Sat.Jun 22, 2019 - Fri.Jun 28, 2019

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How To Find And Resolve Blind Spots In Your Data

Smart Data Collective

There’s a growing number of tools that you can use to analyze data for a business. But you may not be overly confident in the results if you don’t take the data’s blind spots into account. There’s no single way to do that, but we’ll look at some possibilities here. The first thing to keep in mind is that a blind spot generally represents an “unknown unknown.” In other words, it’s a factor you didn’t take into account because you didn’t

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6 Useful Programming Languages for Data Science You Should Learn (that are not R and Python)

Analytics Vidhya

Overview Which programming language should you pick for data science? Here’s a list of 6 powerful ones that are not Python or R These. The post 6 Useful Programming Languages for Data Science You Should Learn (that are not R and Python) appeared first on Analytics Vidhya.

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Take Advantage Of Modern CEO Dashboards – Examples And Templates

datapine

“The only safe ship in a storm is leadership.” – Faye Wattleton. For any business to succeed, it needs purpose, direction, and the resources to grow as well as adapt to the landscape surrounding it – which is why strong leadership is essential. As a CEO, you’re responsible for overseeing every aspect of your business, from the people and the internal culture all the way through to key sales, marketing, and financial strategies.

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Glossaries of Data Science Terminology

Rocket-Powered Data Science

Here is a compilation of glossaries of terminology used in data science, big data analytics, machine learning, AI, and related fields: Glossary of common Machine Learning, Statistics and Data Science terms. Data Science Glossary on DataScienceCentral. Data Science Glossary. Machine Learning Glossary at Google. Glossary of Artificial Intelligence Terms (From A to Z).

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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have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth.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.

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One simple chart: Who is interested in Spark NLP?

O'Reilly on Data

As we close in on its two-year anniversary, Spark NLP is proving itself a viable option for enterprise use. In July 2016, I broached the idea for an NLP library aimed at Apache Spark users to my friend David Talby. A little over a year later, Talby and his collaborators announced the release of Spark NLP. They described the motivation behind the project in their announcement post and in this accompanying podcast that Talby and I wrote, as well as in this recent post comparing popular open source

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Top 5 Must-Read Answers – What does a Data Scientist do on a Daily Basis?

Analytics Vidhya

Overview What does a data scientist do on a day-to-day basis? A popular and must-know question We analyze this question from a data scientist’s. The post Top 5 Must-Read Answers – What does a Data Scientist do on a Daily Basis? appeared first on Analytics Vidhya.

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Product Management for AI

Domino Data Lab

Pete Skomoroch presented “ Product Management for AI ” at Rev. This post provides a distilled summary, video, and full transcript. Session Summary. Pete Skomoroch ’s “ Product Management for AI ”session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and how to navigate key challenges.

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Chief Data Officers Struggle To Make A Business Impact

Bruno Aziza

Although firms continue to restructure the CDO role, with a few firms even having eliminated the position for the time being, the Chief Data Officer function is here to stay.

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Announcing DataHack Summit 2019 – The Biggest Artificial Intelligence and Machine Learning Conference Yet

Analytics Vidhya

DataHack Summit 2019 Bringing Together Futurists to Achieve Super Intelligence DataHack Summit 2018 was a grand success with more than 1,000 attendees from various. The post Announcing DataHack Summit 2019 – The Biggest Artificial Intelligence and Machine Learning Conference Yet appeared first on Analytics Vidhya.

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How Big Data Will Make Or Break Future Smart Cities

Smart Data Collective

In today’s digital age, big data is incorporated into many aspects our daily lives. Big data is essentially massive amounts of data that is used in order to drive strategic decisions. An example of how it is used in daily life is through using online maps such as Google Maps to take the quickest route to work possible. Through the collection of data , patterns of traffic congestion are produced to give you the best route.

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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?

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Who Will Win Wimbledon? Serving Up Some Data

DataRobot

It’s been a fun year in the world of professional tennis. From the men’s tour, we’ve witnessed Novak Djokovik winning in Australia, Rafael Nadal winning in France, and Roger Federer rounding out the continued domination of the Big Three. With the emergence of a new generation on the women’s tour, it’s been exciting to watch Naomi Osaka win in Australia and Ashleigh Barty win in France.

