Sat.Nov 20, 2021 - Fri.Nov 26, 2021

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Using Data Visualization to Explore the Human Space Race!

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Humankind has always looked up to the stars. Since the dawn of civilization, we have mapped constellations, named planets after Gods and so on. We have seen signs and visions in celestial bodies. In the previous century, we finally had the technology to […]. The post Using Data Visualization to Explore the Human Space Race!

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Using AI to Improve Demand Forecasts for the Peak Season

Dataiku

This is a blog post from our friends at Aimpoint Digital. Aimpoint Digital is a leading advanced analytics, data science, and engineering services firm that operates as the trusted advisor for companies looking to extract tangible value from data.

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Analytic Ops: The Last Mile of Data Ops

David Menninger's Analyst Perspectives

Organizations have become more agile and responsive, in part, as a result of being more agile with their information technology. Adopting a DevOps approach to application deployment has allowed organizations to deploy new and revised applications more quickly. DataOps is enabling organizations to be more agile in their data processes. As organizations are embracing artificial intelligence (AI) and machine learning (ML), they are recognizing the need to adopt MLOps.

Analytics 277
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Top Stories, Nov 15-21: 19 Data Science Project Ideas for Beginners

KDnuggets

Also: How I Redesigned over 100 ETL into ELT Data Pipelines; Where NLP is heading; Don’t Waste Time Building Your Data Science Network; Data Scientists: How to Sell Your Project and Yourself.

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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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Performing Time Series Analysis using ARIMA Model in R

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Welcome to the World of Time Series Analysis! From this article, you will learn how to perform time series analysis using the ARIMA model (with code!). The dataset used in this article can be downloaded here. The usage time series data consist of the […]. The post Performing Time Series Analysis using ARIMA Model in R appeared first on Analytics Vidhya.

Modeling 400
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How to Utilize Artificial Intelligence in Your eCommerce SEO Strategy

Smart Data Collective

If you have not lived under a rock for several years, you have undoubtedly heard about artificial intelligence (AI). However, how might artificial intelligence be used in e-commerce operations? Artificial intelligence (AI) is starting to fill every facet of our daily lives. For example, self-checkout cash registers, airport security checks, and other automated processes all use artificial intelligence to some degree.

Strategy 142

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Most Common SQL Mistakes on Data Science Interviews

KDnuggets

Sure, we all make mistakes -- which can be a bit more painful when we are trying to get hired -- so check out these typical errors applicants make while answering SQL questions during data science interviews.

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Understanding K-means Clustering in Machine Learning(With Examples)

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction With the rising use of the Internet in today’s society, the quantity of data created is incomprehensibly huge. Even though the nature of individual data is straightforward, the sheer amount of data to be analyzed makes processing difficult for even computers. To […].

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How BI Can Help Enterprises Overcome The Effects Of The Pandemic

Smart Data Collective

It’s been almost two years since the COVID-19 pandemic started, and now we have enough information to assume that most enterprises weren’t prepared for the crisis. Although teams had vast amounts of data and powerful analytic tools at their fingertips, the pandemic still caught most organizations off guard. As a result, most enterprise executives had to cut their plans and initiatives.

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Ten Things I’ve Learned in 20 Years in Data and Analytics

Teradata

Teradata's Martin Willcox recently passed 17 years at Teradata and a quarter of a century in the industry. Here are the ten things he's learned about data analytics in those 20-odd years.

Analytics 111
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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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Top 4 Data Integration Tools for Modern Enterprises

KDnuggets

Maintaining a centralized data repository can simplify your business intelligence initiatives. Here are four data integration tools that can make data more valuable for modern enterprises.

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Loan Risk Analysis with Supervised Machine Learning Classification

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction to Classification Algorithms In this article, we shall analyze loan risk using 2 different supervised learning classification algorithms. These algorithms are decision trees and random forests. At the outset, the basic features and the concepts involved would be discussed followed by a […].

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How to Make the Most of Big Data in the Fitness Industry

Smart Data Collective

Big data has been a powerful force of change in the fitness industry. More fitness companies are using data analytics, AI and other technology to better understand their customers, improve their operating margins and make other changes to adapt to new trends. If you are running a fitness business, then you can’t afford to overlook the importance of big data.

Big Data 124
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Propensity Matching to Create an Unbiased Training Set

Dataiku

As data scientists, we work on solving various business problems to make predictions on a given population where there isn’t a very easy (and cost effective) way of knowing if you have a true representative training set for the unlabeled population. For this blog, I am trying to predict the census tracts in the U.S. that will have above average counts of people who suffer from diabetes based on their social determinants of health.

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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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On-Device Deep Learning: PyTorch Mobile and TensorFlow Lite

KDnuggets

PyTorch and TensorFlow are the two leading AI/ML Frameworks. In this article, we take a look at their on-device counterparts PyTorch Mobile and TensorFlow Lite and examine them more deeply from the perspective of someone who wishes to develop and deploy models for use on mobile platforms.

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Building an end-to-end Polynomial Regression Model in R

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Overview. Regression analysis is used to solve problems of prediction based on data statistical parameters. In this article, we will look at the use of a polynomial regression model on a simple example using real statistic data. We will analyze the relationship between […]. The post Building an end-to-end Polynomial Regression Model in R appeared first on Analytics Vidhya.

