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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Machine Learning Data Science Statistics Deep Learning 54

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. Visualizations are the best tools to make trends and general insights understandable.

Business Intelligence 239

Business Intelligence Statistics Visualization Data-driven 239

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.

Data Science 53

Data Science Machine Learning Cost-Benefit Deep Learning 53

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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Data Science Machine Learning Statistics Deep Learning 45

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?

Analytics 67

Analytics Data Quality Visualization Metadata 67

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.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Smarten

APRIL 25, 2024

What you need are apps and solutions that allow you to ask easy questions in your own words and receive guidance and recommendations on how to best visualize and present your data and what techniques to use to gain the most insight. These measures empower them with a deeper understanding of their data like never before.

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Data Quality Analytics Machine Learning Visualization 62

Smarten

NOVEMBER 23, 2023

“These tools enable data literacy and digital transformation and increase team productivity and creativity, encouraging power users and those with average technology skills to dive into analytics ad use fact-based decision-making to improve market positioning.”

Publishing 62

Publishing Marketing Analytics Predictive Modeling 62

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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Data Science Statistics Data Strategy Business Intelligence 40

DataRobot Blog

SEPTEMBER 1, 2022

These and many other questions are now on top of the agenda of every data science team. This also shows how the models compare on standard performance metrics and informative visualizations like Dual Lift. New DataRobot Large Scale Monitoring allows you to access aggregated prediction statistics.

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Sisense

APRIL 10, 2020

In Talking Data , we delve into the rapidly evolving worlds of Natural Language Processing and Generation. Text data is proliferating at a staggering rate, and only advanced coding languages like Python and R will be able to pull insights out of these datasets at scale. NLP can be used on written text or speech data.

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Deep Learning Data Science Machine Learning Visualization 98

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.

Analytics Technologies 79

Analytics Technologies Marketing Technology Analytics 79

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.

Machine Learning 65

Machine Learning Optimization Unstructured Data Sales 65

Cloudera

FEBRUARY 25, 2021

Today’s enterprise data science teams have one of the most challenging, yet most important roles to play in your business’s ML strategy. Additionally, the AMPs catalog within CML is completely customizable, enabling your data science teams to build and securely share internal projects as AMPs.

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Deep Learning Machine Learning Forecasting Visualization 85

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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Statistics Machine Learning Deep Learning Optimization 84

Sisense

SEPTEMBER 3, 2020

We live in a constantly-evolving world of data. That means that jobs in data big data and data analytics abound. The wide variety of data titles can be dizzying and confusing! Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization.

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Statistics Metrics Visualization Finance 97

Smarten

MAY 31, 2022

The augmented analytics solution your business chooses must have a complete set of predictive analytical techniques with built-in, sophisticated algorithms and tools that are easy to use and will guide the user to choose the right predictive analytical technique for the data they are analyzing. Descriptive Statistics. Forecasting.

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Domino Data Lab

JULY 10, 2019

For example, there have been multiple promising tools created recently that have Python APIs, are built on top of TensorFlow or PyTorch , and encapsulate deep learning best practices to allow data scientists to speed up research. Practitioner Tip #2 – The command-line calls for visualizations can be difficult to use on some systems.

Deep Learning 110

Deep Learning Visualization Recreation/Entertainment Data Processing 110

Darkhorse

DECEMBER 11, 2017

Analytics and now Data Science are trapped in the middle. A recent HBR article put it at 100% for data science projects. I suggest that there are five distinct job descriptions: SUBSCRIBE TO OUR BLOG. Data Steward – this skillset is alive and well in most organizations. That’s abysmal. He adds scale.

Analytics 80

Analytics Visualization Data Warehouse Data-driven 80

DataRobot Blog

MAY 23, 2022

Department of Transportation Data. Example of border transport data from USDOT. Department of Transportation (USDOT) Bureau of Transportation Statistics (BTS) provide publicly-available monthly summary statistics for both the U.S.-Canada The database contains entry data from Mexico to the U.S. Canada and U.S.-Mexico

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Forecasting Statistics Machine Learning Modeling 59

CIO Business Intelligence

OCTOBER 13, 2023

It’s a role that requires experience with natural language processing , coding languages, statistical models, and large language and generative AI models. According to the survey, 28% of respondents said they have hired data scientists to support generative AI, while 30% said they have plans to hire candidates.

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Deep Learning Machine Learning Modeling Software 143

Smarten

APRIL 12, 2023

Smarten Augmented Analytics tools include Assisted Predictive Modeling , Smart Data Visualization , Self-Serve Data Preparation , Sentiment Analysis , and Clickless Analytics with natural language processing (NLP) for search analytics.

Snapshot 52

Snapshot Key Performance Indicator KPI Business Intelligence 52

DataRobot

JUNE 16, 2021

In several earlier blog posts, we have focused on what we at DataRobot call the AI production gap , which refers to the gap that makes it difficult to transition models from the data science teams who develop them to the IT and DevOps teams who are responsible for deploying and monitoring them in production.

