datapine

NOVEMBER 27, 2019

Over the past decade, business intelligence has been revolutionized. Data exploded and became big. We all gained access to the cloud. Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. The rise of self-service analytics democratized the data product chain. Suddenly advanced analytics wasn’t just for the analysts. 2019 was a particularly major year for the business intelligence industry.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Analytics Vidhya

NOVEMBER 26, 2019

Overview What is the chi-square test? How does it work? Learn about the different types of Chi-Square tests and where and when you should. The post What is the Chi-Square Test and How Does it Work? An Intuitive Explanation with R Code appeared first on Analytics Vidhya.

Testing 306

Testing IT Analytics Statistics 306

Corinium

NOVEMBER 7, 2019

The notion that businesses should be making data-driven decisions may seem obvious to a CDAO. But the fact is, people in other business functions don’t always see things that way.

Data-driven 247

Data-driven 247

O'Reilly on Data

NOVEMBER 25, 2019

In this interview from O’Reilly Foo Camp 2019, Dean Wampler, head of evangelism at Anyscale.io, talks about moving AI and machine learning into real-time production environments. Highlights from the interview include: Facilitating the transition from research to production in a robust way introduces a number of complications, Wampler says, including governance, GDPR, and traceability rules.

Machine Learning 221

Machine Learning Modeling Technology IT 221

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.

Experimentation

KDnuggets

NOVEMBER 28, 2019

Open source is becoming the standard for sharing and improving technology. Some of the largest organizations in the world namely: Google, Facebook and Uber are open sourcing their own technologies that they use in their workflow to the public.

Data Science 160

Data Science Technology Deep Learning Visualization 160

erwin

NOVEMBER 28, 2019

Enterprise architecture tools are becoming more important than ever. The International Enterprise Architecture Institute (IEAI) defines enterprise architecture (EA) as “the analysis and documentation of an enterprise in its current and future states from an integrated strategy, business and technology perspective.”. In the era of data-driven business, such perspective is critical.

Enterprise 145

Enterprise Data-driven Data Governance Digital Transformation 145

Analytics Vidhya

NOVEMBER 10, 2019

Overview What is Game Theory? And how does it apply to artificial intelligence (AI)? Game theory for AI is a fascinating concept that we. The post Game (Theory) for AI? An Illustrated Guide for Everyone appeared first on Analytics Vidhya.

Analytics 307

Analytics IT 307

Corinium

NOVEMBER 27, 2019

Data ethics, data culture and the future of advanced analytics were the three themes that dominated CDAO Fall in Boston, MA last week. As Corinium’s flagship conference, the annual event showcases how the world’s top data and analytics leaders are driving change within their organizations and reveals the key talking points for the year ahead.

Analytics 221

Analytics Data Analytics 221

O'Reilly on Data

NOVEMBER 13, 2019

In this interview from O’Reilly Foo Camp 2019, Eric Jonas, assistant professor at the University of Chicago, pierces the hype around artificial intelligence. Highlights from the interview include: Jonas argues that “AI is a lie”—meaning that our expectations far outsize the reality of what’s currently possible. One of the issues arising from that disconnect is a level of corporate investment in the research process that hasn’t been seen before.

Machine Learning 183

Machine Learning Data Collection Modeling IT 183

KDnuggets

NOVEMBER 28, 2019

This article will walk through an extractive text summarization process, using a simple word frequency approach, implemented in Python.

Analytics 159

Analytics 159

Speaker: Anne Steiner and David Laribee

As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineers’ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.

Strategy

Data Science 101

NOVEMBER 29, 2019

Initiative for Analytics and Data Science Standards (IADSS) is an organization working to develop standards around the roles in data science. They did a large survey earlier this year and they are starting to role out some of their results. Below is a video with some early results. Great Stuff! Data Science 101 is proud to be an IADSS Digital Community Partner.

