Sat.Apr 16, 2022 - Fri.Apr 22, 2022

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The 8 Basic Statistics Concepts for Data Science

KDnuggets

Understanding the fundamentals of statistics is a core capability for becoming a Data Scientist. Review these essential ideas that will be pervasive in your work and raise your expertise in the field.

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What to Do After Deploying Your Model to Production?

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Congratulations, you have deployed a model to production; it is an achievement for you and your team! In a normal software engineering development cycle, you would now sit back and relax; however, in the machine learning development cycle, deployment to production is just about […].

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Pros and cons of AI: Is Artificial Intelligence suitable for you?

Dataconomy

We searched the risks and benefits of artificial intelligence and tried to decide is it evil or not? Humans have long desired to construct machines that can make decisions. It was thought of as a possibility that seemed too good to be true for a long time, and it was.

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Changing Who We Spend Time with as We Get Older

FlowingData

In high school, we spend most of our days with friends and immediate family. Then we get older and get jobs, get married, and grow our own families to spend more time with co-workers, spouses, and kids. Here’s how things change, based on a decade of data from the American Time Use Survey, from age 15 to 80. Read More.

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Navigating the Future: Generative AI, Application Analytics, and Data

Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?

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How to Determine the Best Fitting Data Distribution Using Python

KDnuggets

Approaches to data sampling, modeling, and analysis can vary based on the distribution of your data, and so determining the best fit theoretical distribution can be an essential step in your data exploration process.

Python 398
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Track Your Trip Through an OBD system Using Python

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Introduction Most drivers nowadays are quite familiar with all the indicators on their car dashboard. In more detail, each indicator is a part of an information signal that constantly works to monitor the car’s health status, which can be diagnosed through an OBD […]. The post Track Your Trip Through an OBD system Using Python appeared first on Analytics Vidhya.

Python 380

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7 Data Lineage Tool Tips For Preventing Human Error in Data Processing

Smart Data Collective

Errors in data entry might have serious effects if they are not discovered quickly. Human mistake is the most common cause of data entry errors. Since typical data entry errors may be minimized with the right steps, there are numerous data lineage tool strategies that a corporation can follow. The steps organizations can take to reduce mistakes in their firm for a smooth process of business activities will be discussed in this blog.

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Top YouTube Channels for Learning Data Science

KDnuggets

YouTube has become an important element in people's self-development and increase of knowledge. Check out this list of YouTube channels that offer Data Science learning.

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Determining the Market Price of Old Vehicles Using Python

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Selling old stuff had always been a hassle in earlier times. No matter how good an item might have been, finding a buyer and getting the appropriate Market price was always a challenge. One was only able to sell items within a […]. The post Determining the Market Price of Old Vehicles Using Python appeared first on Analytics Vidhya.

Python 309
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Your choice of XaaS provider can make or break your business

Dataconomy

Anything as a Service (XaaS) is a term that refers to a broad category of cloud computing and remote access services. Anything as a service is an all-encompassing phrase that refers to providing anything as a service. Businesses can pay a monthly subscription to a managed service provider to ensure.

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Get Better Network Graphs & Save Analysts Time

Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

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How to Measure and Mitigate Position Bias

Eugene Yan

Introducing randomness and/or learning from inherent randomness to mitigate position bias.

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A Brief Introduction to Papers With Code

KDnuggets

One-stop shop to learn about state-of-the-art research papers with access to open-source resources including machine learning models, datasets, methods, evaluation tables, and code.

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Predicting SONAR Rocks Against Mines with ML

Analytics Vidhya

This article was published as a part of the Machine Learning. Introduction This article is about predicting SONAR rocks against Mines with the help of Machine Learning. SONAR is an abbreviated form of Sound Navigation and Ranging. It uses sound waves to detect objects underwater. Machine learning-based tactics, and deep learning-based approaches have applications in […].

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When will DaaS get its big break?

Dataconomy

Data as a service (DaaS) is a data management approach that uses the cloud to offer storage, integration, processing, and analytics capabilities through a network connection. The DaaS architecture is based on a cloud-based system that supports Web services and service-oriented architecture (SOA).

Analytics 168
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Understanding User Needs and Satisfying Them

Speaker: Scott Sehlhorst

We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.

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5 Great Tips for Using Data Analytics for Website UX

Smart Data Collective

We have pointed out in the past that big data offers a number of benefits for online commerce. One of the most important benefits of data analytics pertains to optimizing websites for a good user experience. User experience optimization (UX) is becoming more important than ever. One study found that the ROI of UX strategies is 9,900%. As more companies realize the importance of offering a stellar web experience, they will invest in big data as part of their UX strategies.

Analytics 115
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Machine Learning Books You Need To Read In 2022

KDnuggets

I have a list of Machine Learning books you need to read in 2022; beginner, intermediate, expert, and for everybody.

