January, 2020

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CCPA 2020: Getting Your Data Landscape Ready

Octopai

To all BI professionals, CIOs, CDOs, and everyone else relying on data to run a business: Happy New Year! It’s going to be…interesting. From California’s new data privacy law going into full effect to potential new U.S. federal data privacy laws on top of an alphabet soup of global laws and regulations, the name of the game for 2020 is going to be “compliance.”.

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5 Thoughts on How to Transition into Data Science from Different Backgrounds

Analytics Vidhya

Overview Looking to transition into data science? Here are 5 paths for a non-data science person to land a role in this space The. The post 5 Thoughts on How to Transition into Data Science from Different Backgrounds appeared first on Analytics Vidhya.

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Top 5 must-have Data Science skills for 2020

KDnuggets

The standard job description for a Data Scientist has long highlighted skills in R, Python, SQL, and Machine Learning. With the field evolving, these core competencies are no longer enough to stay competitive in the job market.

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Corinium Meets: Quantum Metric Head of Behavioural Research Marina Shapira

Corinium

Ahead of her presentation at CDAO UK, we spoke with Quantum Metric’s Marina Shapira about predictive analytics, why companies should embrace a culture of experimentation how and CAOs and CXOs can work together effectively. What is behavioural research? And what role should it play in an organization's data and analytics strategy? Behavioural research seeks to understand what motivates people, how they perceive the world, make decisions and form habit.

Metrics 300
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From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

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.

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Reinforcement learning for the real world

O'Reilly on Data

Roger Magoulas recently sat down with Edward Jezierski, reinforcement learning AI principal program manager at Microsoft, to talk about reinforcement learning (RL). They discuss why RL’s role in AI is so important, challenges of applying RL in a business environment, and how to approach ethical and responsible use questions. Here are some highlights from their conversation: Reinforcement learning is different than simply trying to detect something in an image or extract something from a da

Insurance 281
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Best Dashboard Ideas & Design Examples To Boost Your Business Success

datapine

The ability to monitor, visualize, and analyze relevant data gives today’s businesses, across a host of sectors, the power to understand their prospects, make informed decisions, increase efficiencies, and work towards a set of rewarding long term goals. With so much data available to today’s brands and businesses, to extract every drop of value from an ever-growing raft of digital insights and set the kind of KPIs that will drive your venture forward, having an easy to use, a visually-stunning

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Build your first Machine Learning pipeline using scikit-learn!

Analytics Vidhya

Overview Understand the structure of a Machine Learning Pipeline Build an end-to-end ML pipeline on a real-world data Train a Random Forest Regressor for. The post Build your first Machine Learning pipeline using scikit-learn! appeared first on Analytics Vidhya.

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Top 10 Technology Trends for 2020

KDnuggets

With integrations of multiple emerging technologies just in the past year, AI development continues at a fast pace. Following the blueprint of science and technology advancements in 2019, we predict 10 trends we expect to see in 2020 and beyond.

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Data Literacy: A Huge Opportunity for the Healthcare Industry

Corinium

It’s easy to get excited about the many ways AI and advanced analytics will shape the future of healthcare. But the industry has a way to go before these technologies begin having a significant impact on the health of ordinary Americans. In the meantime, there’s a great deal that health organizations can be doing today to deliver better quality care with data.

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Metrics Are The Characters of Data Stories

Juice Analytics

I like to think that the hero in your data story is your audience. The hero is the person who starts with conflict and, through the narrative journey, is transformed to resolve this conflict. That’s what you want the data story to accomplish for your audience — start with a question and move your audience (hero) to actions that will resolve the question.

Metrics 136
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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

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.

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Utilize The Effectiveness Of Professional Executive Dashboards & Reports

datapine

Without big data analytics, companies are blind and deaf, wandering out onto the Web like deer on a freeway. – Geoffrey Moore, Author of Crossing the Chasm & Inside the Tornado. In the digital age, brands, businesses, and organizations have a wealth of information at their fingertips: a level of insight that if leveraged correctly, not only has the power to offer a real competitive edge but provides the potential to innovate, inspire and create a well-oiled commercial machine that continues

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5 Ways Data Modeling Is Critical to Data Governance

erwin

Enterprises are trying to manage data chaos. They might have 300 applications, with 50 different databases and a different schema for each one. They also face increasing regulatory pressure because of global data regulations , such as the European Union’s General Data Protection Regulation (GDPR) and the new California Consumer Privacy Act (CCPA), that went into effect last week on Jan. 1.

