Analytics Vidhya
MARCH 7, 2019
This article has been updated on Women’s Day, 2019. Introduction This women’s day, we at Analytics Vidhya are celebrating the power of women in. The post 29 Inspiring Women Blazing a Trail in the Data Science World appeared first on Analytics Vidhya.
O'Reilly on Data
MARCH 7, 2019
NLP systems in health care are hard—they require broad general and medical knowledge, must handle a large variety of inputs, and need to understand context. We’re in an exciting decade for natural language processing (NLP). Computers will get as good as humans in complex tasks like reading comprehension, language translation, and creative writing.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
David Menninger's Analyst Perspectives
MARCH 8, 2019
I am happy to share some insight on Domo drawn from our latest Value Index research, which provides an analytic representation of our assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Analytics and Business Intelligence 2019 is the distillation of a year of market and product research efforts by Ventana Research.
IBM Big Data Hub
MARCH 7, 2019
Success with AI models depends on achieving success with collecting and organizing your data, then analyzing the data to make smarter business decisions.
Speaker: John Mansour
If you're looking to advance your career in product management, there are more options than just climbing the management ladder. Join our upcoming webinar to learn about highly rewarding career paths that don't involve management responsibilities. We'll cover both career tracks and provide tips on how to position yourself for success in the one that's right for you.
Analytics Vidhya
MARCH 5, 2019
Introduction The rapid rise of data science as a professional field has lured in people from all backgrounds. Engineers, computer scientists, marketing and finance. The post 11 Steps to Transition into Data Science (for Reporting / MIS / BI Professionals) appeared first on Analytics Vidhya.
Data Science 101
MARCH 7, 2019
Read to the end to learn more about a new study group I will be launching. In Late January 2019, Microsoft launched 3 new certifications aimed at Data Scientists/Engineers. For a while, Microsoft has been toying with different methods for training and credentials. They launched the Microsoft Professional Program in Data Science back in 2017. While it provides great content, it did not result in either a college diploma or an official Microsoft certification.
Data Leaders Brief brings together the best content for data, strategy, and BI professionals from the widest variety of industry thought leaders.
Sisense
MARCH 7, 2019
Despite progress in recent years, UNESCO says that more girls than boys remain out of school. In fact, according to the UNESCO Institute for Statistics , “16 million girls will never set foot in a classroom – and women account for two-thirds of the 750 million adults without basic literacy skills.”. There’s a widely-known African proverb that says “If you educate a man, you educate an individual.
Analytics Vidhya
MARCH 6, 2019
Introduction Machine learning is disrupting multiple and diverse industries right now. One of the biggest industries to be impacted – finance. Functions like fraud. The post Hands-On Introduction to creditR: An Amazing R Package to Enhance Credit Risk Scoring and Validation appeared first on Analytics Vidhya.
MLWhiz
MARCH 8, 2019
This post is the third post of the NLP Text classification series. To give you a recap, I started up with an NLP text classification competition on Kaggle called Quora Question insincerity challenge. So I thought to share the knowledge via a series of blog posts on text classification. The first post talked about the different preprocessing techniques that work with Deep learning models and increasing embeddings coverage.
IBM Big Data Hub
MARCH 8, 2019
The IBM Hybrid Data Management Platform provides simplicity and control in one package for your hybrid architecture needs or journey to the cloud.
Advertisement
Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?
Data Science 101
MARCH 6, 2019
University can be a great way to learn data science. However, many universities are very expensive, difficult to get admitted, or not geographically feasible. Luckily, a few of them are willing to share data science, machine learning and deep learning materials online for everyone. Here is just I small list I have come across lately. MIT Deep Learning – Lecture notes, slides and guest talks about deep learning and self driving cars Introduction to Artificial Intelligence from UC Berkeley &
Analytics Vidhya
MARCH 6, 2019
Introduction “People underestimate how complex intelligence is.” How close are we to Artificial General Intelligence (AGI)? It seems we take a step closer to. The post DataHack Radio #19: The Path to Artificial General Intelligence with Professor Melanie Mitchell appeared first on Analytics Vidhya.
John Wheeler
MARCH 6, 2019
It has been 5 years since Gartner embarked on the journey to enhance our coverage of the risk management technology marketplace. That journey included in-depth survey research and countless interactions with our end-user clients to understand their need to better manage strategic, operational and IT/cybersecurity risks. These end-user needs and resulting demand led to the definition of a new technology marketplace – integrated risk management (IRM).
IBM Big Data Hub
MARCH 6, 2019
Leveraging IBM cloud and partnered with IBM leaders, Sonoma County transformed with data and Watson AI.
Advertisement
Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.
Sisense
MARCH 6, 2019
Every year there’s high anticipation to see what key message Gartner will present in the yearly Data & Analytics Summits. The BI industry takes Gartner’s perspective very seriously, and year after year, it’s very common to see messages that were first described in the Gartner summits making their way into the websites of many analytics vendors.
Data Science 101
MARCH 2, 2019
A new community for data visualization professionals has launched. It is called the Data Visualization Society. According to the website, The Data Visualization Society aims to collect and establish best practices and foster community to support its members as they develop their data visualization skills. Currently, membership is free and they are looking for help growing the community.
