February, 2017

article thumbnail

25 Big Data Terms Everyone Should Know

Dataconomy

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.

Big Data 195
article thumbnail

One way to help a data science team innovate successfully

Eugene Yan

If things are not failing, you're not innovating enough.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Story time: How I started coding

Ines Montani

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.

article thumbnail

Supervised similarity: Learning symmetric relations from duplicate question data

Explosion

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.

40
article thumbnail

Optimizing The Modern Developer Experience with Coder

Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.

article thumbnail

The Mathematics of Machine Learning

Dataconomy

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.

article thumbnail

6 Ways Business Intelligence is Going to Change in 2017

Dataconomy

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.

More Trending

article thumbnail

Infographic: The 4 Types of Data Science Problems Companies Face

Dataconomy

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.

article thumbnail

How to transform your business with Artificial Intelligence

Dataconomy

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.

article thumbnail

The Next Tech Wave: Why Businesses Use Data Science Platforms

Dataconomy

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.

article thumbnail

The 20 Most Popular Business Intelligence Tools

Dataconomy

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.

article thumbnail

15 Modern Use Cases for Enterprise Business Intelligence

Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?

article thumbnail

2017 – The Year Data Made Bank?

Dataconomy

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.

Big Data 175
article thumbnail

The 3 Reasons Why Companies Should Use Data Intensive Computing

Dataconomy

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.

article thumbnail

The Power of a Data Value Chain For Your Business

Dataconomy

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.

Big Data 167
article thumbnail

The Internet of Things Entrepreneur Checklist – a guide for the budding IoT mogul

Dataconomy

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.

article thumbnail

Marketing Operations in 2025: A New Framework for Success

Speaker: Mike Rizzo, Founder & CEO, MarketingOps.com and Darrell Alfonso, Director of Marketing Strategy and Operations, Indeed.com

Though rarely in the spotlight, marketing operations are the backbone of the efficiency, scalability, and alignment that define top-performing marketing teams. In this exclusive webinar led by industry visionaries Mike Rizzo and Darrell Alfonso, we’re giving marketing operations the recognition they deserve! We will dive into the 7 P Model —a powerful framework designed to assess and optimize your marketing operations function.

article thumbnail

Convince Your Boss! 5 Reasons to Attend the IoT Weekend

Dataconomy

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.

article thumbnail

Stream Processing Myths Debunked

Dataconomy

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.

Big Data 127
article thumbnail

Product Categorization API Part 3: Creating an API

Eugene Yan

Or how to put machine learning models into production.

article thumbnail

Protected:

Dataconomy

There is no excerpt because this is a protected post. The post Protected: appeared first on Dataconomy.

123
123
article thumbnail

Prepare Now: 2025s Must-Know Trends For Product And Data Leaders

Speaker: Jay Allardyce, Deepak Vittal, and Terrence Sheflin

As we look ahead to 2025, business intelligence and data analytics are set to play pivotal roles in shaping success. Organizations are already starting to face a host of transformative trends as the year comes to a close, including the integration of AI in data analytics, an increased emphasis on real-time data insights, and the growing importance of user experience in BI solutions.

article thumbnail

Supervised similarity: Learning symmetric relations from duplicate question data

Explosion

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.

article thumbnail

Deep text-pair classification with Quora's 2017 question dataset

Explosion

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.

article thumbnail

Deep text-pair classification with Quora's 2017 question dataset

Explosion

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.