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4 Applications of Regular Expressions that every Data Scientist should know (with Python code)!

Analytics Vidhya

Overview Regular Expressions or Regex is a versatile tool that every Data Scientist should know about Regex can automate various mundane data processing tasks. The post 4 Applications of Regular Expressions that every Data Scientist should know (with Python code)! appeared first on Analytics Vidhya.

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A Comprehensive Guide to Natural Language Generation

KDnuggets

Follow this overview of Natural Language Generation covering its applications in theory and practice. The evolution of NLG architecture is also described from simple gap-filling to dynamic document creation along with a summary of the most popular NLG models.

Modeling 157
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How to Build an Experimentation Culture for Data-Driven Product Development

Speaker: Margaret-Ann Seger, Head of Product, Statsig

Experimentation is often seen as an aspirational practice, especially at smaller, fast-moving companies who are strapped for time and resources. So, how can you get your team making decisions in a more data-driven way while continuing to remain lean and maintaining ship velocity? In this webinar, Margaret-Ann Seger, Head of Product at Statsig, will teach you how to build an experimentation culture from the ground-up, graduating from just getting started with data-driven development to operating

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The Dawn of AI-Powered Healthcare

Corinium

If there was ever any doubt that AI will revolutionize the healthcare industry, Google Health dispelled it with its latest research paper. Nature reports that the company has developed an AI program that’s better at spotting breast cancer in mammograms than expert radiologists.

Reporting 269
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AI-Driven Predictions for Super Bowl LIV: A Touchdown for Sports Analytics

DataRobot

In Super Bowl LIV this Sunday, the Kansas City Chiefs will face the San Francisco 49ers in Miami, Florida. This will be the first Super Bowl appearance for the Chiefs in 50 years. After a loss in 2013, the 49ers are looking to secure their sixth Super Bowl ring, which would tie them with the New England Patriots and Pittsburgh Steelers for the league record.

Analytics 133
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Cloud Data Science 4

Data Science 101

It was an exciting cloud data science week. Lots of interesting stuff to cover, so let’s get started. News. Microsoft DP-100 Certification Updated – The Microsoft Data Scientist certification exam has been updated to cover the latest Azure Machine Learning tools. Google Dataset Search goes GA – search and discover millions of datasets Google Cloud GPU Price Cut – Google reduces the prices of NVIDIA T4 GPUs which should save some money for people doing AI PyTorch 1.4 Relea

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Types of Data Models: Conceptual, Logical & Physical

erwin

There are three different types of data models: conceptual, logical and physical, and each has a specific purpose. Conceptual Data Models: High-level, static business structures and concepts. Logical Data Models: Entity types, data attributes and relationships between entities. Physical Data Models: The internal schema database design. An organization’s approach to data modeling will be influenced by its particular needs and the goals it is trying to reach, as explained here: What is Data Modeli

Modeling 143
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Entity Resolution Checklist: What to Consider When Evaluating Options

Are you trying to decide which entity resolution capabilities you need? It can be confusing to determine which features are most important for your project. And sometimes key features are overlooked. Get the Entity Resolution Evaluation Checklist to make sure you’ve thought of everything to make your project a success! The list was created by Senzing’s team of leading entity resolution experts, based on their real-world experience.

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Pandas Version 1.0 is Out! Top 4 Features Every Data Scientist Should Know

Analytics Vidhya

Overview Singleton scalar for missing values Dedicated datatype for strings Improved output formats and data summaries Introduction There are only a handful of. The post Pandas Version 1.0 is Out! Top 4 Features Every Data Scientist Should Know appeared first on Analytics Vidhya.

