Dataconomy
FEBRUARY 24, 2017
If you are new to the field, Big Data can be intimidating! With the basic concepts under your belt, let’s focus on some key terms to impress your date, your boss, your family, or whoever. Let’s get started: Algorithm: A mathematical formula or statistical process used to perform an analysis of. The post 25 Big Data Terms Everyone Should Know appeared first on Dataconomy.
Eugene Yan
FEBRUARY 18, 2017
If things are not failing, you're not innovating enough.
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Ines Montani
FEBRUARY 8, 2017
I’ve seen a couple of these posts pop up over the past year or so, and I’ve always enjoyed reading other people’s stories. So here’s mine. We got our first computer in the late 1990s. A guy my dad knew from work was really into computers, still a novelty at the time, and he offered to get us one and set it up. So my parents thought, why not? Looking back, I often think about how my life would have turned out if this hadn’t happened.
Explosion
FEBRUARY 28, 2017
Supervised models for text-pair classification let you create software that assigns a label to two texts, based on some relationship between them. When the relationship is symmetric, it can be useful to incorporate this constraint into the model. This post shows how a siamese convolutional neural network performs on two duplicate question data sets with experimental results.
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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?
Dataconomy
FEBRUARY 15, 2017
In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I’ve observed that some actually lack the necessary mathematical intuition and. The post The Mathematics of Machine Learning appeared first on Dataconomy.
Dataconomy
FEBRUARY 6, 2017
Data-driven businesses are five times more likely to make faster decisions than their market peers, and twice as likely to land in the top quartile of financial performance within their industries. Business Intelligence, previously known as data mining combined with analytical processing and reporting, is changing how organizations move forward. The post 6 Ways Business Intelligence is Going to Change in 2017 appeared first on Dataconomy.
Data Science Current brings together the best content for data science professionals from the widest variety of thought leaders.
Dataconomy
FEBRUARY 9, 2017
There’s a part of data science that you rarely hear about: the deployment and production of data flows. Everybody talks about how to build models, but little time is spent discussing the difficulties of actually using those models. Yet these production issues are the reason many companies fail to see. The post Infographic: The 4 Types of Data Science Problems Companies Face appeared first on Dataconomy.
Dataconomy
FEBRUARY 1, 2017
Ajit Jaokar is a leading expert working at the intersection of Data Science, IoT, AI, Machine Learning, Big Data, Mobile, and Smart Cities. He teaches IoT and Data Science at Oxford and also is a director of Smart Cities Lab in Madrid. Ajit’s work involves applying machine learning techniques to. The post How to transform your business with Artificial Intelligence appeared first on Dataconomy.
Dataconomy
FEBRUARY 13, 2017
Data Science Platforms: Myth v. Reality The phrase “data science platform” has been bandied about a lot recently — at conferences, in market research, and in tech publications like this one. Forrester named data science platforms a top emerging technology last year, and companies using data science at an enterprise. The post The Next Tech Wave: Why Businesses Use Data Science Platforms appeared first on Dataconomy.
Dataconomy
FEBRUARY 20, 2017
Given the enormous amount of Business Intelligence software solutions available, narrowing down the right one for your business can be a tedious process. How does a business start implementing this software? One way to start is by looking at systems that are popular among peers, because those products are the. The post The 20 Most Popular Business Intelligence Tools appeared first on Dataconomy.
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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.
Dataconomy
FEBRUARY 1, 2017
Financial data finally starting to pay off If you are in Finance, you would have read at least one of the many predictions articles that poured from all directions on the internet in the past month. This is not trying to be yet another one but focus on the CX. The post 2017 – The Year Data Made Bank? appeared first on Dataconomy.
Dataconomy
FEBRUARY 22, 2017
The Value of Data Goes Beyond Any Number A quick look around the 21st century marketplace reveals a simple truth: the value of data has changed. Industries that once stood alone and operated in silos, have become interconnected by sheer necessity – collecting, analyzing, sharing and even selling data. Thus, The post The Power of a Data Value Chain For Your Business appeared first on Dataconomy.
Dataconomy
FEBRUARY 10, 2017
2017 is set to be a success for the IoT industry, as the number of connected things grows at soaring speeds. The time has come for businesses, consultancies, and entrepreneurs to tap into this opportunity, if they want to stay in the vanguard. Of the projected 8.4 billion IoT-enabled devices. The post The Internet of Things Entrepreneur Checklist – a guide for the budding IoT mogul appeared first on Dataconomy.
Dataconomy
FEBRUARY 10, 2017
Convince Your Boss! 5 Reasons to Attend the IoT Weekend You really want to come to our IoT workshop but you are not sure how to convince your boss to pay your ticket? Say no more. We’ve prepared some pretty good reasons for you (not that you do not know. The post Convince Your Boss! 5 Reasons to Attend the IoT Weekend appeared first on Dataconomy.
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.
Dataconomy
FEBRUARY 3, 2017
Six Common Streaming Misconceptions Needless to say, we here at data Artisans spend a lot of time thinking about stream processing. Even cooler: we spend a lot of time helping others think about stream processing and how to apply streaming to data problems in their organizations. A good first step. The post Stream Processing Myths Debunked appeared first on Dataconomy.
Eugene Yan
FEBRUARY 12, 2017
Or how to put machine learning models into production.
Dataconomy
FEBRUARY 10, 2017
There is no excerpt because this is a protected post. The post Protected: appeared first on Dataconomy.
Explosion
FEBRUARY 12, 2017
Quora recently released the first dataset from their platform: a set of 400,000 question pairs, with annotations indicating whether the questions request the same information. This data set is large, real, and relevant — a rare combination. In this post, I'll explain how to solve text-pair tasks with deep learning, using both new and established tips and technologies.
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.
Dataconomy
FEBRUARY 27, 2017
Researchers have estimated that 25 years ago, around 100GB of data was generated every day. By 1997, we were generating 100GB every hour and by 2002 the same amount of data was generated in a second. We’re on trajectory – by 2018 – to generate 50TB of data every single. The post The 3 Reasons Why Companies Should Use Data Intensive Computing appeared first on Dataconomy.
Explosion
FEBRUARY 28, 2017
Supervised models for text-pair classification let you create software that assigns a label to two texts, based on some relationship between them. When the relationship is symmetric, it can be useful to incorporate this constraint into the model. This post shows how a siamese convolutional neural network performs on two duplicate question data sets.
Explosion
FEBRUARY 12, 2017
Quora recently released the first dataset from their platform: a set of 400,000 question pairs, with annotations indicating whether the questions request the same information. This data set is large, real, and relevant — a rare combination. In this post, I’ll explain how to solve text-pair tasks with deep learning, using both new and established tips and technologies.
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