Data Science 120

Data Science Analytics 120

datapine

NOVEMBER 19, 2019

In this day and age, we’re all constantly hearing the terms “big data”, “data scientist”, and “in-memory analytics” being thrown around. Almost all the major software companies are continuously making use of the leading Business Intelligence (BI) and Data discovery tools available in the market to take their brand forward. Efficient tactics can be created along with a better overall decision-making process within an organization with the help of social and collaborative business intelligence too

Business Intelligence 146

Business Intelligence Knowledge Discovery Dashboards Software 146

Analytics Vidhya

NOVEMBER 18, 2019

Overview Here’s a quick introduction to building machine learning pipelines using PySpark The ability to build these machine learning pipelines is a must-have skill. The post Want to Build Machine Learning Pipelines? A Quick Introduction using PySpark appeared first on Analytics Vidhya.

Machine Learning 305

Machine Learning Analytics Big Data 305

Corinium

NOVEMBER 27, 2019

Time and time again, we hear from our contacts asking to hear about real use cases. We have partnered with Zendesk to share their customer stories. "The customer is always right" is a phrase that Zendesk takes very seriously. What a customer needs and wants? That's at the heart and soul of everything Zendesk does. Whether it’s small companies trying to rapidly grow and build their brand, or large companies trying to maintain customer loyalty, Zendesk aims to empower businesses to seamlessly serv

Interactive 195

Interactive Management Analytics 195

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.

Testing

O'Reilly on Data

NOVEMBER 18, 2019

In this interview from O’Reilly Foo Camp 2019, Hands-On Unsupervised Learning Using Python author Ankur Patel discusses the challenges and opportunities in making machine learning and AI accessible and financially viable for enterprise applications. Highlights from the interview include: The biggest hurdle businesses face when implementing machine learning or AI solutions is cleaning and preparing unstructured data that exists across silos.

ROI 132

ROI Unstructured Data Machine Learning Modeling 132

KDnuggets

NOVEMBER 27, 2019

Also: Deep Learning for Image Classification with Less Data; How to Speed up Pandas by 4x with one line of code; 25 Useful #Python Snippets to Help in Your Day-to-Day Work; Automated Machine Learning Project Implementation Complexities.

Deep Learning 131

Deep Learning Machine Learning 131

Data Science 101

NOVEMBER 29, 2019

In the United States, it is a holiday week, so the news is pretty limited from many of the big cloud providers. Luckily, Amazon has come through with a flurry of machine learning announcements. Amazon is holding their annual re:Invent Conference next week, so maybe these announcements are precursors to some bigger news next week. We will have to wait and see.

Data Science 115

Data Science Deep Learning Machine Learning Modeling 115

Smart Data Collective

NOVEMBER 28, 2019

Businesses can use big data in many capacities, but those who use it for social media are at a huge advantage. It enables you as a social media marketer to get a closer look at your customer base, understand what drives purchasing decisions , and encourage consumers to pull the trigger. Using big data to augment your social media strategy provides a wealth of opportunities simply because social media is such an integral part of people’s lives.

Big Data 110

Big Data Advertising Recreation/Entertainment Data-driven 110

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Analytics Vidhya

NOVEMBER 3, 2019

Overview Here are six open-source data science projects to enhance your skillset These projects cover a diverse set of domains, from computer vision to. The post 6 Exciting Open Source Data Science Projects you Should Start Working on Today appeared first on Analytics Vidhya.

Data Science 287

Data Science Analytics Deep Learning 287

Corinium

NOVEMBER 24, 2019

Joining us at the Data and Analytics in Healthcare (4-5 March | Melbourne), we are pleased to welcome Day Manuet, Data Analyst – Analytics at Epworth HealthCare. She shares her thoughts on the use of data within Australia's healthcare system, and what steps need to be taken to improve patient outcomes through data analytics and much more.

Analytics 195

Analytics Data Analytics Data Strategy Data Governance 195

FineReport

NOVEMBER 26, 2019

When it comes to data analytics tools, we always have questions. What is the difference between so many data analysis tools? Which is better? Which one should I study? Although this is a commonplace topic, it is really important, and I have been working hard to pursue the answer to this ultimate problem. If you go online to search for relevant information in this area, it is difficult to see a fair point of view.

Data Analytics 98

Data Analytics Analytics Statistics Visualization 98

KDnuggets

NOVEMBER 5, 2019

Artificial Intelligence continues to fill the media headlines while scientists and engineers rapidly expand its capabilities and applications. With such explosive growth in the field, there is a great deal to learn. Dive into these 10 free books that are must-reads to support your AI study and work.