Jet Global
MARCH 5, 2019
With all the attention being paid to artificial intelligence (AI) these days, it’s no surprise that enterprise leaders are scrambling to find ways to shoehorn AI implementations into their technology stack. But when you ask leaders in the enterprise to define what they’re looking for from AI, their answers frequently focus on solutions that will empower better business decision making.
DataRobot
MARCH 5, 2019
In a previous blog post , we introduced you to Zach Deane-Mayer, a data scientist who runs our core modeling team. One of the most important tools in his team’s arsenal is a data science performance evaluation system created and maintained by our QA team. This system is at the core of our comprehensive testing philosophy that we believe is crucial to delivering a platform that our customers can trust, no matter what DataRobot features they’re using or how they’ve chosen to deploy them.
Speaker: Timothy Chan, PhD., Head of Data Science
Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.
Insight
MARCH 5, 2019
Ad recommendations should be understandable to the individual consumer, but is it possible to increase interpretability without sacrificing accuracy? Internet of Thing (AWS IoT) Are you looking to transition into the field of machine learning in Silicon Valley, New York, or Toronto? Apply for the upcoming June session today ( Deadline is March 25th for SV and NYC ) or learn more about the Artificial Intelligence program at Insight!
Data Science 101
MARCH 4, 2019
Brandon Rohrer (along with others ) created an excellent resource for academic programs, Industry recommendations for academic data science programs. The resource is authored by a number of industry data scientists and university faculty. It is collection of useful information for college data science programs. Here are some of the topics: What do Industry data scientists do?
Sisense
MARCH 4, 2019
The world of data is now the world of Big Data. The genie is out of the bottle and there’s no going back. We produce more and more data every day and the datasets being generated are getting more and more complex. Traditionally, the way to handle this has been to scale up computing resources to handle these bigger datasets. That’s not feasible for the long term on a global scale, nor is it tenable in the near term for smaller organizations who may have limited resources to deal with their analyt
Decision Management Solutions
MARCH 4, 2019
Machine learning is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on models and inference instead. It is seen as a subset of artificial intelligence. — Wikipedia. Machine Learning is increasingly widely used to make predictions.
Speaker: Scott Sehlhorst
We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.
DataRobot
MARCH 4, 2019
Can You Trust That Your AI Was Built Correctly? It has been more than three years since the last time I competed in a data science competition, and yet there’s one memory about that competition that remains vivid in my mind. I had spent a busy week at my computer coding up a cool-looking solution, and I was ready to submit my first entry to the competition.
bridgei2i
MARCH 8, 2019
If we are not actively engaged in industries related to technology, we may fail to fully appreciate how we might already be influenced by artificial intelligence in our day-to-day world. Everyone is talking about self-driving cars, seemingly inanimate objects conversing with you about your personal preferences, someone somewhere already seems to recommend your shopping list armed with the knowledge of what you like or dislike.
erwin
MARCH 8, 2019
For regulatory compliance (e.g., GDPR) and to ensure peak business performance, organizations often bring consultants on board to help take stock of their data assets. This sort of data governance “stock check” is important but can be arduous without the right approach and technology. That’s where data governance comes in …. While most companies hold the lion’s share of operational data within relational databases, it also can live in many other places and various other formats.
Sisense
MARCH 7, 2019
By now you probably already know that data and analytics are a must-have for every and all parts of an organization. Although it might feel counterintuitive at first, HR is no exception. In fact, these days HR is taking an even more important role in driving strategic transformation across organizations. However, in order to make a strategic impact, HR teams need the right data and analytics platform that is easy to use and performs extremely well on large amounts and many sources of data.
Advertisement
Start-ups & SMBs launching products quickly must bundle dashboards, reports, & self-service analytics into apps. Customers expect rapid value from your product (time-to-value), data security, and access to advanced capabilities. Traditional Business Intelligence (BI) tools can provide valuable data analysis capabilities, but they have a barrier to entry that can stop small and midsize businesses from capitalizing on them.
DataRobot
MARCH 6, 2019
The DataRobot AI Partner Program is continuing to evolve and grow, so we’d like to highlight our partners at Ironside , a leading data and analytics consulting firm with two decades of applied experience.
IBM Big Data Hub
MARCH 7, 2019
Bring our unified POV on Data Replication and its impact on driving analytics initiatives to counter Qlik buying Attunity. .
Andrew White
MARCH 3, 2019
Ten reasons why you need to increase your data literacy, or why you should engage with Gartner/and attend a Gartner Data and Analytics Conference: Do you “see” your organization in any of these: We don’t need to leverage more data to make smarter decisions. Our analytics strategy is centered on delivering the best dashboards for our business users.
TDAN
MARCH 5, 2019
It seems like we’re so busy running that we no longer have time to think. We want to be faster and more responsive, but we aren’t even sure what we are trying to achieve. It’s like the person at your office that is always too busy, is working extra-long hours (and makes sure that everybody […].
Speaker: Kevin Kai Wong, President of Emergent Energy Solutions
In today's industrial landscape, the pursuit of sustainable energy optimization and decarbonization has become paramount. Manufacturing corporations across the U.S. are facing the urgent need to align with decarbonization goals while enhancing efficiency and productivity. Unfortunately, the lack of comprehensive energy data poses a significant challenge for manufacturing managers striving to meet their targets.
Expert insights. Personalized for you.
292 
Let's personalize your content