TDAN

SEPTEMBER 3, 2019

As I write this, I can almost hear you wail “No, no, we don’t have too much metadata, we don’t have nearly enough! We have several projects in flight to expand our use of metadata.” Sorry, I’m going to have to disagree with you there. You are on a fool’s errand that will just provide […].

Metadata 65

Metadata Data Governance Big Data Modeling 65

datapine

SEPTEMBER 4, 2019

The secret is out, and has been for a while: In order to remain competitive, businesses of all sizes, from startup to enterprise, need business intelligence (BI). Business intelligence has evolved into smart solutions that provide effective data management – from extracting, monitoring, analyzing, and deriving actionable insights needed to stay competitive on the market, to powerful visualizations created with a dashboard builder which enables business users to interact with data and drill into

Dashboards 263

Dashboards Analytics Key Performance Indicator Sales 263

Andrew White

SEPTEMBER 4, 2019

For years theorists, economists, and even tech-evangelists, have all been arguing over the value of data. Notwithstanding the hype, even the Economist in 2017 suggested in a leader article that data was the new oil. Gartner has been writing about ‘data as an asset’ for years. We even ratified calculations to help determine and quantify the value of data assets.

Insurance 61

Insurance Marketing Reporting Sales 61

Analytics Vidhya

SEPTEMBER 4, 2019

Overview What is p-value? Where is it used in data science? And how can we calculate it? We answer all these questions and more. The post Everything you Should Know about p-value from Scratch for Data Science appeared first on Analytics Vidhya.

Data Science 307

Data Science Analytics IT Statistics 307

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Sales

Corinium

SEPTEMBER 4, 2019

Tell us about your experience in working with the data analytics community at TheMathCompany? Why do you like working in this space?

Data Analytics 150

Data Analytics Analytics 150

KDnuggets

SEPTEMBER 4, 2019

In the following post, I am going to give a brief guide to four of the most established packages for interpreting and explaining machine learning models.

Machine Learning 123

Machine Learning Modeling 123

Analytics Vidhya

SEPTEMBER 1, 2019

Overview Working on Data Science projects is a great way to stand out from the competition Check out these 7 data science projects on. The post Here are 7 Data Science Projects on GitHub to Showcase your Machine Learning Skills! appeared first on Analytics Vidhya.

Machine Learning 308

Machine Learning Data Science Analytics 308

Corinium

SEPTEMBER 3, 2019

Mobile BI should extend your ability to discover, collaborate, and act on insights and create an inclusive and effective data culture. The best mobile apps have simple-to-use tools designed specifically with mobile in mind that mean you always know when, what and, importantly, why something changed in your data. Download your copy of 'Mobile-Focused BI: Using Automation to Cut Through the Clutter' to find out more on making analytic insights instantly actionable everywhere.

Analytics 150

Analytics 150

KDnuggets

SEPTEMBER 5, 2019

This blog summarizes the career advice/reading research papers lecture in the CS230 Deep learning course by Stanford University on YouTube, and includes advice from Andrew Ng on how to read research papers.

Machine Learning 123

Machine Learning Deep Learning 123

CIO Business Intelligence

SEPTEMBER 5, 2019

There is golden knowledge in the sea of data that businesses are swimming in. Being able to fish out the business intelligence you need — when you need it — is the key to steering your ship.

Visualization 110

Visualization Business Intelligence IT 110

Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage

When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m

Testing

Analytics Vidhya

SEPTEMBER 2, 2019

Overview Learn how you can use computer vision and deep learning techniques to work with video data We will build our own video classification. The post Step-by-Step Deep Learning Tutorial to Build your own Video Classification Model appeared first on Analytics Vidhya.

Deep Learning 291

Deep Learning Modeling Analytics 291

Data Science 101

AUGUST 31, 2019

The competition site Kaggle has recently released some micro-courses aimed at helping people to quickly learn the skills of data science. It is called Kaggle Learn, Faster Data Science Education. It includes courses on: Python Deep Learning SQL and more. Check them out to quickly get up to speed. Happy Learning.

Deep Learning 107

Deep Learning Data Science IT 107

KDnuggets

SEPTEMBER 3, 2019

These three deep learning frameworks are your go-to tools for NLP, so which is the best? Check out this comparative analysis based on the needs of NLP, and find out where things are headed in the future.

Deep Learning 123

Deep Learning 123

Smart Data Collective

SEPTEMBER 4, 2019

We have talked extensively about the role of big data in marketing in previous articles. However, most of our articles relate to the use of big data with traditional marketing channels, including older digital marketing outlets. There are a number of new channels that use big data as well. Push notifications are among them. Hacker Moon wrote an article on the role of big data with push notifications.

Big Data 105

Big Data Marketing ROI Strategy 105

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Testing

Analytics Vidhya

SEPTEMBER 3, 2019

Overview Learn the inner workings and math behind the HOG feature descriptor The HOG feature descriptor is used in computer vision popularly for object. The post Feature Engineering for Images: A Valuable Introduction to the HOG Feature Descriptor appeared first on Analytics Vidhya.

Analytics 274

Analytics Deep Learning 274

DataCamp

SEPTEMBER 3, 2019

Learn why Python is so important, and how it’s useful across industries and all fields of business analytics.

