2018

Deep automation in machine learning

O'Reilly on Data

We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline. In a previous post , we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure.

Top 12 BI tools of 2019

CIO

With more and more data at our fingertips, it’s getting harder to focus on the information relevant to our problems and present it in an actionable way. That’s what business intelligence is all about. To read this article in full, please click here (Insider Story

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Five Strategies for Slaying the Data Puking Dragon.

Occam's Razor

If you bring sharp focus, you increase chances of attention being diverted to the right places. That in turn will drive smarter questions, which will elicit thoughtful answers from available data. The result will be data-influenced actions that result in a long-term strategic advantage. It all starts with sharp focus. Consider these three scenarios…. Your boss is waiting for you to present results on quarterly marketing performance, and you have 75 dense slides.

The role of academia in data science education

Simply Statistics

I was recently asked to moderate an academic panel on the role of universities in training the data science workforce. I preceded each question with opinionated introductions which I have fused into this blog post. These are weakly held opinions so please consider commenting if you disagree with anything. To discuss data science education we first need to clearly state what it means.

Monitoring AWS Container Environments at Scale

In this eBook, learn how to monitor AWS container environments at scale with Datadog and which key metrics to monitor when leveraging two container orchestration systems (ECS and EKS).

What Business Analysts Can Learn From Swiss Cheese

BA Learnings

Swiss cheese has holes in various places on different slices of cheese when you cut it up. Let’s imagine these holes reflect weaknesses in the system where mistakes can pass through, afterall no system is perfect. One mistake passing through a hole in one slice of cheese might remain unnoticed and not lead to a business catastrophe, if it's corrected.

Meta-Learning For Better Machine Learning

Rocket-Powered Data Science

In a related post we discussed the Cold Start Problem in Data Science — how do you start to build a model when you have either no training data or no clear choice of model parameters. An example of a cold start problem is k -Means Clustering, where the number of clusters k in the data set is not known in advance, and the locations of those clusters in feature space ( i.e., the cluster means) are not known either.

More Trending

What's the difference between data lakes and data warehouses?

IBM Big Data Hub

If you’ve heard the debate among IT professionals about data lakes versus data warehouses, you might be wondering which is better for your organization. You might even be wondering how these two approaches are different at all

Big Data And Analytics Has Been Around Forever! Why Is It Still Important?

Timo Elliott

These are some quick answers to some common questions I get about Business Intelligence, Big Data, and Analytics: Big Data. The term has been around for quite some time. Why is it still important for innovative businesses? It’s clear that data is one of the most important assets of the future. Organizations want to optimize their end-to-end customer experience, to improve productivity, and to engage the workforce in new ways. All of these things require data and analytics.

The most practical causal inference book I’ve read (is still a draft)

Data Science and Beyond

I’ve been interested in the area of causal inference in the past few years. In my opinion it’s more exciting and relevant to everyday life than more hyped data science areas like deep learning. However, I’ve found it hard to apply what I’ve learned about causal inference to my work. Now, I believe I’ve finally found a book with practical techniques that I can use on real problems: Causal Inference by Miguel Hernán and Jamie Robins.

IRM Is Essential for Digital Transformation Success

John Wheeler

Last week, I had the distinct privilege to join my Gartner colleagues from our Risk Management Leadership Council in presenting the Q4 2018 Emerging Risk Report. We hosted more than 500 risk leaders across the globe in our exploration of the most critical risks. The Q4 2018 Emerging Risks Survey, designed by Gartner, captures and analyzes senior executives’ opinions on emerging risks and provides actionable insight on identifying and mitigating these risks.

Assess and Advance Your Organization’s DevSecOps Practices

In this white paper, a DevSecOps maturity model is laid out for technical leaders to use to enable their organizations to stay competitive in the digital economy.

Building tools for enterprise data science

O'Reilly on Data

The O’Reilly Data Show Podcast: Vitaly Gordon on the rise of automation tools in data science. In this episode of the Data Show , I spoke with Vitaly Gordon , VP of data science and engineering at Salesforce. As the use of machine learning becomes more widespread, we need tools that will allow data scientists to scale so they can tackle many more problems and help many more people.