IT 123

IT 123

Speaker: Nicholas Zeisler, CX Strategist & Fractional CXO

The first step in a successful Customer Experience endeavor (or for that matter, any business proposition) is to find out what’s wrong. If you can’t identify it, you can’t fix it! 💡 That’s where the Voice of the Customer (VoC) comes in. Today, far too many brands do VoC simply because that’s what they think they’re supposed to do; that’s what all their competitors do.

Customer Analytics

Data Science 101

NOVEMBER 26, 2019

It is no secret that data science is difficult. Companies struggle to succeed with data science projects. Even Gartner predicts that by 2022 only 20% of analytics projects will deliver business value. That means about 80% will fail to deliver value. Thus, companies need to be very careful about running data analytics projects. There are many reasons for the failure of data science projects.

Data Science 109

Data Science Data Strategy Strategy Software 109

Smart Data Collective

NOVEMBER 21, 2019

Big data is transforming the daily realities of running a business. Companies can use big data to handle certain tasks more quickly and cost-effectively than ever. Vince Campisi, CIO of GE Software, Ash Gupta, an executive with American Express, and many other companies use big data to get a competitive advantage. Of course, big data also raises some new challenges.

Big Data 108

Big Data Sales Cost-Benefit Marketing 108

Analytics Vidhya

NOVEMBER 6, 2019

Overview Deep learning is a vast field but there are a few common challenges most of us face when building models Here, we talk. The post 4 Proven Tricks to Improve your Deep Learning Model’s Performance appeared first on Analytics Vidhya.

Deep Learning 287

Deep Learning Modeling Analytics Unstructured Data 287

erwin

NOVEMBER 6, 2019

Can the 80/20 Rule Be Reversed? erwin released its State of Data Governance Report in February 2018, just a few months before the General Data Protection Regulation (GDPR) took effect. This research showed that the majority of responding organizations weren’t actually prepared for GDPR, nor did they have the understanding, executive support and budget for data governance – although they recognized the importance of it.

Metadata 102

Metadata Data Governance Management Cost-Benefit 102

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Analytics

IBM Big Data Hub

NOVEMBER 22, 2019

Haruto Sakamoto, the Chief Information Officer at a Japanese multinational imaging company, had a few challenges to contend with. His business units had a presence in 180 countries worldwide with geographically-dispersed data warehouses and business intelligence applications in various locations.

Data Lake 93

Data Lake Data Warehouse Business Intelligence Analytics 93

KDnuggets

NOVEMBER 12, 2019

While Pandas is the library for data processing in Python, it isn't really built for speed. Learn more about the new library, Modin, developed to distribute Pandas' computation to speedup your data prep.

Data Processing 122

Data Processing IT 122

Data Science 101

NOVEMBER 7, 2019

Microsoft just held one of its largest conferences of the year, and a few major announcements were made which pertain to the cloud data science world. Here they are in my order of importance (based upon my opinion). Azure Synapse. I think this announcement will have a very large and immediate impact. Azure Synapse Analytics can be seen as a merge of Azure SQL Data Warehouse and Azure Data Lake.

Data Science 106

Data Science Machine Learning Data Lake IoT 106

Smart Data Collective

NOVEMBER 25, 2019

More and more often, businesses are using data to drive their decisions — which makes cutting-edge analytics and business intelligence strategies one of the best advantages a company can have. New technologies, especially those driven by artificial intelligence (or AI), are changing how businesses collect and extract usable insights from data. Here are the six trends you should be aware of that will reshape business intelligence in 2020 and throughout the new decade. 1.

Business Intelligence 100

Business Intelligence Predictive Analytics Data Collection Internet of Things 100

Advertiser: Data Robot

The buzz around generative AI shows no sign of abating in the foreseeable future. Enterprise interest in the technology is high, and the market is expected to gain momentum as organizations move from prototypes to actual project deployments. Ultimately, the market will demand an extensive ecosystem, and tools will need to streamline data and model utilization and management across multiple environments.

Enterprise