Analytics Vidhya
JULY 25, 2019
Overview Curious how the big product companies like Amazon, Walmart, AirBnb, etc. manage the ratings we see? The core idea behind these ratings systems. The post Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! appeared first on Analytics Vidhya.
KDnuggets
JULY 25, 2019
As long as there is ‘data’ in data scientist, Structured Query Language (or see-quel as we call it) will remain an important part of it. In this blog, let us explore data science and its relationship with SQL.
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DataRobot
JULY 25, 2019
In our first blog post on the topic of AI Ethics, we covered the promise that artificial intelligence (AI) holds to improve the speed, accuracy, and operations of businesses across a range of industries. With the potential of AI, it’s hard to believe that businesses are hesitating to move forward with AI projects, but fear holds people back. They fear making mistakes that could damage their company’s reputation or doing something illegal or unethical.
KDnuggets
JULY 25, 2019
Here are the top certificates and certifications in Analytics, AI, Data Science, Machine Learning and related areas.
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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.
Smart Data Collective
JULY 25, 2019
Not long ago, big data was one of the most talked about tech trends , as was artificial intelligence (AI). But, in case people need a reminder of how fast technology evolves , they only need to consider something newer — big data AI. It combines elements of both technologies. AI allows computers to perform cognitive functions, much like the human brain.
KDnuggets
JULY 25, 2019
Find out how to use randomness to learn your data by using Noise Contrastive Estimation with this guide that works through the particulars of its implementation.
Data Leaders Brief brings together the best content for data, strategy, and BI professionals from the widest variety of industry thought leaders.
IBM Big Data Hub
JULY 25, 2019
It’s no surprise: most companies working with stream data today say they are planning to make changes to drive greater value. Advancements in machine learning (ML) and very-high-speed data persistence for real-time analytics are reshaping strategies and architectures.
KDnuggets
JULY 25, 2019
ODSC focuses on research at its conferences and invites the experts pushing the boundaries of AI to speak. Between the two upcoming conferences, researchers from more than 20 of the top research institutes in the country (Open AI, NASA’s JPL, Google, MIT CSAIL, BAIR, The Turing Institute, and Max Planck and more) will deliver talks and lead trainings at ODSC West 2019.
Insight
JULY 25, 2019
A practical and effective approach to reducing bias in machine learning. Continue reading on Insight Fellows Program ».
KDnuggets
JULY 25, 2019
Analyst firm Cognilytica estimates that as much as 80% of machine learning project time is spent on aggregating, cleaning, labeling, and augmenting machine learning model data. So, how do innovative machine learning teams prepare data in such a way that they can trust its quality, cost of preparation, and the speed with which it’s delivered?
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.
Peter James Thomas
JULY 25, 2019
Similar to its predecessor, Why are so many businesses still doing a poor job of managing data in 2019? this brief article has its genesis in the question that appears in its title, something that I was asked to opine on recently. Here is an expanded version of what I wrote in reply: Well the first part of the answer is based on consideing activities which have at least moderate difficulty and complexity associated with them.
KDnuggets
JULY 25, 2019
Responsible for operational leadership and management of the Master of Science in Business Analytics programs. Serves as a thought partner with the program Associate Dean to develop and execute program strategy.
Smart Data Collective
JULY 25, 2019
The healthcare industry is happily embracing big data. Hospitals around the world are finding that data can have a profound impact on their operations. A lot of the emphasis so far has been on the use of big data to better engage with external third-parties, but big data can be equally valuable for managing internal hospital systems. Big Data is the Key to Improving the Efficiency of Hospital Management Systems?
Jedox
JULY 25, 2019
Budgeting, Planning & Forecasting using Excel remains one of the most commonly used methods by FP&A professionals. As we mentioned in our first blog post on Planning in Excel , 80% of organizations reported in a recent survey* that they are still reliant on the tool. The demands on the Office of Finance to increase value across the organization continue to grow due to the impact of digital transformation.
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.
Smart Data Collective
JULY 25, 2019
In the early stages of basketball, there was very little use for data and analytics. Sports, in general, were largely anti-data science simply because it didn’t seem like it could apply to a game. Fast forward to the present day, and data science and data analytics are being used in virtually every single sport. The NBA might not rely on analytics quite as much as the MLB , but all 30 teams are heavily investing more and more money into this side of their business.
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