AI Unlocks The Business Intelligence In BI

Boris Evelson

In most enterprises, data access is a fait accompli: 72% of global data and analytics decision makers say that they can access the data they need to obtain insights in a timely manner. However, even the most modern BI tools that make data more accessible still require significant subject matter expertise to find the right […]. advanced analytics analytics applications artificial intelligence (AI) business intelligence predictive analytics

Closing Data's Last-Mile Gap: Visualizing For Impact!

Occam's Razor

I worry about data’s last-mile gap a lot. As a lover of data-influenced decision making, perhaps you worry as well. A lot of hard work has gone into collecting the requirements and implementation. An additional massive investment was made in the effort to perform ninja like analysis. The end result was a collection trends and insights. The last-mile gap is the distance between your trends and getting an influential company leader to take action.

Chromebook Data Science - a free online data science program for anyone with a web browser.

Simply Statistics

The Johns Hopkins Data Science Lab has been teaching massive online open courses for more than 5 years now. During that time we’ve reached more than 5 million learners who want to break into the number one rated job in America. While we have been incredibly excited about the results of these training programs, we’ve also learned over the last 5+ years that there are still significant barriers to getting into data science.

Address the Challenges of Siloed Monitoring Tools

Companies frequently experience monitoring tool sprawl. Find out why monitoring tool sprawl occurs, why it’s a problem for businesses, and the positive business impacts of monitoring tool consolidation.

Confirmation Bias: What BAs Can Learn From Data Scientists

BA Learnings

When we have a strong belief about something or a bias towards a particular opinion, we consciously or unconsciously seek out evidence that validates what we already believe. When we come across contrary evidence, our default behaviour is to ignore it, diminish it or in some cases, conclude that it’s wrong prematurely without exploring its merits. This behaviour is due to a cognitive bias known as confirmation bias.

Recent top-selling books in AI and Machine Learning

Rocket-Powered Data Science

News and Announcements from Tableau and TC18

David Menninger's Analyst Perspectives

Once again I attended Tableau's Users Conference, along with 17,000 other attendees, affectionately self-referred to as "data nerds". Pushing the envelope in data capabilities and access, Tableau introduced the "Ask Data" feature, allowing users to prose natural language queries and receive a response, along with new data preparation capabilities and other enhancements to help data analysts.

The journey to AI is easier than you might think

IBM Big Data Hub

Only IBM has step by step framework that helps clients accelerate their journey to AI – and it starts with Information Architecture.

IT 85

Cloud-Scale Monitoring With AWS and Datadog

In this eBook, find out the benefits and complexities of migrating workloads to AWS, and services that AWS offers for containers and serverless computing.

How AI is Lowering the Barrier to Entry for BI and Analytics

Birst BI

According to Gartner, more than 3,000 CIOs ranked Business Intelligence (BI) and Analytics as the top differentiating technology for their organizations. If BI and Analytics is such a game-changer, then why is the average adoption rate in organizations only 32%?

KPI 72

Crawling the internet: data science within a large engineering system

The Unofficial Google Data Science Blog

by BILL RICHOUX Critical decisions are being made continuously within large software systems. Often such decisions are the responsibility of a separate machine learning (ML) system. But there are instances when having a separate ML system is not ideal. In this blog post we describe one of these instances — Google search deciding when to check if web pages have changed.

What Kagglers are using for Text Classification

MLWhiz

With the problem of Image Classification is more or less solved by Deep learning, Text Classification is the next new developing theme in deep learning. For those who don’t know, Text classification is a common task in natural language processing, which transforms a sequence of text of indefinite length into a category of text. How could you use that? To find sentiment of a review. Find toxic comments in a platform like Facebook Find Insincere questions on Quora

Managing risk in machine learning

O'Reilly on Data

Considerations for a world where ML models are becoming mission critical. In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in New York last September. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations. Let’s begin by looking at the state of adoption.

Risk 203

What E-Commerce Performance Metrics Are CTOs Monitoring?

