DataRobot Blog

DECEMBER 6, 2022

Savvy data scientists are already applying artificial intelligence and machine learning to accelerate the scope and scale of data-driven decisions in strategic organizations. These data science teams are seeing tremendous results—millions of dollars saved, new customers acquired, and new innovations that create a competitive advantage.

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Analytics Vidhya

MAY 23, 2023

Introduction Join upcoming DataHour sessions for valuable insights and knowledge on data-tech careers. Topics include Prompt Engineering, LlamaIndex, QA systems, ChatGPT in Python, and Excel for Statistics. This blog post introduces the series, covering various subjects in data science and its applications across industries.

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datapine

AUGUST 14, 2019

Data science has become an extremely rewarding career choice for people interested in extracting, manipulating, and generating insights out of large volumes of data. To fully leverage the power of data science, scientists often need to obtain skills in databases, statistical programming tools, and data visualizations.

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IBM Big Data Hub

SEPTEMBER 19, 2023

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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datapine

MAY 14, 2019

By gaining the ability to understand, quantify, and leverage the power of online data analysis to your advantage, you will gain a wealth of invaluable insights that will help your business flourish. The ever-evolving, ever-expanding discipline of data science is relevant to almost every sector or industry imaginable – on a global scale.

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Domino Data Lab

JULY 28, 2021

Data science is an exciting, interdisciplinary field that is revolutionizing the way companies approach every facet of their business. Data Science — A Venn Diagram of Skills. Data science encapsulates both old and new, traditional and cutting-edge. 3 Components of Data Science Skills.

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IBM Big Data Hub

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? This post will dive deeper into the nuances of each field.

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TDAN

JUNE 1, 2021

Data is critical for any business as it helps them make decisions based on trends, statistical numbers and facts. Due to this importance of data, data science as a multi-disciplinary field developed. These days, data science is the […].

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Rocket-Powered Data Science

JULY 6, 2023

You can find my results on my Medium blog site. It is merely a very large statistical model that provides the most likely sequence of words in response to a prompt. Oh, by the way, I asked the generative AI at Stable Diffusion to create some images to go with my short story (which you can find on my Medium blog ). Guess what?

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DataRobot Blog

JULY 28, 2022

This article presents a case study of how DataRobot was able to achieve high accuracy and low cost by actually using techniques learned through Data Science Competitions in the process of solving a DataRobot customer’s problem. Sensor Data Analysis Examples. The Best Way to Achieve Both Accuracy and Cost Control.

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DataRobot Blog

MARCH 16, 2023

Traditionally, developing appropriate data science code and interpreting the results to solve a use-case is manually done by data scientists. The integration allows you to generate intelligent data science code that reflects your use case. Data scientists still need to review and evaluate these results.

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IBM Big Data Hub

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects.

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Domino Data Lab

JULY 28, 2021

Data science is a field at the convergence of statistics, computer science and business. Its value is so significant that scaling data science has become the new business imperative with organizations spending tens of millions of dollars on data, technology and talent. What are Data Scientists?

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Domino Data Lab

JULY 22, 2019

Paco Nathan presented, “Data Science, Past & Future” , at Rev. This blog post provides a concise session summary, a video, and a written transcript. data science’s emergence as an interdisciplinary field – from industry, not academia. He was saying this doesn’t belong just in statistics.

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Rocket-Powered Data Science

JULY 19, 2023

I recently saw an informal online survey that asked users which types of data (tabular, text, images, or “other”) are being used in their organization’s analytics applications. This was not a scientific or statistically robust survey, so the results are not necessarily reliable, but they are interesting and provocative.

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Analytics Vidhya

MAY 28, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction This is Part-3 of the 4-part blog series on Bayesian Decision. The post Bayesian Decision Theory – Discriminant Functions and Normal Density(Part 3) appeared first on Analytics Vidhya.

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Data Science 101

NOVEMBER 18, 2019

After receiving some interest, I have decided to open up the posting more to the data science community. There are more details on the Contribute Page. If there is enough interest, I will be posting community contributed posts on Wednesdays and sponsored posts on Thursdays. What is the difference?

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TDAN

OCTOBER 15, 2019

One of the broadest fields, data science offers a lot of things ranging from courses to career opportunities. In today’s age where petabytes of data are being created by various companies and individuals on a daily basis (1 Petabyte = 10^15 Bytes). A lot of companies are now diving deep into the statistics in order […].

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Analytics Vidhya

MAY 25, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction This is Part-2 of the 4-part blog series on Bayesian. The post Detailed Guide To Bayesian Decision Theory – Part 2 appeared first on Analytics Vidhya.

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Analytics Vidhya

JUNE 12, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction In the last blog we looked at a test to. The post Decoding the Chi-Square Test?-?Use, Use, Implementation and Visualization appeared first on Analytics Vidhya.

