How to Get Into Data Science Without a Degree

Advice from a Data Scientist in the same position

This article is for those who fall into one of the following categories:

  1. You don’t have a post-secondary degree but you’re interested in data science.
  2. You don’t have a STEM-related degree, but you’re interested in data science.
  3. You’re working in a field completely unrelated to data science, but you’re interested in data science.
  4. You’re simply interested in data science and want to learn more about it.

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Previous article on  “How I’d Learn Data Science if I Could Start Over.” 

#data-science

AI has cracked a key mathematical puzzle for understanding our world

Partial differential equations can describe everything from planetary motion to plate tectonics, but they’re notoriously hard to solve.

… But partial differential equations, or PDEs, are also kind of magical. They’re a category of math equations that are really good at describing change over space and time, and thus very handy for describing the physical phenomena in our universe. … The catch is PDEs are notoriously hard to solve.

… Now researchers at Caltech have introduced a new deep-learning technique for solving PDEs that is dramatically more accurate than deep-learning methods developed previously. Read More

#machine-learning, #deep-learning

4 Blockers and 4 Unlockers for successful machine learning projects

How to build reliable and useful machine learning systems

Machine Learning projects are known to fail frequently, according to Gartner 85% of all AI projects fail and even 96% deal with problems. When it comes to new technologies a high degree is normal, but these numbers are alarming. That might be that requirements for machine learning are not met, no value is added or the model did not make it to production for engineering reasons. Often it is possible to identify potential problems beforehand. This article is about early identification of such pitfalls based on my experience in applied machine learning for the last five years. Read More

#strategy

For under $40, you can learn all about Python, machine learning and artificial intelligence

This week in thinking machines news, a Harvard professor and his students have now raised $14 million to create artificial intelligence so smart that even hackers can’t crack it. Meanwhile, reports from the White House suggest the federal government is close to issuing their directives on how agencies should regulate AI going forward.

And if story no. 1 makes you at all dubious about the impact of story no. 2…well, welcome to the amazing world of Python, machine learning and the tech wonders and ethical quandaries of creating human-based artificial life. Read More

#adversarial, #ethics, #python

The creators of South Park have a new weekly deepfake satire show

It’s the first example of a recurring production that will rely on deepfakes as part of its core premise.

The fake news: A new weekly satire show from the creators of South Park is using deepfakes, or AI-synthesized media, to poke fun at some of the most important topics of our time. Called Sassy Justice, the show is hosted by the character Fred Sassy, a reporter for the local news station in Cheyenne, Wyoming, who sports a deepfaked face of president Trump, though a completely different voice, hair style, and persona. Read More

#fake, #vfx

Digital Twin, Virtual Manufacturing, and the Coming Diamond Age

If you have ever had a book self-published through Amazon or similar fulfillment houses, chances are good that the physical book did not exist prior to the order being placed. Instead, that book existed as a PDF file, image files for cover art and author photograph, perhaps with some additional XML-based metadata indicating production instructions, trim, paper specifications, and so forth.

When the order was placed, it was sent to a printer that likely was the length of a bowling alley, where the PDF was converted into a negative and then laser printed onto the continuous paper stock. This was then cut to a precise size that varied minutely from page to page depending upon the binding type, before being collated and glued into the binding.

At the end of the process, a newly printed book dropped onto a rolling platform and from there to a box, where it was potentially wrapped and deposited automatically before the whole box was closed, labeled, and passed to a shipping gurney. Read More

#robotics, #data-science

Deep Generative Design: Integration of Topology Optimization and Generative Models

Deep learning has recently been applied to various research areas of design optimization. This study presents the need and effectiveness of adopting deep learning for generative design (or design exploration) research area. This work proposes an artificial intelligent (AI)-based deep generative design framework that is capable of generating numerous design options which are not only aesthetic but also optimized for engineering performance. The proposed framework integrates topology optimization and generative models (e.g., generative adversarial networks (GANs))in an iterative manner to explore new design options,thus generating a large number of designs starting from limited previous design data. In addition, anomaly detection can evaluate the novelty of generated designs, thus helping designers choose among design options. The 2D wheel design problem is applied as a case study for validation of the proposed framework. The framework manifests better aesthetics, diversity, and robustness of generated designs than previous generative design methods. Read More

#gans

5 Habits of Organizations With Successful AI

Organizations need diverse teams, executive sponsorship and fewer proofs of concept for the strongest AI programs.

Nearly half of CIOs say they now employ AI or intend to within the next 12 months. But how to make AI a core IT competency still eludes most organizations. Boards of directors, CEOs and customers want to use AI to power real improvements in customer and employee experience.

“It might sound counterintuitive, but do as few proofs of concept (POCs) as possible.” Read More

#strategy

Executive’s guide to developing AI at scale

Developing artificial intelligence and analytics applications typically involves different processes, technology, and talent than those for traditional software solutions. Executives who possess a solid understanding of the basics can ensure they’re making the right investments in their tech stacks and teams to build reliable solutions at scale. We’ve created an interactive guide to help. Read More

#strategy

This incredible Google experiment lets you time travel to your hometown 200 years ago

In the 20 years he’d lived in New York, Raimondas Kiveris had seen the city change immensely. “It was a completely different place, a different town,” says Kiveris, a software engineer at Google Research. This got him wondering what his neighborhood looked like even before that—before he’d lived there, before he’d even been born. “There’s really no easy way to find that information in any organized way,” he says. “So I was starting to think, can we somehow enable this kind of virtual time travel?”

Thee years later, his attempt at virtual time travel is taking shape as an open-source map that can show, in both a bird’s-eye view and a pedestrian-level view, the changes that happen to city streetscapes over time. Read More

#big7