In this eBook, Danny Miles, CTO of Dollar Shave Club, reveals an efficient framework for thinking about and prioritizing the performance metrics that matter most to him, providing a blueprint for fellow e-commerce CTOs to follow as they evaluate their own business.

Top 10 Data Governance Predictions for 2019

erwin

This past year witnessed a data governance awakening – or as the Wall Street Journal called it, a “global data governance reckoning.” There was tremendous data drama and resulting trauma – from Facebook to Equifax and from Yahoo to Marriott. The list goes on and on. And then, the European Union’s General Data Protection Regulation (GDPR) took effect , with many organizations scrambling to become compliant. So what’s on the horizon for data governance in the year ahead?

Unsexy Fundamentals Focus: User Experiences That Print Money

Occam's Razor

Like me, I'm sure you are working on complex challenges when it comes to data. Multi-petabyte data warehouses. Multi-touch, cross-channel attribution analysis. Media mix modeling. Predictive analytics. Human-centric analysis. Oh, and let's not forget the application of machine learning to every facet of your work. It is genuinely fun to work on these opportunities. They’re difficult, and every step forward offers a renewed sense of excitement and inspiration.

Sales 102

Divergent and Convergent Phases of Data Analysis

Simply Statistics

There are often discussions within the data science community about which tools are best for doing data science. The most recent iteration of this discussion is the so-called “First Notebook War” , which is well-summarized by Yihui Xie in his blog post (it is a great read).

5 Simple Ways BAs Can Avoid Repeating Mistakes From Past Projects

BA Learnings

Once you embark on a new business analyst job or project, chances are that you will try to avoid past mistakes and look for ways in which you can deliver better results. A lot can be said for this motivation. The beginning of a project is usually an opportunity for a fresh start. Regardless of how advanced your business analysis skills are, or how fine-tuned your business analysis process is, there is always room to do better.

AI in Manufacturing

Manufacturers want to deliver the best products on the market as quickly and ethically as possible. Learn how to solve your most urgent manufacturing and business needs with an enterprise AI platform.

Data Scientist’s Dilemma – The Cold Start Problem

Rocket-Powered Data Science

The ancient philosopher Confucius has been credited with saying “study your past to know your future.” This wisdom applies not only to life but to machine learning also. Specifically, the availability and application of labeled data (things past) for the labeling of previously unseen data (things future) is fundamental to supervised machine learning. Without labels (diagnoses, classes, known outcomes) in past data, then how do we make progress in labeling (explaining) future data?

The Market of Data at Strata

David Menninger's Analyst Perspectives

In 2017 Strata + Hadoop World was changed to the Strata Data Conference. As I pointed out in my coverage of last year’s event , the focus was largely on machine learning and artificial intelligence (AI). That theme continued this year, but my impression of the event was of a community looking to get value out of data regardless of the technology being used to manage that data.

Why next-generation execs should care about data governance

IBM Big Data Hub

There’s a general need for next-gen executives to not only understand corporate regulations, but be able to adhere to and follow them using metadata solutions like data governance. As the business world’s top asset becomes data, data governance will ensure that data and information being handled is consistent, reliable and trustworthy. Establishing and deploying an analytics platform that embeds data governance and data integration, amongst other solutions, has never been more critical

Inside the Mind and Methodology of a Data Scientist

Birst BI

When you hear about Data Science, Big Data, Analytics, Artificial Intelligence, Machine Learning, or Deep Learning, you may end up feeling a bit confused about what these terms mean. And it doesn’t help reduce the confusion when every tech vendor rebrands their products as AI. So, what do these terms really mean? What are overlaps and differences? And most importantly, what can this do for your business?

5 Things You Always Wanted to Know About Automating Data Science, But Never Asked!

Speaker: Judah Phillips, Co-CEO and Co-Founder, Product & Growth at Squark

Automating the sophisticated, complex aspects of data science is now simple with the no-code platform Squark. Judah Phillips, the co-CEO & co-Founder of Squark answers the 5 Things You Always Wanted to Know About Automating Data Science, but Never Asked!