Wed.Jan 20, 2021

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An Introduction to Hypothesis Testing

Analytics Vidhya

ArticleVideos This article was published as a part of the Data Science Blogathon. Introduction: Many problems require that we decide whether to accept or. The post An Introduction to Hypothesis Testing appeared first on Analytics Vidhya.

Testing 391
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Do You Need a DataOps Dojo?

DataKitchen

As DataOps activity takes root within an enterprise, managers face the question of whether to build centralized or decentralized DataOps capabilities. Centralizing analytics brings it under control but granting analysts free reign is necessary to foster innovation and stay competitive. The beauty of DataOps is that you don’t have to choose between centralization and freedom.

Metrics 243
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A Quick Introduction to K – Nearest Neighbor (KNN) Classification Using Python

Analytics Vidhya

ArticleVideos This article was published as a part of the Data Science Blogathon. Introduction This article concerns one of the supervised ML classification algorithm-KNN(K. The post A Quick Introduction to K – Nearest Neighbor (KNN) Classification Using Python appeared first on Analytics Vidhya.

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Tableau and Salesforce bring New Look to Business Analytics

David Menninger's Analyst Perspectives

Businesses are transforming their organizations, building a data culture and deploying sophisticated analytics more broadly than ever. However, the process of using data and analytics is not always easy. The necessary tools are often separate, but our research shows organizations prefer an integrated environment. In our Data Preparation Benchmark Research , we found that 41% of participants use Analytics and Business Intelligence tools for data preparation.

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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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In-depth Intuition of K-Means Clustering Algorithm in Machine Learning

Analytics Vidhya

ArticleVideos This article was published as a part of the Data Science Blogathon. Introduction Clustering is an unsupervised machine learning technique. It is the. The post In-depth Intuition of K-Means Clustering Algorithm in Machine Learning appeared first on Analytics Vidhya.

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Customer Data Analytics is Critical to the Future of Account-Based Marketing

Smart Data Collective

We have talked at length about the importance of data analytics in the field of marketing. Data analytics offers many useful insights for companies striving to boost their market share. One of the best applications of data analytics is through enhanced account-based marketing. There are a lot of ways to use big data to get a better understanding of a target customer group, which is a vital part of any marketing strategy.

Marketing 140

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Building a Machine Learning Application With Cloudera Data Science Workbench And Operational Database, Part 3: Productionization of ML models

Cloudera

In this last installment, we’ll discuss a demo application that uses PySpark.ML to make a classification model based off of training data stored in both Cloudera’s Operational Database (powered by Apache HBase) and Apache HDFS. Afterwards, this model is then scored and served through a simple Web Application. For more context, this demo is based on concepts discussed in this blog post How to deploy ML models to production.

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Implementation of Attention Mechanism for Caption Generation on Transformers using TensorFlow

Analytics Vidhya

ArticleVideos Overview Learning about the state of the art model that is Transformers. Understand how we can implement Transformers on the already seen image. The post Implementation of Attention Mechanism for Caption Generation on Transformers using TensorFlow appeared first on Analytics Vidhya.

Modeling 292
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Digital Transformation is a Data Journey From Edge to Insight

Cloudera

Digital transformation is a hot topic for all markets and industries as it’s delivering value with explosive growth rates. Consider that Manufacturing’s Industry Internet of Things (IIOT) was valued at $161b with an impressive 25% growth rate, the Connected Car market will be valued at $225b by 2027 with a 17% growth rate, or that in the first three months of 2020, retailers realized ten years of digital sales penetration in just three months.

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Storing Data in the Cloud

TDAN

In the cloud-era, should you store your corporate data in Cosmos DB on Azure, Cloud Spanner on the Google Cloud Platform, or in the Amazon Quantum Ledger? The overwhelming number of options today for storing and managing data in the cloud makes it tough for database experts and architects to design adequate solutions. However, they […].

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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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Starting a Data Career

Data Science 101

Starting a Data Career. If you are just starting a career in data, you will not want to miss this live interview. Join me as I speak with Henrique Senra from Simbiose Ventures to discuss the book, Getting Started with Data. The book was written out of a desire to provide a better future for low-income Brazilians. We will talk about. The story behind the book Tips for getting started with a data career Who the book is for Why now is a great time to learn data Henrique’s data journey Any que

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Make Data Prep Less of a Hassle

Dataiku

The goal of the data preparation phase of the AI life cycle is to wrangle and enrich data as input for model building. Data prep is key for good machine learning models — the more data that is collected and used for model training, the higher the model's accuracy. By preparing data, both features and labels, in an efficient way, models can perform better, increasing the business value of the output.

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Successful Data Virtualisation: more than the right choice of platform

Data Virtualization

Learn in 12 minutes: What makes a strong use case for data virtualisation How to come up with a solid Proof of Concept How to prepare your organisation for data virtualisation You’ll have read all about data virtualisation and you’ve.

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Why Good Data Management Is Essential to Data Analytics

Jet Global

Businesses today have access to a greater volume of data than ever before. Organizations that can effectively leverage data as a strategic asset will inevitably build a competitive advantage and outperform their peers over the long term. In order to achieve that, though, business managers must bring order to the chaotic landscape of multiple data sources and data models.

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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.

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How to configure clients to connect to Apache Kafka Clusters securely – Part 3: PAM authentication

Cloudera

In the previous posts in this series, we have discussed Kerberos and LDAP authentication for Kafka. In this post, we will look into how to configure a Kafka cluster to use a PAM backend instead of an LDAP one. The examples shown here will highlight the authentication-related properties in bold font to differentiate them from other required security properties, as in the example below.

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What is xP&A: A Look at the Evolution of Financial Planning

Jet Global

Financial planning and analysis (FP&A) is recognized as an essential tool for driving business success. At a time when agility has taken center stage, though, FP&A is giving way to the new discipline of extended planning and analysis (xP&A). Why? Because in a world where agility is paramount, actionable real-time feedback is essential. The Origin of xP&A.

Finance 52