies, such as:
- What’s the price distribution for each product category?
- Which cup size is cheaper? And which cup size is the most expensive?
- If you want to save money, which color is cheaper?
Victoria’s Secret is one of the best-known brands for lingerie, pajamas, and bathing suits. Thanks to Kaggle, we have the opportunity to dive into their best-selling products and see how their pricing reflects their inventory. In this first part of our series, we’ll explore their inventory and unique products to get a better understanding of the overall dataset. This is always a best practice when you work with your own data - when you invest the time up front to get an overview of your dataset, you’ll better hone your analysis and save a lot of time in the long run.
And how we can fix it.
A few weeks ago, we wrote about why we were building and distributing our Data Science Communicator Toolkit. Part of our initiative included collecting information from people who work with data so we could shape the toolkit to help bridge the communication gaps between them and their colleagues. We found some interesting results and are excited to share them with you here, as well as some recommendations for how you can alleviate the road blocks that your organization faces on its way to becoming more data driven.
A core tenet of our mission at Data Society is to empower employees and teams with powerful data science skills, and provide them with the tools to implement analytics to automate processes and find new insights.