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.

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.

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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.

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.

Are Your Embedded Analytics DevOps-Friendly?

Does your analytics solution work with your current tech stack and DevOps practices? If not, any update to the analytics could increase deployment complexity and become difficult to maintain. Learn the 5 elements of a DevOps-friendly embedded analytics solution.

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.

Consolidation around Cognos 11.1 and other news from IBM Analytics University

David Menninger's Analyst Perspectives

IBM's Analytics University (held in both Miami and Stockholm) brought about some large changes. Big announcements this year included a consolidation of IBM's Watson Analytics into Cognos 11.1, helping provide some clarity to their analytics offerings, along with new visualizations and better data preparation. This also includes a new conversational assistant to help generate narrative explanations of displays and interactive queries.

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.

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.

Data governance is not a sailor’s yarn

eSchool News

Whether you want to build a bridge, explore the sea, or simply try to identify new markets, you will only be as good as the data you use. This means it must be complete, in context, trusted and easily accessible to drive insights

Digital Trends Report 2020

As part of our goal to continue helping our community during these times, we wanted to share with you this critical data on the state of digital products across industries and provide context on how businesses are responding to the changing winds.

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.

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.

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.

5 Things a Data Scientist Can Do to Stay Current

DataRobot together with Snowflake – a leading cloud data platform provider — is helping data scientists stay current with the latest technology and data science best practices so that they can excel in an increasingly AI-driven workplace. Five Things a Data Scientist Can Do to Stay Current offers data scientists guidance for thriving in AI-driven enterprises.

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.

Creating a Holistic View: Data Consolidation and Integration

Perficient Data & Analytics

The consolidation of data and integration of systems is essential to providing a holistic 360-degree view of patients and members. This view can enable a variety of activities to enhance and drive efficiency in business and clinical activities, such as increasing patient safety and the quality of care healthcare delivery organizations provide to patients. One organization that understands the challenges associated with bringing data together across a large number of hospitals is Mayo Clinic.

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 87

Testing at Every Stage of Development

Up to 80% of new products fail. The reality is harsh and the reasons why are endless. Perhaps the new product couldn’t oust a customer favorite. Maybe it looked great but was too hard to use. Or, despite being a superior product, the go-to-market strategy failed. There’s always a risk when building a new product, but you can hedge your bets by understanding exactly what your customers' expectations truly are at every step of the development process.

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.

A Comprehensive Guide to Real-Time Big Data Analytics

ScienceSoft

Our big data consultants have come up with an easy guide to real-time big data analytics. We explain the term and describe a typical architecture, as well as share our thoughts about whether real-time analytics can be a competitive advantage

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

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

How Embedding AI-Powered Analytics Can Give You a Competitive Advantage

Embedding dashboards and reports aren’t enough. Futureproof your application by offering instant, actionable insights that will give you and your customers a competitive advantage.

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 92

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.

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?

Building Like Amazon

Speaker: Leo Zhadanovsky, Principal Solutions Architect, Amazon Web Services

Amazon's journey to its current modern architecture and processes provides insights for all software development leaders. To get there, Amazon focused on decomposing for agility, making critical cultural and operational changes, and creating tools for software delivery. The result was enabling developers to rapidly release and iterate software while maintaining industry-leading standards on security, reliability, and performance. Whether you're developing for a small startup or a large corporation, learning the tools for CI/CD will make your good DevOps team great. We are excited to be joined by Leo Zhadanovsky, a Principal Solutions Architect at Amazon Web Services.

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.

Will Digital Healthcare Technology Disrupt Independent Physicians

Perficient Data & Analytics

Why fear change? Change is good and has developed the world into what it is today. Change partners with adaptation, to promote a new way of doing things. However, is change in the healthcare industry putting independent physicians at risk? With the increased usage of digital healthcare technology, will the independent physician still be able to maintain the walk-in base of customers?

Prescriptive analytics: The cure for a transforming healthcare industry

IBM Big Data Hub

Prescriptive analytics offers healthcare decision makers the opportunity to influence optimal future outcomes. Based on decision optimization technology, these capabilities allow users to not just recommend the best course of action for patients or providers, they also enable comparison of multiple “what-if” scenarios to assess the impact of choosing one action over another

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

Rethinking Information Governance In The Age of Unstructured Enterprise Data

Today’s organizations are faced with the overwhelming challenge of managing, finding, and leveraging their information. This eBook discusses a newly discovered information discipline and is filled to the brim with helpful information.