Data + Brit

This weeks Makeover Monday 's data set was the Top 100 Song's Lyrics. After just returning from Tableau's annual conference and being eager to try their new feature, TabPy , this seemed like the perfect opportunity to test it out. In this blog post, I'm going to offer a step-by-step guide on how I did this. If you haven't used Python before, have no fear - this is definitely achievable for novices - read on!  For some context before I begin, I have limited experience with Python. I recently completed a challenging but great course through edX that I'd highly recommend if you are looking for foundational knowledge -  Introduction to Computer Science and Programming Using Python . The syllabus included advanced Python including Classes and thinking about algorithmic complexity. However, to run the analysis I did, it would be helpful to look up and understand at a high level: basic for loops lists dictionaries importing libraries The libraries I

As some of you may know, I co-founded a group in the Bay Area for women who work in the data industry with Chloe Tseng  back in March. These past 8 months have been extremely rewarding for me. Not only have I been sharpening my organizing and community building skills but I've built an amazing network of friendship and support.  One thing that continually will come up in our conversations is the idea of "non-traditional" vs. "traditional" educational backgrounds. "Traditional" referring to professionals who have a STEM (i.e. Computer Science, Statistics, Math, etc.) background versus those of us who have a degree in a liberal arts field (i.e. Communications, Business, PoliSci, etc.). It's really interesting to see how this manifests in the types of struggles they face. Speaking personally, I studied Political Science and had a few non-data jobs before entering this space. I've always felt a bit behind from peers who have STEM backgrounds whi

Last Update: May 22, 2017 Over the last year I have noticed that as my social engagement increased I started to receive many messages to the likes of the following:  "I came across your Tableau profile/blog/Twitter and as a new user I would love to know your journey/resources you used to learn the tool." "How did you enter the data field with a background in political science?" "I came across your profile and was very impressed with your achievements and career path as you have grown into the Business Intelligence field. What advice would you have to a new comer?" "As someone who came from a non-technical background and quickly grown into the BI field successfully, I am wondering if you would share your experiences and tips."  For a while I felt a bit out of place to receive the compliments and struggled to realize I had a point of view that could be valuable to others in their own career progression. With the support of my peers and

Today I decided to participate in Andy Kriebel's and Andy Cotgreave's #MakeoverMonday Tableau Challenge. I have been enjoying seeing this series progress the last few weeks. People have produced such interesting variations of the same data set (see the variety, here !). I particularly like this project for a few reasons: It removes all the pre-viz steps such as data collection, cleaning, etc. and allows you to focus right in on best practices and design. If you stick with suggested 1 hour time block, it makes participation less daunting. It allows you to see what others in the community came up with for the same dataset. I have been finding myself having a-ha moments and really drawing inspiration...I might have an update to this with a makeover of my own incorporating all my favorite parts of others :) By engaging with the community, it will encourage you to continue to participate and expand your skillset. So why not give next week's #MakeoverMonday a go? Full det

I have recently been experimenting with what I've seen being referred to as a tile map, grid map or periodic map. NPR did a great write up on traditional choropleth maps, cartograms and tile maps. Some awesome Tableau folks have also done great tutorials and published these non-traditional map types publically including Brittany Fong , Matt Chambers and Kris Ericson . There are definitely instances where this type of map enhances the data view or enables better flow and certainly some where it won't be suitable (for example, showing data at the county level among others - example ). I came into this field from a non-traditional background like many others. There's definitely an emergence of new or rebranded data science degree and certificate programs. I was excited when I came across Dan Murray's article on the Interworks Blog  that used data and an awesome tableau visualization to show programs throughout the U.S. Since I came across this at the same time tha

A connection of mine recently shared a great resource with me for those of you who are aspiring data scientist or just love data. It's an open-source data science program that can be found here:  http://datasciencemasters.org/ . Check out this great data repository compiled by the project: Open Data List of Public Datasets  - user-curated DBpedia  - utilizing a large multi-domain ontology Public Data Sets on AWS  - common web crawl corpus, NASA satellite imagery, Human Genome, Google Book NGrams, Wikipedia Traffic, Million Song Dataset, Federal Reserve Economic Data, PubChem, more. Governmental Data Compendium of Governmental Open Data Sources Data.gov (USA) Africa Open Data US Census  - Population Estimates and Projections, Nonemployer Statistics and County Business Patterns, Economic Indicators Time Series, more. Non-Governmental Org Data The World Bank  - business regulation measures, company-level data in emerging markets, household consumption pattern