Mon.Jan 17, 2022

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Data Quality: The Good, The Bad, and The Ugly

KDnuggets

Incorrect or unclean data leads to false conclusions. The time you take to understand and clean the data is vital to the outcome and quality of the results. Data Quality always takes the win against complex fancy algorithms.

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Roadmap to Master NLP in 2022

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction A few days ago, I came across a question on “Quora” that boiled down to: “How can I learn Natural Language Processing in just only four months?” Then I began to write a brief response. Still, it quickly snowballed into a detailed explanation […].

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A busy year ahead in low-code and no-code development

DataKitchen

The post A busy year ahead in low-code and no-code development first appeared on DataKitchen.

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Layers of the Data Platform Architecture

Analytics Vidhya

Overview In this article, I will walk you through the layers of the Data Platform Architecture. First of all, let’s understand what is a Layer, a layer represents a serviceable part that performs a precise job or set of tasks in the data platform. The different layers of the data platform architecture that we are […]. The post Layers of the Data Platform Architecture appeared first on Analytics Vidhya.

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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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Models Are Rarely Deployed: An Industry-wide Failure in Machine Learning Leadership

KDnuggets

In this article, Eric Siegel summarizes the recent KDnuggets poll results and argues that the pervasive failure of ML projects comes from a lack of prudent leadership. He also argues that MLops is not the fundamental missing ingredient – instead, an effective ML leadership practice must be the dog that wags the model-integration tail.

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A Comprehensive Guide on Kubernetes

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Image-1 Introduction Today, In this guide, we will dive in to learn about Kubernetes and use it to deploy and manage containers at scale. Container and microservice architecture had used more to create modern apps. Kubernetes is open-source software that allows you to deploy […].

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Sentiment Analysis with LSTM

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Sentiment Analysis is an NLP application that identifies a text corpus’s emotional or sentimental tone or opinion. Usually, emotions or attitudes towards a topic can be positive, negative, or neutral. This makes sentiment analysis a text classification task. Examples of positive, negative, and […].

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The Role and Importance of Data Collection in Healthcare

Smart Data Collective

Did you know that global businesses are expected to spend $274 billion on big data this year? That figure is projected to grow at a rapid pace for years to come. The healthcare sector, in particular, has discovered a number of benefits of leveraging data technology. There are a lot of reasons that big data can be useful for healthcare businesses of all sizes.

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Microsoft Malware Detection

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction As a part of writing a blog on the ML or DS topic, I selected a problem statement from Kaggle which is Microsoft malware detection. Here this blog explains how to solve the problem from scratch. In this blog I will explain to […]. The post Microsoft Malware Detection appeared first on Analytics Vidhya.

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Data Scientist vs Data Analyst vs Data Engineer

KDnuggets

In this article, I will describe three of the most promising career options within the data industry? — data analytics, data science, and data engineering.

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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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A Guide to Understand Machine Learning Pipeline with Case Study

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Machine learning is one of the most advancing technologies in Computer Science in the present era. A lot of Researchers, Academicians, and Industrialists are investing their efforts to innovate in this field. If you find the process of training machines to learn to […].

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KDnuggets Top Blogs Rewards for December 2021

KDnuggets

The December blogs that won KDnuggets Rewards include: Write Clean Python Code Using Pipes; Building a solid data team; How to Get Certified as a Data Scientist; 3 Tools to Track and Visualize the Execution of Your Python Code; and more.

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Logistic Regression: An Introductory Note

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Linear regression maps a vector x to a scalar y. If we can squash the Linear regression output in the range 0 to 1, it can be interpreted as a probability. We can have a classifier that gives the class label’s probability for […]. The post Logistic Regression: An Introductory Note appeared first on Analytics Vidhya.

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From the Lab to the Enterprise: Getting Your Work Adopted Across the Organization

Dataiku

The need to be understood is not only a core human trait, but it's also an important part of a data scientist's responsibilities. Models do not exist in a vacuum, and “ analytics products ” have no intrinsic value on their own — their purpose and potential are only fulfilled when they are consumed by people and applied in organizations.

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Driving Business Impact for PMs

Speaker: Jon Harmer, Product Manager for Google Cloud

Move from feature factory to customer outcomes and drive impact in your business! This session will provide you with a comprehensive set of tools to help you develop impactful products by shifting from output-based thinking to outcome-based thinking. You will deepen your understanding of your customers and their needs as well as identifying and de-risking the different kinds of hypotheses built into your roadmap.

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Human Dignity in Artificial Intelligence: Algorethics

Jen Stirrup

Given the persistent drive for human dignity at the forefront of all human action, computer ethics has been around almost as long as computers themselves. Beginning with Norbert Weiner’s Cybernetics (1948) and The Human Use of Human Beings (1950), Weiner saw in the emerging technology of cybernetics an opportunity, or a destiny, to affect every major aspect of life.

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Large Pharma Achieves 5X Productivity Gain With DataOps Process Hub

DataKitchen

The Challenge. A large pharmaceutical Business Analytics (BA) team struggled to provide timely analytical insight to its business customers. The company invested significant effort into managing lists of potential prescribers for certain drugs and treatments. However, the BA team spent most of its time overcoming error-prone data and managing fragile and unreliable analytics pipelines. .

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Determining Business Intelligence Requirements That Will Delight Your Customers

Jet Global

Choosing the right BI solution involves thoroughly evaluating the technology, understanding the expertise offered by the vendor, and implementing a process to ensure success. It also means keeping your customers top of mind as you determine requirements. These five BI requirements (both technical and non-technical) are critical to any analytics implementation and common to most evaluations. 1.

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What Makes a Useful Data Story? 5 Questions to Ask 

Depict Data Studio

Ready to tell a story with data? Here’s my definition of data storytelling , in case you missed the previous blog post. Great! Let’s remove the guesswork from our graphs. The next step is to figure out which message we’ll highlight. We can’t visualization everything—that dilutes the power of our graph. What Makes a Useful Data Story? 5 Questions to Ask.

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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