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Solving the Ocean Plastic Pollution Problem With Data

Dataiku

Plastic may be one of the most versatile materials on this planet, with a broad range of useful applications in almost every aspect of our daily lives, but it’s also one of the worst pollutants. It can take up to 1,000 years to biodegrade and only a small fraction of what we produce ends up being recycled.

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The Future of FP&A

Jedox

We are pleased to welcome an expert in Financial Planning and Analysis (FP&A), Mr. Anders Liu-Lindberg, as a guest to the Jedox blog. Mr. Liu-Lindberg is a Senior Finance Business Partner at Maersk and co-author of the book “Create Value as a Finance Business Partner.” He is also a finance industry blogger on LinkedIn with more than 33,000 followers.

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Big Data For Instagram: Using Data To Perfect Your Instagram Storyboard

Smart Data Collective

Do you want to create Instagram stories like a pro, but don’t know how to go about it? You have come to the right page because we are going to discuss how you create the storyboard for Instagram stories. The good news is that big data has made it easier than ever to create powerful Instagram content. How Big Data is Making Instagram Stories More Effective.

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8 Steps to Transformation at Speed & Scale – Your Guide to Deploying StratOps

📌Is your Data & AI transformation struggling to really impact the business? Discover the game-changing StratOps approach that: Bridges the Gap : Connect your Data & AI strategy to your operating model, to ensure alignment at every level. Prioritizes Outcomes : Focuses on concrete business outcomes from day one, rather than capabilities in isolation.

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How automating AI empowers humans to master business innovation

IBM Big Data Hub

With the automated AI and ML advancements, you may find yourself wondering--what are the overall impacts to business? How will all of this technological progress impact the ways we run our business and perform our jobs?

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Top-Down vs. Bottom-Up Culture Change

Dataiku

Last year when we surveyed over one hundred data professionals, they ranked organizational change as their third biggest data challenge (behind data cleaning and model productionalization). However, this year, organizational change moved up to the second place slot, suggesting that organizations have not yet overcome the culture change obstacles to data integration.

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What Working “at Scale” Really Means

Teradata

Rob Armstrong discusses the challenges of moving from a departmental solution to operational and production systems working at scale, and how Teradata Vantage can solve for them.

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How Data Analytics Is Changing The Insurance Industry

Smart Data Collective

The insurance industry is based on the idea of managing risk. To determine this risk, the industry must consult data and see what trends are evident to draft their risk profiles. The journal Risk Management and Insurance Review mentions that historically, in the latter half of the twentieth century, the analysis of trends was the primary driver in determining risk in the insurance business.

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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.

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Cloudera Provides First Look at Cloudera Data Platform, the Industry’s First Enterprise Data Cloud

Cloudera

Cloudera Unveils Industry’s First Enterprise Data Cloud in Webinar. How do you take a mission-critical on-premises workload and rapidly burst it to the cloud? Can you instantly auto-scale resources as demand requires and just as easily pause your work so you don’t run up your cloud bill? On June 18th, Cloudera provided an exclusive preview of these capabilities, and more, with the introduction of Cloudera Data Platform (CDP), the industry’s first enterprise data cloud.

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Measure Twice, Cut Once: How the Right Data Modeling Tool Drives Business Value

erwin

The need for an effective data modeling tool is more significant than ever. For decades, data modeling has provided the optimal way to design and deploy new relational databases with high-quality data sources and support application development. But it provides even greater value for modern enterprises where critical data exists in both structured and unstructured formats and lives both on premise and in the cloud.

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How IBM is advancing Watson Anywhere with new DataOps and discovery capabilities

IBM Big Data Hub

While data is an enterprise’s most valuable resource when it comes to gaining competitive advantage and improving business performance, time is a critical component. Businesses run 24x7, tasking our data citizens to maximize actionable insights that will drive the actions of tomorrow.

