Dataconomy

DECEMBER 7, 2017

Undoubtedly, 2017 has been yet another hype year for machine learning (ML) and artificial intelligence (AI). As ML and AI become increasingly ubiquitous in many industries, so does the proof that advanced analytics significantly improve day-to-day operations and drive more revenue for businesses. Yes, it’s true – enterprises worldwide have. The post Machine Learning & Data Analysts: Seizing the Opportunity in 2018 appeared first on Dataconomy.

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Eugene Yan

AUGUST 12, 2017

OMSCS CS6300 (Software Development Process) - Java and collaboratively developing an Android app.

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Distill

APRIL 4, 2017

We often think of optimization with momentum as a ball rolling down a hill. This isn't wrong, but there is much more to the story.

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Explosion

DECEMBER 17, 2017

In this video, we’ll show you how to use Prodigy to train a phrase recognition system for a new concept. Specifically, we’ll train a model to detect references to drugs, using text from Reddit.

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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?

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DataRobot Blog

NOVEMBER 9, 2017

After attending this year’s HR Tech World in Amsterdam, journalist Phil Wainwright made an interesting observation about a trend amongst product companies. He explained that they’re layering in a superficial layer of artificial intelligence (AI) — e.g., an Alexa skill — into their products just to be able to claim that their product uses AI. He calls this trend “Machine Washing.”.

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Ines Montani

AUGUST 4, 2017

I’m excited and proud to finally share what we’ve been working on since launching Explosion AI , alongside our NLP library spaCy and our consulting projects. Prodigy is a project very dear to my heart and seeing it come to life has been one of the most exciting experiences as a software developer so far. A lot of the consulting projects we’ve worked on in the past year ended up circling back to the problem of labelling data to train custom models.

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Hacker News

JULY 5, 2017

[update] I'm on some list now that I have written an article about some kind of "bomb", ain't I?

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Christian Haschek

SEPTEMBER 9, 2017

**In this project I'll upgrade a cheap fish tank LED light to be dimmable, controllable via Wifi a

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Dataconomy

OCTOBER 11, 2017

The power of Algorithms to calculate, contemplate and anticipate the needs of patients is improving rapidly and still has no sign of slowing down. Everything from patient diagnosis to therapy selection will soon be moving at exponential rates. Does that mean the end of doctors? Not quite. To better understand. The post Algorithms will out-perform Doctors in just 10 years time appeared first on Dataconomy.

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Dataconomy

NOVEMBER 24, 2017

At some point, almost every company faces questions like How good are the customers that we acquire? How do they differ from each other? How much can we spend to encourage their first or next transaction? As a measure that determines the amount of profit a customer brings over the. The post Understanding the value of your customer: CLV 101 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.

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Dataconomy

SEPTEMBER 1, 2017

Data innovation and technology are a much discussed but rarely successfully implemented in large financial services firms. Despite $480 Billion spent globally in 2016 on financial services IT, the pace of financial innovation from incumbents lags behind FinTech which received a comparatively puny $17 Billion in investment in 2016. What. The post Why large financial institutions struggle to adopt technology and data science appeared first on Dataconomy.

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Dataconomy

SEPTEMBER 8, 2017

For many people, the concept of Artificial Intelligence (AI) is a thing of the future. It is the technology that is yet to be introduced. But Professor Jon Oberlander disagrees. He was quick to point out that AI is not in the future, it is now in the making. He began by mentioning Alexa, The post AI – The Present in the Making appeared first on Dataconomy.

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Dataconomy

OCTOBER 17, 2017

The importance of data science is only going to grow in the coming years. As we see the results of our data-empowered work take form in how we shape our businesses, our products and our own goals, we are beholden to take a reflective gaze at the relationship between our. The post Three Things Data Scientists Can do to help themselves and their organizations appeared first on Dataconomy.

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Dataconomy

JULY 24, 2017

Today, most banking, financial services, and insurance (BFSI) organizations are working hard to adopt a fully data-driven approach to grow their businesses and enhance the services they provide to customers. Like most other industries, analytics will be a critical game changer for those in the financial sector. Though many BFSI. The post The Impact of Big Data on Banking and Financial Systems appeared first on Dataconomy.

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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 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.