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The DataHour: Artificial Intelligence in Retail

Analytics Vidhya

Dear Readers, We are back with another episode of our flagship learning series on data analytics, “The DataHour”. In this edition, Dr. Shantha Mohan, Mentor and Project Guide at Carnegie Mellon University’s Integrated Innovation Institute, will guide you through “Artificial Intelligence in Retail” applications. Machine learning plays a vital role in Retail Management, primarily due […].

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Good news for data scrapers! US appeals court rules out that it is legal for public data

Dataconomy

Public data scraping is not a problem according to the US Court of Appeals for the Ninth Circuit. The court recently ruled that data scraping from a public website does not constitute computer fraud under the Computer Fraud and Abuse Act (CFAA). In 2017, HiQ filed a lawsuit against LinkedIn’s.

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Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

Speaker: Timothy Chan, PhD., Head of Data Science

Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.

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Preserving Data Quality is Critical for Leveraging Analytics with Amazon PPC

Smart Data Collective

Amazon is without a doubt the largest retailer in the world. More businesses than ever are turning to Amazon to expand their reach. Unfortunately, the Amazon marketplace has become extraordinarily competitive in recent years. Companies that utilize data analytics to make the most of their business model will have an easier time succeeding with Amazon.

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Deploy a Machine Learning Web App with Heroku

KDnuggets

In this article, you will learn to deploy a fully functional ML web application in under 3 minutes.

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An Overview of HDFS: NameNodes and DataNodes

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Modern applications and products deal with large amounts of data. The quantity of data being processed and utilised in modern times is enormous. So, the question arises? How to manage large files and data. Data size soon outgrows a machine’s storage limit […].

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Green computing is the key to sustainable future

Dataconomy

Green computing is a method for making efficient and sustainable use of computers. It includes producing, designing, discarding, and responsibly utilizing computers and related equipment with minimal to no adverse side effects on the environment. Going green is a growing trend gaining popularity as the preferred approach to doing things.

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How Embedded Analytics Gets You to Market Faster with a SAAS Offering

Start-ups & SMBs launching products quickly must bundle dashboards, reports, & self-service analytics into apps. Customers expect rapid value from your product (time-to-value), data security, and access to advanced capabilities. Traditional Business Intelligence (BI) tools can provide valuable data analysis capabilities, but they have a barrier to entry that can stop small and midsize businesses from capitalizing on them.

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Tax services want your data

FlowingData

Taxes are due today in the U.S. (yay). Geoffrey A. Fowler for The Washington Post on the part when tax services like TurboTax and H&R Block ask for your data : What he discovered is a little-discussed evolution of the tax-prep software industry from mere processors of returns to profiteers of personal data. It’s the Facebook-ization of personal finance.

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A Community for Synthetic Data is Here and This is Why We Need It

KDnuggets

The first open-source platform for synthetic data is here to help educate the broader machine learning and computer vision communities on the emerging technology.

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Getting Started with PySpark Using Python

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction In this article, we will be getting our hands dirty with PySpark using Python and understand how to get started with data preprocessing using PySpark. This particular article’s whole attention is to get to know how PySpark can help in the data cleaning process […].

Python 265
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Quantum machine learning: Search for an impact

Dataconomy

Quantum Machine Learning (QML) is a young theoretical research discipline exploring the interplay of quantum computing and machine learning approaches. In the last couple of years, several experiments demonstrated the potential advantages of quantum computing for machine learning. The overall goal of Quantum Machine Learning is to make things move.

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Manufacturing Sustainability Surge: Your Guide to Data-Driven Energy Optimization & Decarbonization

Speaker: Kevin Kai Wong, President of Emergent Energy Solutions

In today's industrial landscape, the pursuit of sustainable energy optimization and decarbonization has become paramount. Manufacturing corporations across the U.S. are facing the urgent need to align with decarbonization goals while enhancing efficiency and productivity. Unfortunately, the lack of comprehensive energy data poses a significant challenge for manufacturing managers striving to meet their targets.

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VPNs Are Essential Data Protection Tools for Home Offices

Smart Data Collective

Data protection is becoming more important than ever. The risk of cyberattacks has risen sharply, as more people are working from home even as the pandemic subsides. A recent report showed data breaches jumped 68% in 2021 to the highest level ever. That figure is likely to rise even more in the coming months. One of the most unusual consequences of the war in Ukraine is the increased cybersecurity risk, with one report suggesting that Russia is preparing to launch destructive attacks on the US a

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How Artificial Intelligence Can Transform Data Integration

KDnuggets

Let's take a look at what goes into creating a foundation for enterprise-wide data intelligence and how AI and ML can permanently transform data integration.

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What is MySQL Partitions and its Types?

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction In today’s data-driven world, organisations work with massive datasets and leverage some aspects of this data for their day-to-day operations. Data professionals in such companies prefer to have small partitions of data as it allows them to analyse and manipulate information without any hassle. […].

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A new neural network is able to read tree heights using satellite images

Dataconomy

The researchers at ETH Zurich have developed the first high-resolution global vegetation height map for 2020 from satellite photos using an artificial neural network. This map may be vital in the fight against climate change and species loss, as well as in designing long-term sustainable development plans. Researchers from ETH.

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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.