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4 Ways Predictive Analytics Will Improve Healthcare

Smart Data Collective

Medical care has relied on the education and expertise of doctors. Human error is common and 250,000 people per year die from medical errors. As the third-leading cause of death in the United States, limiting errors is a key focus in the healthcare industry. Big data and predictive analytics will lead to healthcare improvement. But how? Health IT Analytics previously published an excellent paper on some of the best use cases of predictive analytics in healthcare.

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The Cloud Development Environment Adoption Report

Cloud Development Environments (CDEs) are changing how software teams work by moving development to the cloud. Our Cloud Development Environment Adoption Report gathers insights from 223 developers and business leaders, uncovering key trends in CDE adoption. With 66% of large organizations already using CDEs, these platforms are quickly becoming essential to modern development practices.

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My Favorite Source for State Icons to Use in Data Visualizations

Depict Data Studio

Does the first draft of your report or slideshow have way too much text? Are you looking for strategies for transforming your wall of text into an effective visual? Do you worry that you’re just not a visual person? Are you hesitant to add visuals to your projects because you think it’ll take forever and blow your budget? If so, I wrote this article for you!

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Automate While You Migrate: Cloud Automation Tools and You

Sisense

Migrating your data to the cloud is a huge undertaking, rife with challenges and obstacles. It’s also an opportunity to simplify your life if you choose the right solution. Cloud BI is becoming increasingly common, as more organizations put their data on the cloud. BI providers have responded by offering a range of options to deliver the analytical horsepower users demand, with the simplicity they have come to expect from cloud-based solutions.

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Predictive Analytics World 2019 – What I Learned and What I Said

Decision Management Solutions

I presented on Backwards Engineering – planning Machine Learning (ML) deployment in reverse. Data shows that a traditional data-first approach to analytics is not generating much value for companies and I urged the audience instead to adopt a decisions-first mindset. The last mile – getting ML models embedded into production systems – is critically important for analytic value and yet it is hard and often neglected.

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Technology News: Artificial Intelligence Can Now Identify PTSD

Smart Data Collective

Technological development is only speeding up. Virtual reality and high-tech solutions are changing reality in its numerous aspects. Today, we have smart homes with internet-connected kettles and robotic vacuum cleaners, job interviews are conducted by AI, and countless gadgets are meant to make our lives more comfortable and cozy. Very soon, humans may even be able to communicate without words , converting thoughts into electric signals.

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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.

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5 Data Science Challenges Banks Face (And How to Overcome Them)

DataRobot

Making predictions has been a part of the banking industry since the world was flat. These days, you would be hard-pressed to identify a line of business or function in a bank that doesn’t have multiple needs for predictive analytics.

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3 Ways Data Engineers Can Deal with Enterprise Data Pipelines

Sisense

Data engineers are considered the real builders in the data world today, and one of the main reasons is that they help organizations get value out of their data. For an enterprise company , that can mean building and maintaining data pipelines or optimizing database queries and anything in between. If you are a data engineer, then you know that data is your most valuable asset.

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How Teradata and Oxford Saïd are Modernizing Analytics for Academic Research

Teradata

Oxford and Teradata partner to modernize analytics for academic research, shape new bodies of research and find answers to pressing business challenges.

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How Too Many eCommerce Marketers Let Their Big Data Go To Waste

Smart Data Collective

If you haven’t been paying attention over the last several years, big data is the reigning web 2.0 business model. In an age of global competition and nearly limitless offerings, your customers demand personalization. If they don’t feel like you are speaking to them directly, and considering their unique needs, they’re likely to take their business elsewhere.

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The Ultimate Guide To Data-Driven Construction: Optimize Projects, Reduce Risks, & Boost Innovation

Speaker: Donna Laquidara-Carr, PhD, LEED AP, Industry Insights Research Director at Dodge Construction Network

In today’s construction market, owners, construction managers, and contractors must navigate increasing challenges, from cost management to project delays. Fortunately, digital tools now offer valuable insights to help mitigate these risks. However, the sheer volume of tools and the complexity of leveraging their data effectively can be daunting. That’s where data-driven construction comes in.