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datapine

MARCH 31, 2022

According to the US Bureau of Labor Statistics, demand for qualified business intelligence analysts and managers is expected to soar to 14% by 2026, with the overall need for data professionals to climb to 28% by the same year. A background in (or a firm grasp of) data warehousing and mining. BI engineer.

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Data Science and Beyond

DECEMBER 31, 0001

This website/blog is mostly meant to group together stuff I write in various places. It’s mainly focused on data science. I’m an experienced data scientist and software engineer with a strong background in computer science, programming, machine learning, and statistics. My name is Yanir Seroussi.

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Smarten

JANUARY 30, 2024

Business users cannot even hope to prepare data for analytics – at least not without the right tools. Gartner predicts that, ‘data preparation will be utilized in more than 70% of new data integration projects for analytics and data science.’ So, why is there so much attention paid to the task of data preparation?

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O'Reilly on Data

SEPTEMBER 15, 2021

64% of the respondents took part in training or obtained certifications in the past year, and 31% reported spending over 100 hours in training programs, ranging from formal graduate degrees to reading blog posts. To nobody’s surprise, our survey showed that data science and AI professionals are mostly male. Salaries by Gender.

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Domino Data Lab

OCTOBER 7, 2020

Statistical methods for analyzing this two-dimensional data exist. This statistical test is correct because the data are (presumably) bivariate normal. When there are many variables the Curse of Dimensionality changes the behavior of data and standard statistical methods give the wrong answers.

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Smarten

APRIL 25, 2024

Users can harness the power of statistics and machine learning to uncover hidden insights and improve the overall quality of your data. Data insights provide actionable recommendations to address various types of data quality errors, enabling users to take prompt corrective actions for improved data integrity.

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DataRobot Blog

SEPTEMBER 1, 2022

These and many other questions are now on top of the agenda of every data science team. To quantify how well your models are doing, DataRobot provides you with a comprehensive set of data science metrics — from the standards (Log Loss, RMSE) to the more specific (SMAPE, Tweedie Deviance).

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Aryng

OCTOBER 18, 2022

Typically, a data scientist is considered as someone who uses statistical methods, machine learning, and AI approaches to solve problems, spending their day using tools like python or R using pre-existing libraries to […] The post Mythbuster: Only Data Scientists Can Analyze Data appeared first on Aryng's Blog.

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IBM Big Data Hub

NOVEMBER 29, 2023

For instance, if a business prioritizes accuracy in generating synthetic data, the resulting output may inadvertently include too many personally identifiable attributes, thereby increasing the company’s privacy risk exposure unknowingly. How to get started with synthetic data in watsonx.ai Explore the benefits of watsonx.ai

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IBM Big Data Hub

FEBRUARY 13, 2024

Like diligent students, these generative models soak up information and identify patterns, structures and relationships between data points, which is how they learn the grammar of poetry, artistic brushstrokes and musical melodies. Imagine each data point as a glowing orb placed on a vast, multi-dimensional landscape.

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IBM Big Data Hub

DECEMBER 19, 2023

Anomalies are not inherently bad, but being aware of them, and having data to put them in context, is integral to understanding and protecting your business. The challenge for IT departments working in data science is making sense of expanding and ever-changing data points.

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The Unofficial Google Data Science Blog

DECEMBER 14, 2021

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature.

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Smart Data Collective

JULY 24, 2023

Nabil M Abbas of Towards Data Science talked about one of the most interesting ways that data analytics is changing the NBA. Analytics technology has made them even more reliable content distribution networks, since they have detailed engagement data that brands can take advantage of. billion on analytics last year.

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Smarten

MAY 29, 2023

World-renowned technology analysis firm Gartner defines the role this way, ‘A citizen data scientist is a person who creates or generates models that leverage predictive or prescriptive analytics, but whose primary job function is outside of the field of statistics and analytics. ‘If

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The Unofficial Google Data Science Blog

NOVEMBER 19, 2015

by SEAN GERRISH If you are a regular at this blog, thanks for reading. We will continue to bring you posts from the range of data science activities at Google. We briefly highlight some of the things we look for in data scientists we hire at Google and give tips on ways to prepare. This post is different. Be passionate.

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Smarten

NOVEMBER 23, 2023

About Smarten The Smarten Augmented Analytics product suite includes traditional and modern business intelligence tools, and is designed for the average business user and supports the transformation of business users to Citizen Data Scientists.

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Birst BI

NOVEMBER 20, 2018

When you hear about Data Science, Big Data, Analytics, Artificial Intelligence, Machine Learning, or Deep Learning, you may end up feeling a bit confused about what these terms mean. To solve practical decision problems, the Data Scientist typically uses combinations of these methods. So, what do these terms really mean?

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