Analytics 362
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The Book to Start You on Machine Learning

KDnuggets

This book is thought for beginners in Machine Learning, that are looking for a practical approach to learning by building projects and studying the different Machine Learning algorithms within a specific context.

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Corinium Meets: Alteryx CDAO Alan Jacobson

Corinium

Ahead of his presentation at CDAO UK, we spoke with Alteryx CDAO Alan Jacobson about driving ROI with data, putting the ‘transformation’ into digital transformation and why there’s never been a more exciting time to be a data scientist. What would you say have been the greatest changes in the UK’s data and analytics space in recent years? It's an extraordinary period of time.

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Data Science Fails: The Transparency Sweet Spot

DataRobot

Does your organization apply appropriate human resources governance when hiring staff? Large enterprises tend to follow the same basic processes for hiring human staff. First, hiring managers write a job description, including the tasks the position requires and the skills and attributes of a suitable candidate. Job vacancies are posted, sometimes recruiters are also used, and people submit their resumes for consideration.

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Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity

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.

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White-Box vs Black-Box Models: Balancing Interpretability and Accuracy

Dataiku

Data scientists and business leaders building or using machine learning models and AI systems face a serious challenge today -- how to balance interpretability and accuracy stemming from the difference between black-box and white-box models.

Modeling 129
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What Is Data Modeling? Data Modeling Best Practices for Data-Driven Organizations

erwin

What is Data Modeling? Data modeling is a process that enables organizations to discover, design, visualize, standardize and deploy high-quality data assets through an intuitive, graphical interface. Data models provide visualization, create additional metadata and standardize data design across the enterprise. As the value of data and the way it is used by organizations has changed over the years, so too has data modeling.

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Build Your First Text Classification model using PyTorch

Analytics Vidhya

Overview Learn how to perform text classification using PyTorch Understand the key points involved while solving text classification Learn to use Pack Padding feature. The post Build Your First Text Classification model using PyTorch appeared first on Analytics Vidhya.

Modeling 371
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7 Resources to Becoming a Data Engineer

KDnuggets

An estimated 8,650% growth of the volume of Data to 175 zetabytes from 2010 to 2025 has created an enormous need for Data Engineers to build an organization's big data platform to be fast, efficient and scalable.

Big Data 156
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The Big Payoff of Application Analytics

Outdated or absent analytics won’t cut it in today’s data-driven applications – not for your end users, your development team, or your business. That’s what drove the five companies in this e-book to change their approach to analytics. Download this e-book to learn about the unique problems each company faced and how they achieved huge returns beyond expectation by embedding analytics into applications.

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AI Poised to Disrupt the Insurance Industry

Corinium

AI is coming to the disrupt the insurance industry. From Ping An in China to Lemonade in the US, companies across the globe are harnessing AI technologies to drag the sector into the 21st century.

Insurance 243
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Data Science Success – 3 Tips to Consider

Data Science 101

Previously, we looked at some Challenges of Data Science Projects. They can be difficult, and they do not alway succeed. Below are 3 techniques to help your next project become a data science success. 1. Start with a Plan. If you don’t know where you are going, you might end up someplace else. Yogi Berra. This quote by Yogi is humorous, but it is true.

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NRF 2020: Putting the “AI” in Retail

DataRobot

As always too common, retail is often written about as an industry that is in decline and needs triple bypass heart surgery to bring it back to the good old days where stores were packed with excited customers ready to spend their hard-earned cash. It is hard to argue that retail is not in a tough period when so many retailers continue to close unprofitable stores and we see some legendary brands going out of business altogether.

IT 126
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Data Privacy and Why it Matters to Our Customers

Teradata

People want control over their personal data, but are also willing to trade it away for convenience. When does the exploitation of our data become unethical? Read more!

IT 115
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The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Communication

Speaker: David Bard, Principal at VP Product Coaching

In the fast-paced world of digital innovation, success is often accompanied by a multitude of challenges - like the pitfalls lurking at every turn, threatening to derail the most promising projects. But fret not, this webinar is your key to effective product development! Join us for an enlightening session to empower you to lead your team to greater heights.