Wed.Oct 16, 2019

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A Comprehensive Guide to Learn Swift from Scratch for Data Science

Analytics Vidhya

Overview Swift is quickly becoming one of the most powerful and effective languages for data science Swift is quite similar to Python so you’ll. The post A Comprehensive Guide to Learn Swift from Scratch for Data Science appeared first on Analytics Vidhya.

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How to Perfect Your Data Culture Recipe

Corinium

One of the first questions new clients generally ask us is: “How do we maximize the value from our data?”.

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How to Become a (Good) Data Scientist – Beginner Guide

KDnuggets

A guide covering the things you should learn to become a data scientist, including the basics of business intelligence, statistics, programming, and machine learning.

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What is business intelligence? Transforming data into business insights

CIO Business Intelligence

Business intelligence definition. Business intelligence (BI) leverages software and services to transform data into actionable insights that inform an organization’s strategic and tactical business decisions. BI tools access and analyze data sets and present analytical findings in reports, summaries, dashboards, graphs, charts and maps to provide users with detailed intelligence about the state of the business.

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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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The 5 Classification Evaluation Metrics Every Data Scientist Must Know

KDnuggets

This post is about various evaluation metrics and how and when to use them.

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Our Top 20 Most-Read Data and Analytics Research Last Week (to Oct 13)

Andrew White

Click here for an interactive PDF to connect to the most read data and analytics research directly. This list excludes our branded research such as Magic Quadrants etc. Lydia Clougherty Jones new note entered the charts last week at number 14 and in its second week stole the show and moved into top spot!

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KDnuggets™ News 19:n39, Oct 16: Key Ideas in Document Embedding; The problem with metrics is a big problem for AI

KDnuggets

This week on KDnuggets: Beyond Word Embedding: Key Ideas in Document Embedding; The problem with metrics is a big problem for AI; Activation maps for deep learning models in a few lines of code; There is No Such Thing as a Free Lunch; 8 Paths to Getting a Machine Learning Job Interview; and much, much more.

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How to Build a Free Cash Flow Dashboard, with Downloadable Template

Jet Global

Hundreds of different performance indicators exist, but fewer than 20 are considered absolutely essential. They have that designation because companies have discovered a direct correlation between the trajectory of these metrics and their own success or failure. To put it plainly, companies can’t understand how well they’re actually doing without tracking key performance indicators.

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Top KDnuggets tweets, Oct 09-15: #DeepLearning for Natural Language Processing (#NLP) using RNNs & CNNs #KDN Post

KDnuggets

Also: Kannada-MNIST: A new handwritten digits dataset in ML town; Math for Programmers; The 4 Quadrants of Data Science Skills and 7 Principles for Creating a Viral Data Visualization; The Last SQL Guide for Data Analysis You’ll Ever Need.

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On Being Model-driven: Metrics and Monitoring

Domino Data Lab

This article covers a couple of key Machine Learning (ML) vital signs to consider when tracking ML models in production to ensure model reliability, consistency and performance in the future. Many thanks to Don Miner for collaborating with Domino on this article. For additional vital signs and insight beyond what is provided in this article, attend the webinar.

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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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Probability Learning I: Bayes’ Theorem

KDnuggets

Learn about one of the fundamental theorems of probability with an easy everyday example.

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Forrester Does the Math on the ROI of the Alation Data Catalog

Alation

Whether we’re speaking to data analysts or CDOs, data people almost instantly understand the value of the Alation Data Catalog. Faces light up when we describe how Alation helps enterprises find, understand, trust, use and reuse data. The response is usually some form of, “exactly, that’s the problem my company needs to solve!” At some […]. The post Forrester Does the Math on the ROI of the Alation Data Catalog appeared first on Alation.

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Challenges You’ll Face While Building Up Your Data Analytics Team

ScienceSoft

To grow and structure an in-house data analytics team is not always a safe choice. Our Head of Analytics Department shares an example of a typical company that chooses this option, as well as 3 main challenges that they are likely to face in the process.

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Metadata Archiving with Snowflake

CDW Research Hub

The importance of metadata. Metadata is best defined as data that characterizes data. When you query a database, it returns a specific piece of information. Metadata provides the who, what, where, when, why and how of that information. When companies have a properly engineered process to create, store and manage metadata, it benefits all focus areas of the business.

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Driving Business Impact for PMs

Speaker: Jon Harmer, Product Manager for Google Cloud

Move from feature factory to customer outcomes and drive impact in your business! This session will provide you with a comprehensive set of tools to help you develop impactful products by shifting from output-based thinking to outcome-based thinking. You will deepen your understanding of your customers and their needs as well as identifying and de-risking the different kinds of hypotheses built into your roadmap.

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Dedication, Expertise and Opportunity: Highlights from the Partner Xchange at.NEXT Europe

Nutanix

Last week, we wrapped up our fourth.NEXT Europe where we welcomed a record 1300 partner attendees to Copenhagen. While we use our time at this event to share the latest training, news and information from Nutanix; I’m always impressed by what our partners bring, what they teach us, and their passion for what they do.

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How to Plan a Successful BI Project (and Manage it)

Sisense

Blog. A Three-Step Guide for BI Professionals. Launching a new business intelligence initiative or project can be tricky: while we’re staunch believers in agile BI and quick wins, it’s important for both the BI person and the business executives to first align their needs and expectations, and to understand what the organization hopes to achieve. The good news is, an efficient business analyst can get it done in a day or two.

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What is DevOps, and Why Now?

Nutanix

Although the term “DevOps” has been around for a decade now, it seems as though we’ve reached an inflection point in terms of industry-wide acceptance and interest.

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