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 306
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How To Succeed As a DataOps Engineer

DataKitchen

What makes an effective DataOps Engineer? A DataOps Engineer shepherds process flows across complex corporate structures. Organizations have changed significantly over the last number of years and even more dramatically over the previous 12 months, with the sharp increase in remote work. A DataOps engineer runs toward errors. You might ask what that means.

Testing 246
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15 Modern Use Cases for Enterprise Business Intelligence

Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?

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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 398
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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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11 Data Presentation Tips and Resources to Deliver More Client Value

Juice Analytics

Whether you are a consultant, marketer, researcher, or financial analyst…a big part of your job is presenting data. It takes a special combination of skills to articulate your insights and support them with effectively visualized data. You need to be part salesperson, part data analyst, and part author. We’ve collected 11 of the most useful tips and resources to help you improve how you present data.

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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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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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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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8 Steps to Transformation at Speed & Scale – Your Guide to Deploying StratOps

📌Is your Data & AI transformation struggling to really impact the business? Discover the game-changing StratOps approach that: Bridges the Gap : Connect your Data & AI strategy to your operating model, to ensure alignment at every level. Prioritizes Outcomes : Focuses on concrete business outcomes from day one, rather than capabilities in isolation.

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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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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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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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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 128
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Marketing Operations in 2025: A New Framework for Success

Speaker: Mike Rizzo, Founder & CEO, MarketingOps.com and Darrell Alfonso, Director of Marketing Strategy and Operations, Indeed.com

Though rarely in the spotlight, marketing operations are the backbone of the efficiency, scalability, and alignment that define top-performing marketing teams. In this exclusive webinar led by industry visionaries Mike Rizzo and Darrell Alfonso, we’re giving marketing operations the recognition they deserve! We will dive into the 7 P Model —a powerful framework designed to assess and optimize your marketing operations function.

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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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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 392
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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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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 116
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Prepare Now: 2025s Must-Know Trends For Product And Data Leaders

Speaker: Jay Allardyce, Deepak Vittal, and Terrence Sheflin

As we look ahead to 2025, business intelligence and data analytics are set to play pivotal roles in shaping success. Organizations are already starting to face a host of transformative trends as the year comes to a close, including the integration of AI in data analytics, an increased emphasis on real-time data insights, and the growing importance of user experience in BI solutions.

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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.

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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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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 151
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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 106
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The Ultimate Guide To Data-Driven Construction: Optimize Projects, Reduce Risks, & Boost Innovation

Speaker: Donna Laquidara-Carr, PhD, LEED AP, Industry Insights Research Director at Dodge Construction Network

In today’s construction market, owners, construction managers, and contractors must navigate increasing challenges, from cost management to project delays. Fortunately, digital tools now offer valuable insights to help mitigate these risks. However, the sheer volume of tools and the complexity of leveraging their data effectively can be daunting. That’s where data-driven construction comes in.

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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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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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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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Data Privacy and Internet Safety Tips for College Students

Smart Data Collective

Data privacy concerns have become greater than ever in recent years. One recent study from the University of Maryland found that there is a data breach every 39 seconds. The threat of data breaches has become a lot greater in recent years as more businesses and consumers become dependent on big data. The proliferation of big data has made digital privacy concerns much more significant.

Big Data 102
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Launching LLM-Based Products: From Concept to Cash in 90 Days

Speaker: Christophe Louvion, Chief Product & Technology Officer of NRC Health and Tony Karrer, CTO at Aggregage

Christophe Louvion, Chief Product & Technology Officer of NRC Health, is here to take us through how he guided his company's recent experience of getting from concept to launch and sales of products within 90 days. In this exclusive webinar, Christophe will cover key aspects of his journey, including: LLM Development & Quick Wins 🤖 Understand how LLMs differ from traditional software, identifying opportunities for rapid development and deployment.

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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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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 384
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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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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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Data Modeling for Direct Mail: Boosting Multi-Channel Reach and Response

Speaker: Jesse Simms, VP at Giant Partners

This new, thought-provoking webinar will explore how even incremental efforts and investments in your data can have a tremendous impact on your direct mail and multi-channel marketing campaign results! Industry expert Jesse Simms, VP at Giant Partners, will share real-life case studies and best practices from client direct mail and digital campaigns where data modeling strategies pinpointed audience members, increasing their propensity to respond – and buy.