When There Is Data…?

an inquisitive data journal

Pauline Chow

Connects people, processes, and data. She is a polymath and successful intrapreneur with an insatiable curiosity and ability to connect multidisciplinary objectives. Her background is a unique blend of legal, advocacy, analytical, and systems experiences.

Currently, Pauline is data scientist and data engineer at start-ups and new company initiatives. She is a data science and analytics instructor at General Assembly @GA_LA, including adult immersive and part-time learning programs.

Data TinkeringGovernmentNLPOpen DataTransparency

Data Science in Politics: Legislative Bill Prediction with Topics Only

Quantifying and featuring-izing the “messy”world of politics can elucidate order and truth. It’s not quite there yet but I bet the Underwoods could not outsmart machines. Well unless they find a way to destroy the servers. Data science is already being used to detect sneaky business, such as fraud and security anomalies, why not in politics? Quantifying and featuring-izing the “messy”world of politics can elucidate order and truth. Politics is one of the most important yet uninteresting place to the public, who have everything to gain and lose from every day decisions. While the passage of legislative bills are not the end all be all of public policy shaping, understanding when and how laws change impacts us […]

GovernmentNLPPolicy: Elections

The Best Election Prep: Understanding Candidate Sentiment from the First Presidential Elections

We all should be sitting at the edge of our seats in the next couple of months. Change is inevitable but the vehicle may be questionable. In order to get psyched for this final stretch before the elections, as only a data scientist would/could/love to do, I conducted natural language processing (NLP) on this week’s first presidential debates. Let’s have the candidates great you first, where their silhouettes are enclose most key terms communicated during the debate. If you have been paying attention to the speaking style of the candidates then some things will appear familiar. “Country”, “American(s)”, and “jobs” are common in most political candidates. I find it interesting that the center theme of keywords for […]

Career Quest Logs: Q/AData Science Careers

Career Quest Logs: Should I Consider Data Science as a Law Student ?

Sharing is caring, especially when searching for the meaning in life and works can be aided by strangers on the internet. I am sharing questions and answers between myself and inquisitive minds so some of these conversations may help you. See survey: Your Road to Data Science. My Conversion with law student roommates. Big Take aways: Data science has become part of the vernacular of students and universities. The popularity of the term in the media has made data science career harder to understand. People interested initially in data science may often times be mixing the data science knowledge base with computer science and other technical positions Two bodies of knowledge are difficult to master simultaneously. If you want both data […]

Business GuidesData CollectionData Tinkering

Are You Curating the Best Data for Content Analytics?

Let Data Sets from Mashable and Portuguese Bank Be Your Guide. There is no denying that the next best thing for content analytics is data science. Yet for many content publishers this is often unattainable with their current data sets. Vanity metrics may be readily available but have less flexibility, especially when unique users and pageviews are not enough to demonstrate growth and stickiness. Beware of Vanity Metrics (HBR) can also point to the other pitfalls of relying on counting beans on the surface.  Most likely, medium to large content publishers are moving from or adding metrics to WordPress plugins or Google analytics tools.[1] Building data science structures is a harrowing endeavor because it encompasses all aspects […]

Data Science Careers

Aspire to be a Data Scientist: Take the Road to Data Science Survey

Where are you in your journey to data science? If you’re interested in receiving customized recommendation for your next steps, then fill out the Road to Data Science survey: http://goo.gl/forms/uFdzXhWIa8 I will respond to most respondents through email and then set up one-to-one google hangouts or calls with a small group of respondents. Make sure you provide your email, twitter handle, or LinkedIn profile. Thanks for your insights! Loading…

Data Science Careers

So, You’re the First Data Scientist: Data Culture Questions for New Teams and Leads

The most important role of the leading data scientist and analytics person is to lay a solid foundation for the rest of the team. There is a high demand for enterprising data scientist and data professionals to pave the way for data in all types of organizations. Businesses have a FOMO or fear-of-missing-out, especially if they have not been fundamentally data informed. The accelerated frenzy for talent is the result of increasing data sources, open source systems, and third party tools. As businesses orient to data driven cultures, professionals have opportunities to reframe their experience, skills, and abilities to be data oriented. In the early boom time of “data scientists,” the role can hold a level of mystic and magic […]

Data Science Careers

Journey into Machine Learning at Georgia Tech OMSCS: Tips and Considerations

It turned out to be a wonderful Fall Semester at Georgia Tech through their Online Master in Computer Science (OMSCS) program. As of 2015, OMSCS is a unique MOOC (Massive Open Online Courses) partnership with Udacity. The most promising aspects of OMSCS is its accessible, affordable, and challenging curriculum. I personally applied to the program to dive deeper into machine learning, which I wasn’t able to do it on my own or in other non-degree MOOC programs. Coursera is a great place to start to get a solid data science and analytics foundation, the courses range from free to super affordable. Now, for what you came for, the low down on Machine Learning at Georgia Tech (CS 7641). […]

Data Science for Organizations

Using Social Intelligence Across the Entire Business: Software as a Service Edition

fireworks, explosion, black background

Originally posted on LinkedIn Pulse on January 4, 2016.   Social Media Week Chicago 2015 in November featured a master class on business intelligence using social analytics. The three speakers on the panel were from start-up fashion publisher (Clique Media Group, Inc. or CMG), digital marketing agency (Tenthwave Digital), and retail pharmacy chain (CVS Health). The panel discussed ways to leverage social media to make business decisions, gain customer insights, and stay competitive. Insights in social media in this class were leveraged from Digimind’s social listening and analytics platforms, which are known as software as a service (SaaS) products. Read the complication of tweets and panel notes in this storify link. I was invited as […]

Data TinkeringOpen DataPolicy: Legal and Justice

Open Data: How do Los Angeles domestic violence crimes look over Time?

vast mountain view with fog

It’s not news that there has been a nation wide hike in crimes across the United States, including Los Angeles. NPR episode on LAPD. Inequality and crimes against fellow humans are disheartening and could often seem impossible to resolve. Open data provides one resource for viewing impossible problems and collaborating on solutions. Hence the vast mountain range in the header photo. Summary of points from the review of LA crimes with a focus on domestic violence occurrences: What programs could be extended to mitigate the impact of family violence when school-age children are home from school? Below reviews show DV crimes increase in the summer time and before the holidays and New Year. There is […]

Data Science CareersData Science for Organizations

Chasing the Data Dream OR Show me the Data

This is a “thing” in your data science career: chasing the promises of the data. If you haven’t already had this experience then you are a lucky duck or you are now jinxed. (Sorry, not sorry) A CEO and tech writer, Slate Victoroff, wrote in Big Data Doesn’t Exist, “clients lie about how much data they have” above all else. Take at least two antidotal experiences on limited data and now assume your client or interviewing party only has a fraction of what they tell you. Victoroff applies the one-thousandth rule of thumb. I take a more nuanced approach and assume that clients have data but will probably only be able to query about 25% of […]