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Dataconomy

APRIL 26, 2017

According to the prediction of IDC Futurescapes, Two-thirds of Global 2000 Enterprises CEOs will center their corporate strategy on digital transformation. A major part of the strategy should include machine-learning (ML) solutions. The implementation of these solutions could change how these enterprises view customer value and internal operating model today.

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Dataconomy

APRIL 21, 2017

It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based. The post Intro to Machine Learning: 10 Essential Algorithms For Machine Learning Engineers appeared first on Dataconomy.

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Dataconomy

JUNE 14, 2017

Big data sets are so complex and large that common data processing tools and technologies cannot cope with them. The process of inspection of such data and uncovering patterns is called big data analytics. The basic question which arises in our mind is, “In what way is the drug discovery. The post Big Data Is Revolutionizing The Way We Develop Life-Saving Medicine appeared first on Dataconomy.

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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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Dataconomy

APRIL 24, 2017

Many process manufacturing owner-operators in this next phase of a digital shift have engaged in technology pilots to explore options for reducing costs, meeting regulatory compliance, and/or increasing overall equipment effectiveness (OEE). Despite this transformation, the adoption of advanced analytics tools still presents certain challenges. The extensive and complicated tooling.

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Dataconomy

SEPTEMBER 22, 2017

In today’s digital landscape, customers expect you to deliver products and services in a fast and efficient manner. Heavyweights like Amazon and Google have set a bar in terms of operations, and they’ve set it high. An increasing need for more streamlined and efficient processes, combined with advancing technologies has. The post 4 data-driven ways to digitize your business appeared first on Dataconomy.

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Dataconomy

JULY 12, 2017

Agility and reactivity. These are two words more likely now than ever to feature in a corporate strategy session. Today’s business landscape is, after all, highly dynamic: increasing competition, margin pressures and the threat of disruptive innovation all conspire to erode the market shares of the complacent. The public sector. The post What is really driving public opinion?

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Dataconomy

MARCH 7, 2017

We hear the term “machine learning” a lot these days (usually in the context of predictive analysis and artificial intelligence), but machine learning has actually been a field of its own for several decades. Only recently have we been able to really take advantage of machine learning on a broad. The post Infographic: A Beginner’s Guide to Machine Learning Algorithms appeared first on Dataconomy.

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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.

Analytics

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.

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Dataconomy

JUNE 23, 2017

This article is part of a media partnership with PyData Berlin, a group helping support open-source data science libraries and tools. To learn more about this topic, please consider attending our fourth annual PyData Berlin conference on June 30-July 2, 2017. Miroslav Batchkarov and other experts will be giving talks. The post How Faulty Data Breaks Your Machine Learning Process appeared first on Dataconomy.

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Dataconomy

JUNE 28, 2017

As happens when boundless potential meets hard reality, enterprises now face a long, painful slog through the trenches of disillusionment and disappointment as they pursue the business transformation promised by Machine Learning for the Enterprise. The machine learning hype cycle is in overdrive, inflating expectations for magically easy and automated solutions to complex business problems decades.

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Dataconomy

APRIL 21, 2017

In 2016, global market uncertainty seemed to make investors somewhat more cautious, thanks to the results of the votes in the UK and the USA. However, fintech’s stellar run did not come to a halt. According to the February report by KPMG, venture capital investment in the space rose 7%, The post Banks and fintechs, instead of banks versus fintechs appeared first on Dataconomy.

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Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.

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Dataconomy

MARCH 13, 2017

As the scale of data grows across organizations with terabytes and petabytes coming into systems every day, running ad hoc queries across the entire dataset to generate important metrics and intelligence is no longer feasible. Once the quantum of data crosses a threshold, even simple questions such as what is. The post If you care about Big Data, you care about Stream Processing appeared first on Dataconomy.

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Dataconomy

NOVEMBER 10, 2017

Blockchain technology has rapidly asserted itself as one of the most potentially disruptive technological forces of the 21st century. Just like the internet has had far-reaching implications and touched upon nearly every aspect of modern life, blockchain architecture can create a wave of radical changes similar to those brought on by. The post Why Smart Contracts Will Bring Blockchain to the Masses appeared first on Dataconomy.

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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.

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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.

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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.

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