Dataconomy
MAY 8, 2017
Everyone has heard the old moniker garbage in – garbage out. It is a simple way of saying that machine learning is only as good as the data, algorithms, and human experience that goes into them. But even the best results can be thought of as garbage if no one. The post Big Data for Humans: The Importance of Data Visualization appeared first on Dataconomy.
Eugene Yan
MAY 14, 2017
OMSCS CS6476 Computer Vision - Performing computer vision tasks with ONLY numpy.
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Explosion
MAY 9, 2017
As more and more people and companies are getting involved with open-source software, balancing the expectations of an open community and a traditional provider vs. consumer relationship is becoming increasingly difficult. Are maintainers becoming too authoritarian? Are users becoming too demanding? Are large companies selling out open-source?
Ines Montani
MAY 9, 2017
As more and more people and companies are getting involved with open-source software, balancing the expectations of an open community and a traditional provider vs. consumer relationship is becoming increasingly difficult. Are maintainers becoming too authoritarian? Are users becoming too demanding? Are large companies selling out open-source? In this post I’ll share some lessons we’ve learned from running spaCy , the popular and fast-growing library for Natural Language Processing in Python.
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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
MAY 1, 2017
As buzzwords become ubiquitous they become easier to tune out. We’ve finely honed this defense mechanism, for good purpose. It’s better to focus on what’s in front of us than the flavor of the week. CRISPR might change our lives, but knowing how it works doesn’t help you. VR could. The post The Business Implications of Machine Learning appeared first on Dataconomy.
Dataconomy
MAY 3, 2017
Bitcoin is currently trading at over $1250 and if you are someone who invested a grand in bitcoins back in 2011, your investments are potentially worth over $600K. The most valuable contribution of the bitcoin community is not in the financial returns itself, but in the introduction of blockchain technology. The post Blockchains could be every Data Scientist’s dream appeared first on Dataconomy.
Data Science Current brings together the best content for data science professionals from the widest variety of thought leaders.
Dataconomy
MAY 15, 2017
The late data visionary Hans Rosling mesmerised the world with his work, contributing to a more informed society. Rosling used global health data to paint a stunning picture of how our world is a better place now than it was in the past, bringing hope through data. Now more than. The post Confused by data visualization? Here’s how to cope in a world of many features appeared first on Dataconomy.
Dataconomy
MAY 10, 2017
The rise of the data scientists continues and social media is filled with success stories – but what about those who fail? There are no cover articles praising the failures of the many data scientists that don’t live up to the hype and don’t meet the needs of their stakeholders. The post Three Mistakes that Set Data Scientists up for Failure appeared first on Dataconomy.
Dataconomy
MAY 12, 2017
The digital age is characterised increasingly by the collective. The centralised database is being superseded by the blockchain; expert opinion yields ever more to the insights of the crowd. The information generated by tapping into the minds of many is driving decisions in both the public and private sector; market. The post Data Mining for Social Intelligence – Opinion data as a monetizable resource appeared first on Dataconomy.
Dataconomy
MAY 24, 2017
Welcome to Part 2 of How to use Elasticsearch for Natural Language Processing and Text Mining. It’s been some time since Part 1, so you might want to brush up on the basics before getting started. This time we’ll focus on one very important type of query for Text Mining. The post How to use ElasticSearch for Natural Language Processing and Text Mining — Part 2 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
MAY 30, 2017
R is ubiquitous in the machine learning community. Its ecosystem of more than 8,000 packages makes it the Swiss Army knife of modeling applications. Similarly, Apache Spark has rapidly become the big data platform of choice for data scientists. Its ability to perform calculations relatively quickly (due to features like in-memory. The post Machine Learning using Spark and R appeared first on Dataconomy.
Dataconomy
MAY 29, 2017
As the creation and consumption of data continues to grow among businesses of all sizes, so does the challenge of analyzing and turning that data into actionable insights. According to IBM, 90 percent of the data in the world today has been created in the last two years, at 2.5. The post Data Nirvana – How to develop a data-driven culture appeared first on Dataconomy.
Dataconomy
MAY 5, 2017
The Estimators API in tf.contrib.learn (See tutorial here) is a very convenient way to get started using TensorFlow. The really cool thing from my perspective about the Estimators API is that using it is a very easy way to create distributed TensorFlow models. Many of the TensorFlow samples that you. The post How to do time series prediction using RNNs, TensorFlow and Cloud ML Engine appeared first on Dataconomy.
Dataconomy
MAY 31, 2017
During my years as a Consultant Data Scientist I have received many requests from my clients to provide a frequency distribution reports for their specific business data needs. These reports have been very useful for the company management to make proper business decisions quickly. In this paper I would like. The post Frequency Distribution Analysis using Python Data Stack – Part 1 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
MAY 23, 2017
The R language is often perceived as a language for statisticians and data scientists. Quite a long time ago, this was mostly true. However, over the years the flexibility R provides via packages has made R into a more general purpose language. R was open sourced in 1995, and since. The post Boost Your Data Wrangling with R appeared first on Dataconomy.
Dataconomy
MAY 17, 2017
The march to the cloud for mission-critical applications is picking up speed. Even financial services firms, noted for their caution, are making headway. UK-based insurance intermediaryTowergate Insurance announced last year that it is moving its IT infrastructure to the cloud. And The Wall Street Journal reported in June 2016 that. The post Dare to Share in The Cloud: How Secure Is Your Data?
Dataconomy
MAY 26, 2017
Data processing today is done in form of pipelines which include various steps like aggregation, sanitization, filtering and finally generating insights by applying various statistical models. Amazon Kinesis is a platform to build pipelines for streaming data at the scale of terabytes per hour. Parts of the Kinesis platform are. The post Amazon Kinesis vs.
Dataconomy
MAY 22, 2017
For people in the know, machine learning is old hat. Even so, it’s set to become the data buzzword of the year — for a rather mundane reason. When things get complex, people expect technology to ‘automagically’ solve the problem. Whether it’s automated financial product consultation or shopping in the supermarket of. The post Keep it real — say no to algorithm porn!
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.
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