Modeling 400
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5 Ways Data Analytics Sets a New Standard for Revenue Marketing

Smart Data Collective

With the “big data” or insurmountable, high-volume amount of information, data analytics plays a crucial role in many business aspects, including revenue marketing. Data analytics refers to the systematic computational analysis of statistics or data. It lays a core foundation necessary for business planning. Data analytics make up the relevant key performance indicators ( KPIs ) or metrics necessary for a business to create various sales and marketing strategies.

Marketing 108
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In AI we Trust? Why we Need to Talk about Ethics and Governance (part 1 of 2)

Cloudera

Advances in the performance and capability of Artificial Intelligence (AI) algorithms has led to a significant increase in adoption in recent years. In a February 2021 report by IDC, they estimate that world-wide revenues from AI will grow by 16.4% in 2021 to USD $327 billion. Furthermore, AI adoption is becoming increasingly widespread and not just concentrated within a small number of organisations.

Insurance 100
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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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Dask DataFrame is not Pandas

KDnuggets

This article is the second article of an ongoing series on using Dask in practice. Each article in this series will be simple enough for beginners, but provide useful tips for real work. The next article in the series is about parallelizing for loops, and other embarrassingly parallel operations with dask.delayed.

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Getting Started with Data Analysis using Power BI

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. What is Power BI? Microsoft‘s business analytics product, Power BI, delivers interactive data visualization BI capabilities that allow users to see and share data and insights throughout their organisation. Power BI provides insight data by using data interactively and exploring it by visualizations. […].

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From Disparate Data to Visualized Knowledge Part II: Scaling on Both Ends

Ontotext

In our previous blog post of the series, we covered how to ingest data from different sources into GraphDB , validate it and infer new knowledge from the extant facts. Today we’ll deal with the big issue of scaling, tackling it on two sides: what happens when you have more and faster sources of data? And what happens when you want more processing power and more resilient and available data?

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Does AI Decision-Making Drive Your Business?

DataRobot

It should. By the year 2030, AI will deliver economic growth of $15.7 trillion, according to PwC Research. Does your business need: To target its customers more precisely? Reduce its operational costs? Develop exciting new products? Increase the reliability of its supply chain? Whatever its requirements, applying data-driven AI strategies can help. Let’s look at some examples to see how AI applications can aid any organization, large or small. .

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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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Accelerating AI with MLOps

KDnuggets

Companies are racing to use AI, but despite its vast potential, most AI projects fail. Examining and resolving operational issues upfront can help AI initiatives reach their full potential.

IT 146
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Pattern Library for Natural Language Processing in Python

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. There is a wide variety of data available on the internet. Data can be numbers, images, text, audio, and son. The vast amount of data available online and generated is vast. The vast amount of text data can be overwhelming to analyze and […]. The post Pattern Library for Natural Language Processing in Python appeared first on Analytics Vidhya.

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Six Factors to Consider When Choosing a Corporate Performance Management Software Vendor

Jet Global

It’s always a good idea to invest in improving your organization’s capacity to consume, digest, and analyze information quickly and efficiently. For many organizations, though, the end of the year is an especially good time to roll out new financial intelligence initiatives. As budgeting season sets in and the finance team ramps up for the end-of-year closing process, many finance leaders find that it’s a natural time to start looking at corporate performance management (CPM), sometimes referred

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Benefits of Using Data Analytics in Equipment Financing

Smart Data Collective

Data analytics technology has touched on virtually every element of our lives. More companies are using big data to address some of their biggest concerns. Securing financing is a huge example. Data analytics technology is helping more companies get the financing that they need for a variety of purposes. One of the most important benefits of big data involves getting financing for new equipment.

Finance 98
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Reimagined: Building Products with Generative AI

“Reimagined: Building Products with Generative AI” is an extensive guide for integrating generative AI into product strategy and careers featuring over 150 real-world examples, 30 case studies, and 20+ frameworks, and endorsed by over 20 leading AI and product executives, inventors, entrepreneurs, and researchers.

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A Spreadsheet that Generates Python: The Mito JupyterLab Extension

KDnuggets

You can call Mito into your Jupyter Environment and each edit you make will generate the equivalent Python in the code cell below.

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KNIME Tutorial – A Friendly Introduction to Components using the KNIME Analytics Platform

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. In the last article A Friendly Introduction to KNIME Analytics Platform I provided a brief insight into the open-source software KNIME Analytics Platform and what it is capable of. With the help of a customer segmentation example, I showed the general functions of […]. The post KNIME Tutorial – A Friendly Introduction to Components using the KNIME Analytics Platform appeared first on Analytics Vidhya.

Analytics 395
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What Everybody Ought To Know About BI For Marketing

FineReport

The digital marketing era is coming. Marketing has become a data-driven industry that requires fast data processing and intuitive demonstration. Social media, email, web-based advertising brings numberless data to companies. Marketers have to see to it that these data are fully made use of and can add positive value to their work. However, with the aid of BI, there are several ways to achieve these goals.

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European cloud BI implementations are driven by flexibility and scalability

BI-Survey

E uropean cloud BI implementations are much more driven by flexibility and scalability than North American ones, which focus more on cost and deployment speed. In terms of actual benefits, European companies find greater benefits in outsourcing hardware and lower administration costs, while North American firms benefit most from scalability and speed of deployment.

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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.