Business Analytics 102

Business Analytics Analytics 102

KDnuggets

SEPTEMBER 6, 2019

Instead of focusing on skills thought to be required of data scientists, we can look at what they have actually done before.

Data Science 122

Data Science 122

IBM Big Data Hub

SEPTEMBER 5, 2019

Among organizations investing in AI hardware, software or services, more will buy IBM and rely on Watson than any other vendor. This according to a new IDC report which names IBM as 2018’s market leader in AI. So just what sets apart IBM as leader of the AI provider pack?

Marketing 101

Marketing Reporting Software 101

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Ontotext

SEPTEMBER 4, 2019

My great grandparents were married for more than sixty years. They had a telepathy that comes from living together that long. They could finish each other’s sentences or anticipate when one wanted a coffee or lunch. An exchange could go like this. ‘Remember those figs?’ Grandma said. ‘You’re talking about that holiday to France in ’76 and it wasn’t figs, it was apples.’.

Metadata 95

Metadata Consulting Reporting Technology 95

Smart Data Collective

SEPTEMBER 4, 2019

Maternal mortality rates in the United States jumped over 25% between 2000 and 2013. The CDC uses data to better understand why the United States has the highest maternal death rates in the developed world. Big data allows researchers to dig deeper into the issue to better understand what’s occurring that’s leading to increased deaths for mothers. Understaffed hospitals and medical errors are causing most of the deaths.

Big Data 93

Big Data Risk Testing Technology 93

KDnuggets

SEPTEMBER 4, 2019

A recurring subject in NLP is to understand large corpus of texts through topics extraction. Whether you analyze users’ online reviews, products’ descriptions, or text entered in search bars, understanding key topics will always come in handy.

Modeling 122

Modeling Analytics 122

MLWhiz

SEPTEMBER 1, 2019

We as data scientists have gotten quite comfortable with Pandas or SQL or any other relational database. We are used to seeing our users in rows with their attributes as columns. But does the real world really behave like that? In a connected world, users cannot be considered as independent entities. They have got certain relationships between each other and we would sometimes like to include such relationships while building our machine learning models.

Machine Learning 89

Machine Learning Modeling 89

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Reporting

Srividya Sridharan

SEPTEMBER 5, 2019

Discover the themes and topics we'll be tackling at this year's Data Strategy & Insights Forum.

Data Strategy 81

Data Strategy Strategy Predictive Analytics Customer Analytics 81

Smart Data Collective

SEPTEMBER 4, 2019

Big data is playing a big role in the commerce industry. Many experts believe it will be even more important in 2019. Big data is partially responsible for the 15% increase in ecommerce sales in 2018. Changing your data from one website or store to another is something that seems like a lot of work. However, if you pay attention to the data and other specifics when changing, you shouldn’t have any issues switching from one platform to another.

Big Data 91

Big Data Sales IT Technology 91

KDnuggets

SEPTEMBER 4, 2019

Recommender systems are an important class of machine learning algorithms that offer "relevant" suggestions to users. Categorized as either collaborative filtering or a content-based system, check out how these approaches work along with implementations to follow from example code.

Machine Learning 121

Machine Learning 121

Decision Management Solutions

SEPTEMBER 5, 2019

A great article appeared in Information Age recently based on an interview with Tom Davenport If you want to see the benefits of AI, forget moonshots and think boring. In it, Tom argues that “if enterprises ever want to see the benefits of AI, they must embrace the mundane”. This is particularly true for companies that aren’t tech startups. As Tom says, “moonshots are possible if you’re a tech giant and you have billions of dollars to spend on experimenting” but what if you can’t “pivot”?

Management 78

Management Cost-Benefit Insurance Manufacturing 78

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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?

Business Intelligence

Sisense

SEPTEMBER 3, 2019

Blog. Having seven years of experience with managing Redshift , a fleet of 335 clusters, combining for 2000+ nodes, we (your co-authors Neha, Senior Customer Solutions Engineer, and Chris, Analytics Manager, here at Periscope Data by Sisense) have had the benefit of hours of monitoring their performance and building a deep understanding of how best to manage a Redshift cluster.

Data Warehouse 78

Data Warehouse OLAP Statistics Cost-Benefit 78

Domino Data Lab

SEPTEMBER 3, 2019

This article covers how to perform hyperparameter optimization using a sequential model-based optimization (SMBO) technique implemented in the HyperOpt Python package. There is a complementary Domino project available. Introduction. Feature engineering and hyperparameter optimization are two important model building steps. Over the years, I have debated with many colleagues as to which step has more impact on the accuracy of a model.

Optimization 76

Optimization Dashboards Modeling Machine Learning 76

KDnuggets

SEPTEMBER 5, 2019

This is an interview between Rosaria Silipo and data scientists Paolo Tamagnini, Simon Schmid and Christian Dietz, asking a few questions on the topic of automated machine learning from their point of view, and some interesting examples of its practical use.

Machine Learning 121

Machine Learning IT 121

BizAcuity

SEPTEMBER 5, 2019

Admit it or not, casinos are booming to become a much bigger playground for businesses. With most operating within a hotel-like framework, there are more ways to engage their customers than ever before. From an operational standpoint, this means that casino-owners need to deploy different systems in different places across the floor of the casino to ensure seamless functioning.

ROI 75

ROI Data Warehouse Advertising Data Processing 75

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