It is sometimes said that you don't need to know math to be a data scientist. Sometimes the opposite is said, after all, data science is supposed to be a science! Regardless, below are a few of my articles featuring how data science and math can benefit from each other - not just math to solve data science problems, but also data science to solve math problems.
In the previous post, ten strategies to implement ai on the cloud and edge, I discussed strategies for end to end deployment for machine learning modules.
How this relates to Agile?
Deployment of AI comes within the scope the normal SDLC (software development lifecycle)
So, normal Agile techniques like scrum, sprints, backlog…Continue
Added by ajit jaokar on January 20, 2020 at 1:06pm — No Comments
Summary: Just how much should you trust your AI systems? Best practice points to constant review, strong governance, and the willingness to override results that seem illogical.
Added by William Vorhies on January 20, 2020 at 8:26am — No Comments
Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this link. …Continue
Added by Vincent Granville on January 19, 2020 at 4:30pm — No Comments
Having spent the better part of the last decade helping organisations in the U.S. and the U.K. use data to drive profit, efficiency, and performance improvements, I thought it would be helpful to jot down best practices for organizations that are looking to get the most value out of their enterprise-level financial and operational data sets.
Every organisation entering the roaring 20s knows that they must use their data as a strategic differentiator to gain a competitive advantage and…Continue
Added by Fahad Zaidi on January 19, 2020 at 6:00am — No Comments
In my previous post, I discussed the differences between Business Intelligence and Business Analytics. Two other terms that are often confused are Business Analytics and Data Analytics, but they are actually quite separate entities. This one picture highlights the differences between the two…Continue
Added by Stephanie Glen on January 18, 2020 at 7:12am — No Comments
Imagine this scenario:
You enter your dentist’s office as a follow-up visit to the 6-month checkup you had last week. You’re nervous because the checkup revealed bleeding in your gums and cracks in your fillings, all things that require your dentist’s repair on this occasion.
So, you’re there nervously fidgeting in the lobby waiting for your name to be called, envisioning all sorts of dentistry implements of torture that will soon be…Continue
Added by Bill Schmarzo on January 17, 2020 at 12:30pm — No Comments
In 2020, HandWiki has become the largest online wiki encyclopedia for major science topics (physics, math etc.) and computing. It has more than 105,000 scholarly articles, incorporating the current Wikipedia articles, scholarly articles submitted to the Wikipedia foundation (but later…Continue
Added by jwork.ORG on January 16, 2020 at 6:30pm — No Comments
Quantum computers come in 2 varieties, quantum annealers and quantum gate models. So far, DWave is the only company selling quantum annealers.
A third type of device is now available from Fujitsu. Fujitsu calls its device a "quantum inspired digital annealer". The Fujitsu device does not have any quantum correlation (quantum entanglement)…Continue
Added by Robert R. Tucci on January 16, 2020 at 1:30pm — No Comments
The deployment of Machine Learning and Deep Learning algorithms on Edge devices is a complex undertaking. In this post, I list the strategies for deploying AI to Edge devices end-to-end i.e. for the full pipeline covering machine learning (building modules) and deployment (devops)
I welcome your comments on additional ideas that could be included. In subsequent posts, I will elaborate these ideas in detail and…Continue
Added by ajit jaokar on January 16, 2020 at 11:30am — No Comments
Here is our selection of featured articles and technical resources posted since Monday:
Added by Vincent Granville on January 16, 2020 at 11:00am — No Comments
Data Science methods and techniques allow new approaching the solution of complex tasks in terms of mathematics, and statistics for the various aspects and areas of our life, work, and business. Therefore, this makes it possible to produce the most unobvious…Continue
Added by Igor Bobriakov on January 16, 2020 at 10:30am — No Comments
Machine Learning (ML) development is an iterative process in which the accuracy of predictions made by the models is continuously improved by repeating the training and evaluation phases. In each of these iterations, certain parameters are tweaked continuously by developers. Any parameter manually selected based on learning from previous experiments qualify to be called a model hyper-parameter. These parameters represent intuitive decisions whose value cannot be estimated from data or from…Continue
This is a major contribution of analytics and big data when you talk about the success of mobile app development. Big Data is large data sets that can be analyzed to know more about the user’s interests, demographics, trends and interactions. We rely on mobile apps present on our phone for most of the tasks like a reminder, planner, health and fitness, etc.…Continue
Added by Veronica Hanks on January 15, 2020 at 8:43pm — No Comments
Hello! This is my first post on Data Science Central, so please be patient. I've had a short Q&A session with our CTO Sergey Nemesh.
Q: What problems can Blockchain solve for the FinTech sector?
A: The main global problem in the FinTech sector is trust providing as for financial operations it plays a crucial role. Blockchain provides trustless transactions which…Continue
Added by Valery Geldash on January 15, 2020 at 10:07am — No Comments
Summary: This blog details R data.table programming to handle multi-gigabyte data. It shows how the data can be efficiently loaded, "normalized", and counted. Readers can readily copy and enhance the code below for their own analytic needs. An intermediate level of R coding sophistication is assumed.
In my travels over the holidays, I…Continue
Added by steve miller on January 15, 2020 at 5:29am — No Comments
To get a defining advantage over the competitors the banks, as well as other financial institutions must dig deep into technology breakthroughs. Luckily, Artificial Intelligence and Machine Learning are already here to help them to achieve it. Improving data processing will lead to better strategies and fraud prevention levels.…Continue
Added by Roman Chuprina on January 15, 2020 at 4:00am — No Comments
Software is always not only a set of instructions but also a context that manages, interacts and executes these instructions. At the start of developing, the engineer configures a dev environment. He can continue to change it in all stages of development. The problem appears when…Continue
Added by Igor Bobriakov on January 14, 2020 at 6:00pm — No Comments
While many of the programming libraries encapsulate the inner working details of graph and other algorithms, as a data scientist it helps a lot having a reasonably good familiarity of such details. A solid understanding of the intuition behind such algorithms not only helps in appreciating the logic behind them but also helps in making conscious decisions about their applicability in real life cases. There are several graph based algorithms and most notable are the shortest path…Continue
Added by Murali Kashaboina on January 14, 2020 at 3:00pm — No Comments
Machine Learning is a term heard around the world these days. Industries like Retail, Healthcare, and Manufacturing are taking the best out of it. So does the leaders in Banking and Finance. There is enough time and room before the technology will truly explode. However, we can still talk about some real-world use cases and ways your business can…Continue
Added by Roman Chuprina on January 14, 2020 at 5:00am — No Comments