Management 52

Management Modeling Statistics Data Science 52

Alation

FEBRUARY 20, 2020

The head of sales needs the most up-to-date statistics on the state of the business, and they need it now — well, actually, yesterday. Now, every Einstein Analytics user can gain the power of Alation’s data recommendations through the Alation Data Catalog. Read more about the partnership on Salesforce’s blog here.

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Smarten

JUNE 17, 2022

Smarten Augmented Analytics tools include Assisted Predictive Modeling , Smart Data Visualization , Self-Serve Data Preparation , Sentiment Analysis , and Clickless Analytics with natural language processing (NLP) for search analytics.

Analytics 67

Analytics IT Business Intelligence Predictive Modeling 67

Sisense

JUNE 12, 2019

our annual client conference, I gave a presentation that took a deep dive into artificial intelligence and subgroups including AI, ML, and statistics. Stay tuned for more blogs by me as we roll these capabilities out!). Yesterday, during Eureka! , If you were not in attendance at Eureka! Back to my session. Why AI Now?

Analytics 75

Analytics Data-driven Dashboards IoT 75

bridgei2i

OCTOBER 7, 2020

To get a perspective on evolving trends in insurance in the post-COVID world, read BRIDGEi2i’s blog here. Underwriting essentially means evaluating the risk proposition of an insurance cover and determining how much an insurer should charge for the same by studying historical data and statistical models. click here.

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Insurance Sales Strategy Digital Transformation 52

Domino Data Lab

JANUARY 6, 2019

In Paco Nathan ‘s latest column, he explores the theme of “learning data science” by diving into education programs, learning materials, educational approaches, as well as perceptions about education. He is also the Co-Chair of the upcoming Data Science Leaders Summit, Rev. Learning Data Science.

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IBM Big Data Hub

AUGUST 7, 2023

Therefore, IBM observes that more clients tend to consult AI leaders to help establish governance and enhance AI and data science capabilities, an operating model in the form of co-delivery partnerships. The post Optimizing clinical trial site performance: A focus on three AI capabilities appeared first on IBM Blog.

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Optimization Forecasting Data-driven Strategy 55

FineReport

MAY 30, 2024

In today’s data-driven world, the data visualization specialist plays a pivotal role in transforming complex information into visually appealing formats. The demand for skilled professionals in this field is rapidly increasing as businesses rely more on data for decision-making and operations.

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Visualization Dashboards KPI Reporting 52

IBM Big Data Hub

APRIL 18, 2024

Achieving these feats is accomplished through a combination of sophisticated algorithms, natural language processing (NLP) and computer science principles. LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language.

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Sisense

SEPTEMBER 25, 2020

A data scientist must be skilled in many arts: math and statistics, computer science, and domain knowledge. No matter your skill, career level, or title, the ability to analyze, organize, and visualize data are vital skills in our world of quickly growing and ever-changing data. Linear regression.

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Domino Data Lab

JULY 15, 2021

In the multiverse of data science, the tool options continue to expand and evolve. While there are certainly engineers and scientists who may be entrenched in one camp or another (the R camp vs. Python, for example, or SAS vs. MATLAB), there has been a growing trend towards dispersion of data science tools.

Data Science 145

Data Science Visualization Modeling Enterprise 145

The Unofficial Google Data Science Blog

SEPTEMBER 23, 2015

by KAY BRODERSEN For the first time in the history of statistics, recent innovations in big data might allow us to estimate fine-grained causal effects, automatically and at scale. It's why estimating causal effects has been at the heart of data science. Analyses might begin by exploring, visualizing, correlating.

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Advertising Statistics Big Data Modeling 47

The Unofficial Google Data Science Blog

OCTOBER 31, 2016

By PATRICK RILEY For a number of years, I led the data science team for Google Search logs. Some people seemed to be naturally good at doing this kind of high quality data analysis. I'd like to share the contents of that document in this blog post. On the flip side, you sometimes have a small volume of data.

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Metrics Measurement Statistics Data Collection 107

Domino Data Lab

JUNE 8, 2021

In this blog post we take a deep dive into the internals of Support Vector Machines. and they are all available within the Domino Data Science Platform. Let’s start by loading the needed Python libraries, loading and sampling the data, and plotting it for visual inspection. 1999) and more. Rouveirol (Eds.),

Optimization 76

Optimization Machine Learning Measurement Data Science 76

Alation

SEPTEMBER 28, 2022

Moreover, as the highly fragmented technologies of the data industry are now ever-expanding, having a DataOps mindset and infrastructure helps data teams to build a sustainable way of adopting new technologies as a company grows. This requires that data engineers embrace learning and integrating new technologies, such as AI tools.

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