Sat.Jul 04, 2020 - Fri.Jul 10, 2020

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Statistics for Data Science: What is Skewness and Why is it Important?

Analytics Vidhya

Overview Skewness is a key statistics concept you must know in the data science and analytics fields Learn what is skewness, the formula for. The post Statistics for Data Science: What is Skewness and Why is it Important? appeared first on Analytics Vidhya.

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No Keys to the Kingdom: New Single Sign-On Algorithm Provides Superior Privacy

Dataconomy

Researchers develop cryptographic scheme that completely hides your personal information from third parties when using single sign-on systems. Single sign-on systems (SSOs) allow us to login to multiple websites and applications using a single username and password combination. But these are third party systems usually handled by Big Tech companies.

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How to Update a GitHub Profile README Automatically

Eugene Yan

I wanted to add my recent writing to my GitHub Profile README but was too lazy to do manual updates.

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If then do A else do B?—?ifelse function in R & Exploratory

learn data science

If then do A else do B — ifelse function in R & Exploratory Most likely you have used or heard about ‘ifelse’ function before. It’s everywhere such as Excel, database, etc. And of course, it is in R, which means you can use it in Exploratory as well. I’m going to talk about how you can use the ifelse function in Exploratory. Basics The ifelse function takes 3 arguments. ifelse(a condition, a return value when the condition is TRUE, a return value when the condition is FALSE) Example 1 — Grea

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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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Time Series Forecasting using Microsoft Power BI

Analytics Vidhya

Introduction Time series forecasting is a really important area of Machine Learning as it gives you the ability to “see” ahead of time and. The post Time Series Forecasting using Microsoft Power BI appeared first on Analytics Vidhya.

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How can governments harness the power of volunteering

Dataconomy

Assistant Volunteer, a project of Nable Solutions, was born during the HackCoronaGreece online hackathon to better coordinate the efforts of volunteers. Today, Assistant Volunteer’s platform is part of the Greek Ministry of Health’s official response to eradicating the pandemic. This year, online hackathons have proven to be a great source. The post How can governments harness the power of volunteering appeared first on Dataconomy.

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Calculating Customer Lifetime Value (CLV) in 3 Ways?—?Guts, Churn Rates, Survival Rates

learn data science

Photo by Austin Distel on Unsplash Calculating Customer Lifetime Value (CLV) in 3 Ways — Guts, Churn Rates, Survival Rates The survival Algorithm (Kaplan-Meyer) can give you a better estimate for CLV. Understanding the Customer Lifetime Value is critical for subscription businesses including SaaS (Software-as-a-Service). Unlike, transactional businesses, the subscription business accumulate revenues from the same customer over time.

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Deploy an Image Classification Model Using Flask

Analytics Vidhya

Overview Get an overview of PyTorch and Flask Learn to build an image classification model in PyTorch Learn how to deploy the model using. The post Deploy an Image Classification Model Using Flask appeared first on Analytics Vidhya.

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How to approach almost any real-world NLP problem

Depends on the Definition

This time, I’m going to talk about how to approach general NLP problems. But we’re not going to look at the standard tips which are tosed around on the internet, for example on platforms like kaggle.

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The 85% Rule: When Giving It Your 100% Gets You Less than 85%

Eugene Yan

I thought giving it my all led to maximum outcomes; then I learnt about the 85% rule.

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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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Top 10 Applications of Natural Language Processing (NLP)

Analytics Vidhya

Introduction Natural Language Processing is among the hottest topic in the field of data science. Companies are putting tons of money into research in. The post Top 10 Applications of Natural Language Processing (NLP) appeared first on Analytics Vidhya.

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Difference between R-squared and Adjusted R-squared

Analytics Vidhya

Overview Understand the concept of R-squared and Adjusted R-Squared Get to know the key differences between R-Squared and Adjusted R-squared Introduction When I. The post Difference between R-squared and Adjusted R-squared appeared first on Analytics Vidhya.

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Build Text Categorization Model with Spark NLP

Analytics Vidhya

Overview Setting up John Snow labs Spark-NLP on AWS EMR and using the library to perform a simple text categorization of BBC articles. Introduction. The post Build Text Categorization Model with Spark NLP appeared first on Analytics Vidhya.

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Tableau Tip: Visualize a Single Value Against Others

Analytics Vidhya

Introduction How often have we all tried to compare a value against a range, with unsatisfying results? Excel is the most common tool for. The post Tableau Tip: Visualize a Single Value Against Others appeared first on Analytics Vidhya.

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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.