data mining


Data Science or Machine Learning? Here's How to Spot the Difference

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In this new world of artificial intelligence and data management, it's easy to get confused by some of the terms that are most commonly used in the IT world. For example, data science and machine learning have a lot to do with each other. It's not surprising that many people with only a passing knowledge of these disciplines would have trouble figuring out how they differ from one another. Join nearly 200,000 subscribers who receive actionable tech insights from Techopedia. First of all, data science is really a broad, overarching category of technology that encompasses many different types of projects and creations.


5 cheap online courses that could help you land a job in AI

Mashable

These days, pundits galore are proselytizing about the Future of Work. Depending on who you ask, the robots may or may not be taking over, leaving us mere humans pondering how work fits into our lives and whether we're going to be eventually rendered obsolete. Just look at the stark contrast in tone between these two headlines: the Wall Street Journal's White-Collar Robots Are Coming For Jobs versus Wired's Chill: Robots Won't Take All Our Jobs. Who should we *really* believe?! The truth is there isn't one easy answer.


Nvidia GPUs for data science, analytics, and distributed machine learning using Python with Dask

ZDNet

Nvidia has been more than a hardware company for a long time. As its GPUs are broadly used to run machine learning workloads, machine learning has become a key priority for Nvidia. In its GTC event this week, Nvidia made a number of related points, aiming to build on machine learning and extend to data science and analytics. Nvidia wants to "couple software and hardware to deliver the advances in computing power needed to transform data into insights and intelligence." Jensen Huang, Nvidia CEO, emphasized the collaborative aspect between chip architecture, systems, algorithms and applications.


Women in Data Science conference unites global community of researchers and practitioners

MIT News

The MIT Institute for Data, Systems, and Society (IDSS) convened professional data scientists, academic researchers, and students from a variety of disciplines for the third annual daylong Women in Data Science (WiDS) conference in Cambridge. WiDS Cambridge is one of many global satellite events of the WiDS conference at Stanford University, where attendees join a global community of data science researchers and practitioners. The conference is open to anyone interested in data science, but strives especially to create opportunities for women in the field to showcase their work and network with each other. "I think WiDS is a great opportunity to bring together women at all professional levels -- students, postdocs, faculty, and professionals in industry -- who are working in data science, building community, and learning from a wide variety of perspectives," said Stefanie Jegelka, an IDSS affiliate faculty member with the Department of Electrical Engineering and Computer Science (EECS). Jegelka is an MIT WiDS planning committee member who also gave a talk exploring the properties of neural networks, focusing on ResNet architecture and neural networks for graphs.


Machine learning identifies links between world's oceans

MIT News

Oceanographers studying the physics of the global ocean have long found themselves facing a conundrum: Fluid dynamical balances can vary greatly from point to point, rendering it difficult to make global generalizations. Factors like the wind, local topography, and meteorological exchanges make it difficult to compare one area to another. To add to the complexity, one would have to analyze billions of data points for numerous parameters -- temperature, salinity, velocity, how things change with depth, whether there is a trend present -- to pinpoint what physics are most dominant in a given region. "You would have to look at an overwhelming number of different global maps and mentally match them up to figure out what matters most where," says Maike Sonnewald, a postdoc working in the MIT Department of Earth, Atmospheric and Planetary Sciences (EAPS) and a member of the EAPS Program in Atmospheres, Oceans and Climate (PAOC). Sonnewald, who has a background in physical oceanography and data science, uses computers to reveal connections and patterns in the ocean that would otherwise be beyond human capability.


This AI predicts online trolling before it happens

Mashable

How do you keep online trolls in check? Dr. Srijan Kumar, a post-doctoral research fellow in computer science at Stanford University, is developing an AI that predicts online conflict. His research uses data science and machine learning to promote healthy online interactions and curb deception, misbehavior, and disinformation. His work is currently deployed inside Indian e-commerce platform Flipkart, which uses it to spot fake reviewers. We spoke to Dr. Kumar ahead of a lecture on healthy online interactions at USC.


How to use process data mining to improve DevOps

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DevOps combines the information technology and software development teams and increases communication and collaboration between the two groups. With DevOps, then, it becomes possible to adopt an approach to project management that allows for shorter times between new versions of apps or other products. As such, DevOps encourages continual evolution brought about by team or client needs and feedback. Something called process data mining -- analysing large amounts of data about processes and taking action accordingly -- could enhance DevOps practices in several ways. Data mining involves looking through collections of information and identifying patterns.


How To Work In Data Science, AI, Big Data

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In summer 2013, I interviewed for a lead role in the data science and analytics team at tech-for-good company JustGiving. During the interview, I said I planned to deliver batch machine learning, graph analytics and streaming analytics systems, both in-house and in the cloud. A few years later, my former boss Mike Bugembe and I were both presenting at international conferences, winning awards and becoming authors! Here is my story, and what I learnt on the journey -- plus my recommendations for you. I've always been interested in artificial intelligence (AI), machine learning (ML) and natural language processing (NLP).


SAS to invest $1 billion in AI for industry uses, education, R&D

ZDNet

SAS in recent years "hasn't been as visible as it could have been," said Schabenberger. But the company has been making pivots to software as a service, connecting its platform to other analytics tools and targeting industries better. SAS has been focused on "how our offering can bring analytics to areas undiscovered," he added SAS has also been focused on targeting a wide range of companies beyond large enterprises and making its offering more consumable. The company is entering a results as a service model where customers come with business problems and SAS can help solve them with its expertise in analytics, machine learning and data science. Most of the company's customers are on-premises, but migrating to cloud workloads at different paces, said Schabenberger.


Unicorns, Reproducibility, and other Machine Learning Myths ActiveState

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But if you judge a fish by Its ability to climb a tree, it will live Its whole life believing that it is stupid." While commonly attributed to Einstein the above quote is likely apocryphal. Nonetheless, it's instructive in pointing out that fact that, despite obvious misalignment, we in the tech industry often persevere in trying to make square pegs fit into round holes. Recently, nowhere is this more endemic than in Machine Learning initiatives. Two of the most common "fish climbing a tree" scenarios include "data science unicorns" and "data-driven hypotheses."