In the past few years, much attention has been drawn to the dearth of women and people of color in tech-related fields. A recent article in Forbes noted, “Women hold only about 26% of data jobs in the United States. There are a few reasons for the gender gap: a lack of STEM education for women early on in life, lack of mentorship for women in data science, and human resources rules and regulations not catching up to gender balance policies, to name a few.” Federal civil rights data further demonstrate that “black and Latino high school students are being shortchanged in their access to high-level math and science courses that could prepare them for college” and for careers in fields like data science.
As an education company offering tech-oriented courses at 20 campuses across the world, General Assembly is in a unique position to analyze the current crop of students looking to change the dynamics of the workplace.
Looking at GA data for our part-time programs (which typically reach students who already have jobs and are looking to expand their skill set as they pursue a promotion or a career shift), here’s what we found: While great strides have been made in fields like web development and user experience (UX) design, data science — a relatively newer concentration — still has a ways to go in terms of gender and racial equality.
Twenty-four percent of all NFL games are decided by three-points or less. If that happens this weekend at the 51st Super Bowl, all the glory (or the blame) will fall on Matt Bryant (placekicker, Atlanta Falcons) or Stephen Gostkowski (placekicker, New England Patriots). It seems reasonable to give them the credit, but in this case reason has it wrong. Giving Bryan or Gostkowski the MVP for making a crucial kick is like giving a gambler credit for the roulette wheel landing on red.In American football the team is generally a single unit, but the kicker is a unique position. Quarterbacks are the de facto leaders of the team, but a quarterback is only as good as his offensive line, receivers, and running backs. Unlike baseball or even basketball, measuring the performance of an individual player in football is notoriously difficult. Unless that player is the kicker. In that case, it’s easy. Continue reading →
At first glance, data science seems to be just another business buzzword — something abstract and ill-defined. While data can, in fact, be both of these things, it’s anything but a buzzword. Data science and its applications have been steadily changing the way we do business and live our day-to-day lives — and considering that 90% of all of the world’s data has been created in the past few years, there’s a lot of growth ahead of this exciting field.
While traditional statistics and data analysis have always focused on using data to explain and predict, data science takes this further and uses data to learn — constructing algorithms and programs that collect from various sources and apply hybrids of mathematical and computer science methods to derive deeper insights. Whereas traditional analysis uses structured data sets, data science dares to ask further questions, looking at unstructured “big data” derived from millions of sources as well as nontraditional mediums such as text, video, and images.
So how is this all manifesting in the market? Here, we take a look at three real-world examples of how data science is driving business innovation across a wide range of industries.
It makes perfect sense that this job is both new and popular, since every move you make online is actively creating data somewhere for something. Someone has to make sense of that data and discover trends in the data to see if the data is useful. That is the job of the data scientist. But how does the data scientist go about the job? Here are the three skills and three tools that every data scientist should master.
Big data may be the buzzword of our time. Our uber-connected world is creating more information than ever before, but data on its own is useless. The value comes from data literate professionals distilling all of that information into useful insights. As the demand for data science skills has tripled over the past five years, companies and employees alike are rushing to wrangle the data that surrounds us and use it to our advantage.
Whether you are brand new to working with data or a seasoned data scientist, learning from practitioners will bring you up-to-date on the exciting ways big data is being used to improve our everyday lives.
Here, we’ve handpicked five of our favorite podcasts relating to data science that you can listen to at your desk, on your commute, or during your next road trip.
Launching for the first time in San Francisco and Washington, D.C. on April 11, this full-time Immersive program will equip you with the tools and techniques you need to become a data pro in just 12 weeks.
The Data Journalists handbook defines data literacy as, “the ability to consume for knowledge, produce coherently and think critically about data.” It goes on to say that “data literacy includes statistical literacy but also understanding how to work with large data sets, how they were produced, how to connect various data sets and how to interpret them.”
At General Assembly, we’d like to imagine a world where you don’t need a Ph.D. in Statistics to have a data-informed conversation about your business, your health, or your life in general. Over the past year, we’ve embarked on the journey to build a more data literate world through education offerings that meet the diverse needs of our students.
In building these courses, we’ve sought advice from data scientists, analysts, and hiring managers to determine the critical skills you need to become data literate in today’s workforce. We discovered that it isn’t just a concrete list of skills, but a mindset geared towards data—a way of approaching problems beyond “gut instincts.”
Here, we’ve proposed a few simple questions that will help you start to view the world through the lens of data.
The term “big data” is everywhere these days, and with good reason. More products than ever before are connected to the Internet: phones, music players, DVRs, TVs, watches, video cameras…you name it. Almost every new electronic device created today is connected to the Internet in some way for some purpose.
The result of all those things connected to the Internet is data. Big, big data. What’s that mean for you? Simply put, it means if you can quickly, accurately, and intelligently sift through data and find trends, you are extremely valuable in today’s tech job market. More specifically, here are five job titles that require data analytics expertise to get ahead.
We teamed up with Fast Company to host two of the leading minds in data, Claudia Perlich from Dstillery and Marc Maleh from R/GA, at our campus in New York City. Sarah Lawson, an assistant editor at Fast Company, moderated the discussion as they chatted about their everyday work with data, their favorite parts of the industry, and what it’s really like to work in data.
If you thought the introduction of the commercial Internet changed mass media, take a look at what’s in front of you today. Behind the sites of your favorite newspapers and blogs (yes, even this one), publishers are using data to create better audience experiences. For anyone who has ever considered working with data as part of their career, there are now more opportunities than ever to bring media and data together. Here are some of the most important technologies to have on your radar.