Sense has launched to the public today, March 18, 2015. Sense is an online data science platform providing you the capability to easily perform your entire data science workflow via a browser. No need to provision new servers or install software, just click “New Project” and start your analysis. The Sense platform includes the following features:
Languages: R, Python, Julia, SQL
Simple collaboration for teams
Easily Scale up or down with just the click of a mouse
Notifications for completion of long running tasks
An on-premise Enterprise version
I have been one of the early beta-testers for Sense, and I have previouslywritten about using the platform. I really like the platform. I find it easy, intuitive, and clean. Plus, I love being able to run all my analysis with just my chromebook. So, go signup and please feel free to follow me at sense.io/ryanswanstrom as I am sure to be adding some new analysis.
Below is a video with an expanded introduction to the Sense Platform.
Ben Wellington gives an excellent Ted Talk on open data. He argues that cities need to make more of an effort to release data in a standardized and machine-readable format. This could help cities be safer and fiscally responsible. He is hoping New York City sets the standards for open data for cities. As a bonus, he is a wonderful story teller.
Have you ever wondered what the deal was behind all the hype of “big data”? Well, so did we. In 2014, data science hit peak popularity, and as graduates with degrees in statistics, business, and computer science from UC Berkeley we found ourselves with a unique skill set that was in high demand. We recognized that as recent graduates, our foundational knowledge was purely theoretical; we lacked industry experience; we also realized that we were not alone in this predicament. And so, we sought out those who could supplement our knowledge, interviewing leaders, experts, and professionals – the giants in our industry. What began as a quest for the reality behind the buzzwords of “big data” and “data science,” The Data Analytics Handbook, quickly turned into our first educational product of our startup Leada (see www.teamleada.com). Thirty plus interviews and four editions later, the handbook has been downloaded over 30,000 times by readers from all over the world In them, you’ll discover whether “big data” is overblown, what skills your portfolio companies should look for when hiring a data scientist, how leading “big data” and analytics companies interview, and which industries will be most impacted by the disruptive power of data science. We hope you enjoy reading these interviews as much as we enjoyed creating them!
Do you need to build your skills to land that dream job as a data scientist? Well, Metis Data Science Bootcamp in New York City offers a 12 week program to do just that. The program sounds fantastic with a few of the highlights being: help landing a job, great guest speakers, no PhD required, and a strong curriculum. Tarlin Ray, Head of Admissions at Metis, was kind enough to answer 3 questions about the bootcamp.
If you are interested in applying, the next bootcamp starts in April and the deadline for early admission is next Monday, February 16, 2015.
Can you describe a student who would be successful in the program?
If students do not have the technical skills, then they will struggle in the Metis Data Science bootcamp. Students must have some programming background and experience with statistics in order to get the most out of the bootcamp. We do not set a minimum score for what that means, rather we use our application, coding challenge and interview to asses someone’s skill level. The technical skills are definitely a determinant of success but really serve as one piece of the equation. Students with strong verbal and written communication skills will be very successful in the program. A student must be adept at listening to instructors, fellow students and even speakers in the program. This skill will enable the student, as a data scientist, to get to the heart of an issue and to create a question or set of questions to help uncover insights. Students should have an innate curiosity. The thirst for the answer (1+1=2) is often only half of what a data scientist is tasked to do. The other half is the search for the why something is the way it is. Students should not be afraid to tap into their creative sides. The output for many data science projects is a visual representation of numbers, statistics and business insight. Students need to feel comfortable with a blank canvas and muddy data with the goal of creating a new work of art, digestible by a wide audience. Lastly, a student should have a ton of grit. There should be no give up in the student. They should be comfortable with the unknown, googling for answers and just figuring things out. The life of a data scientist is one of a life long learner. Successful students will have this quality. (for a more in depth answer to this question please check out a blog post written by Laurie Skelly, co-creator of Metis Data Science Curriculum and Data Scientist at Datascope Analytics.)
What sets your program apart from other data science bootcamps?
Our bootcamp is focused on providing accessibility, practical training and creating pathways to a career as a data scientist.
Accessibility: There are bootcamps in the market that will only accepts students coming out of a PhD program. We fundamentally believe that there is a much bigger population of students that have the skills and the ability to become data scientist. Over our first 2 cohorts we had 44% bachelors, 36% Masters, 17% PhD and 3% with Professional degrees. If you have the skills, we can help you meet your data science goals.
Practical Training: The program is hyper-focused on exposing students to what it takes to design and deliver a data science project. This means teaching people how to think like a data scientist, which starts with how to ask the right questions that will drive business value. Once students know what question they’re trying to answer, only then can they use the many technologies, algorithms and tools that we teach in the bootcamp to find the data, clean the data, analyze the data, and communicate the data. In our bootcamp, students go through this design-thinking process five separate times, creating a portfolio of five distinct projects, all using real data, that they can share with prospective employers. In addition to the project based approach, we also know that Data Scientists do not work alone and that collaboration is key to transitioning into a role as a data scientist. We ensure this is embedded in the 12 week curriculum.
Pathways to a Career: see next question
How does your program prepare a student for life after the bootcamp?
Pathways to a Career: The whole Metis organization focuses on the ultimate outcome, helping students secure a job. In order to provide guidance, Metis has a full time Talent Placement team that works with the students even prior to the start of the program. In parallel with the Bootcamp curriculum the Talent placement team has a career curriculum to help students prepare for their job search. In addition to on going support Metis is very committed to bringing in speakers from the industry to expose students to various roles and companies. Last Metis holds a Career Day at the end of the program to allow for students to present their final projects and to engage with hiring partners. The Metis talent placement team maintains contact with the graduates beyond the program up until the students are placed.