Medicines and Healthcare products Regulatory Agency’s Post

For data analysts/data scientists/statisticians: How to communicate data, in layman's terms, to data illiterate decision makers: https://lnkd.in/gkguB9es

Here are some invaluable pieces of advice I received — and followed — while seeking employment as a junior data engineer.

In this blog post, Egor Howell reviews and explains the differences between a data scientist, analyst, and engineer:

Data is ubiquitous, but understanding and deriving meaning from it is a different challenge altogether. Jay Baer reminds us that while data exists everywhere, not everyone harnesses its power. This underscores the crucial role of skilled data analysts and scientists who can manage large datasets and apply them effectively in business contexts. Want to begin your journey as a data scientist? Visit www.analytixlabs.co.in

The Big Grid At almost every company I work for, I wind up building (really have built for data science)”The Big Grid.” TBG is defined by each row representing a device/user/household (depending on the company) and each column represents data about the device/user/household. TBG lets data scientists and analysts do rapid analysis on a well structured dataset. It is generally kept current and historical versions might be saved, but it is intended to represent what we know about our devices/users/households at a given time. I think of building and maintaining a TBG as best practice in data science. As an aside (and because I am basicly a dock worker with a PhD) in some roles, we referred to TBG as TBFG. The downside of having my first real job as a Roadie 😞.

Airline pilots, firefighters, astronauts, architects, and...data scientists? That's right, HBR says Data Scientists are the sexiest job of the 21st century. Learn how to attract more of them to your business with our handy guide.

Some of the data we get to work with at the MTA is, I have to say, just very fun. You get to see how the city moves, and there's so much richness that it takes a big community to really explore it. This is why we love our Open Data program: we get to put this data in the hands of New Yorkers (and folks around the world!) who share a deep interest in transit. This week, we are releasing a big (!) dataset that I think is especially exciting: the MTA's "pretty good estimates" of how riders are traveling across the subway system. It consists of "Origin-Destination" pairs that, as Julia Lynn writes in this blog post (https://lnkd.in/eUHPMgQm), estimate where riders travel across the city after they swipe or tap into a station. This is the same data that our own data scientists - including teams led by the esteemed Jay Krantz and Daniel Wood - use to plan service changes, shuttle routes, and more. And now, it's in your hands to work with too. Take a look at the blog post (https://lnkd.in/eUHPMgQm) and the Open Dataset (https://lnkd.in/e6XKZc-d). If you build something cool with it, let us know at opendata@mtahq.org! And finally, if this kind of work excites you, our team is currently hiring Data Scientists. If you or a friend is interested, encourage them to reach out and apply here: https://lnkd.in/e7WTHCTN

Behind every successful data scientist is a history of data missteps. Everything from bad assumptions made about data to bad choices made about data pipelines, and plenty of bad results. As Thomas Edison said, “Results! Why, I have gotten a lot of results. I know several thousand things that won't work.” After nearly 15 years in the field, I have certainly learned a lot of data science things that didn’t work for me (SMOTE and TSNE, I’m looking at you). Next month I’ll be sharing some of my key lessons learned and spilling some tea at the 10 year anniversary of PyData London: https://lnkd.in/esWQ6DWN Some day I should probably write a book about the chaotic early days of data science. Until then, join me June 14-16 at PyData London, right after #LondonTechWeek. Hope to see you there! #pydata #numfocus #pydatalondon #datascience

Are there any connections within my network currently seeking to fill a Data Science position, or perhaps aware of a company in search of one? While this isn't my specialty, I recognize the importance of networking and its potential impact. If so please comment below. I met Ceren Engin during a workshop I facilitated at and they are looking for work! Ceren Engin Please comment below any additional details that you want to share!

At LefeWare Solutions we've run into multiple instances where our clients went out and hired a high-level, expensive data scientist, only to be left disappointed with the results. Why might you ask? Watch the video below to find out https://lnkd.in/dU7uWukU