CIO Business Intelligence

JUNE 7, 2022

What is data analytics? Data analytics is a discipline focused on extracting insights from data. It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. What are the four types of data analytics?

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CIO Business Intelligence

JULY 28, 2022

They’ve also created a relationship with universities, setting up a pipeline of emerging technology-focused interns, who work at the company, gain experience in data science, and then can potentially be hired after they graduate. . Expanding data science teams. These people are making up a data science support system.

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CIO Business Intelligence

APRIL 25, 2022

An education in data science can help you land a job as a data analyst , data engineer , data architect , or data scientist. Here are the top 15 data science boot camps to help you launch a career in data science, according to reviews and data collected from Switchup.

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Data Science Machine Learning Deep Learning Statistics 145

Smart Data Collective

MARCH 27, 2023

Unleash your analytical prowess in today’s most coveted professions – Data Science and Data Analytics! As companies plunge into the world of data, skilled individuals who can extract valuable insights from an ocean of information are in high demand.

Data Science 90

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CIO Business Intelligence

JUNE 29, 2022

In essence, MLOps analysts are a section of the data science team. Whereas data scientists focus on algorithm design and the interpretation of information, MLOps concentrate on making sure the model is working at its best amid changing demands. Where does MLOps fit into data science projects? .

Data Science 95

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Smart Data Collective

AUGUST 18, 2021

When applied to the hiring process, data analytics can help you strategically grow and manage your team with greater accuracy and success. More companies are using big data to create a stronger company culture. Moreover, big data can also improve talent retention by 56% and better clarify skills gaps by 50%.

Data Science 136

Data Science Management Big Data Metrics 136

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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Data Science Statistics Recreation/Entertainment Predictive Analytics 110

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 56

Smart Data Collective

NOVEMBER 18, 2020

You should learn what a big data career looks like , which involves knowing the differences between different data processes. Online courses and universities are offering a growing number of programs of study that center around the data science specialty. What is Data Science? Where to Use Data Science?

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CIO Business Intelligence

MARCH 21, 2022

Data scientists are analytical data experts who use data science to discover insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. Learn from data scientists about their responsibilities and find out how to launch a data science career. |

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Unstructured Data Data Analytics Analytics Structured Data 136

KDnuggets

MAY 31, 2023

Bard for Data Science Cheat Sheet • Top 10 Tools for Detecting ChatGPT, GPT-4, Bard, and other LLMs • Data Analytics Tools You Need To Know in 2023 • AI is Eating Data Science • A Deep Dive into GPT Models: Evolution & Performance Comparison

Data Science 71

Data Science Modeling Data Analytics Analytics 71

Decision Management Solutions

MARCH 16, 2021

If you are working in data science, you may be frustrated with the progress and ROI of your data science and artificial intelligence projects. The first step in achieving success with data science is recognizing these problem areas in your approach. by James Taylor, CEO, Decision Management Solutions.

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Data Science ROI Machine Learning Modeling 59

Jen Stirrup

MAY 5, 2020

Recently, I gave a Make Your Data Work Monday webinar on the complexities of the data sources for data science in Azure, and I thought it important enough to turn into an actual post. How can you differentiate the different opportunities to store your data in Azure? The data is also distributed.

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Machine Learning Data Science Data Lake Big Data 89

Smart Data Collective

AUGUST 21, 2019

They recognize that the overemphasis on big data has created problems, so they have presented alternatives. Data Science Companies Focus on Optimal Data Utilization Rather than Just Emphasizing Data Scalability. Endor is a leading pioneer in data science. appeared first on SmartData Collective.

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Data Science Big Data Data Strategy Risk 67

Rocket-Powered Data Science

MARCH 19, 2021

How to make smarter data-driven decisions at scale : [link]. The determination of winners and losers in the data analytics space is a much more dynamic proposition than it ever has been. A lot has changed in those five years, and so has the data landscape. But if they wait another three years, they will never catch up.”

Internet of Things 259

Internet of Things Analytics IoT Prescriptive Analytics 259

Peter James Thomas

AUGUST 4, 2020

As usual, the new definitions range across the data arena: from Data Science and Machine Learning; to Information and Reporting; to Data Governance and Controls. Analytical Repository. Conformed Data (Conformed Dimension). Data Capability. Data Capability Framework (Data Capability Model).

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Data Analytics Analytics Machine Learning Data Governance 98

FineReport

NOVEMBER 26, 2019

When it comes to data analytics tools, we always have questions. What is the difference between so many data analysis tools? Because the reviewers who evaluate a certain data analytics tool may be from a different perspective, with some personal feelings. The Skills That Data Analysts Need to Master.

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Data Analytics Analytics Statistics Visualization 98

Alation

JUNE 9, 2023

We were thrilled to attend Gartner Data and Analytics Summit 2023 , on May 22–24 in London. D&A leaders from around the world gathered to discuss and find ways to overcome the latest challenges through strategies and innovations backed by data, analytics, and data science.

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Data Analytics Analytics Data Quality Data-driven 52

Rocket-Powered Data Science

JULY 30, 2018

Journey Sciences (using graph and linked data modeling). And check out the many excellent resources and consulting services (in Big Data Analytics, Data Science, Machine Learning, and Machine Intelligence) at Booz Allen Hamilton , to help all of your data-driven campaigns make big moves and move forward more effectively.

Machine Learning 199

Machine Learning Marketing Behavioral Analytics IoT 199

Cloudera

OCTOBER 11, 2022

Al needs machine learning (ML), ML needs data science. Data science needs analytics. And they all need lots of data. Because that is how models learn. You also need to continually feed models new data to keep them up to date. But it isn’t just aggregating data for models.

Snapshot 89

Snapshot Data Science Digital Transformation Metadata 89

Dataiku

MAY 8, 2023

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. These powerful models enable individuals without coding expertise to easily access, comprehend, and analyze complex datasets.

Visualization 59

Visualization Data Science Modeling Data Analytics 59

CIO Business Intelligence

SEPTEMBER 14, 2023

These generalists are often responsible for every step of the data process, from managing data to analyzing it. Dataquest says this is a good role for anyone looking to transition from data science to data engineering, as smaller businesses often don’t need to engineer for scale.

Analytics 135

Analytics Data Science Unstructured Data Data mining 135

IBM Big Data Hub

DECEMBER 4, 2017

Readers of the IBM Big Data & Analytics Hub were hungry for knowledge this year. They voraciously read blog posts about incorporating machine learning, choosing the best possible data model, determining how to make the most of data science skills, working with open source frameworks and more.

Big Data 79

Big Data Data Analytics Analytics Machine Learning 79

CIO Business Intelligence

JANUARY 31, 2024

Everything from geothermal data centers to more efficient graphic processing units (GPUs) can help. But AI users must also get over the urge to use the biggest, baddest AI models to solve every problem if they truly want to fight climate change. Is it necessary for a model that can also write a sonnet to write code for us?”

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Cost-Benefit Modeling Testing IoT 131

O'Reilly on Data

FEBRUARY 4, 2019

In practice this means developing a coherent strategy for integrating artificial intelligence (AI), big data, and cloud components, and specifically investing in foundational technologies needed to sustain the sensible use of data, analytics, and machine learning. Machine Learning model lifecycle management.

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Machine Learning Enterprise IoT Big Data 277

Smarten

APRIL 10, 2023

Investment in predictive analytics benefits everyone in the organization, including business users and team members, data scientists and the organization in general. Team members can bridge the gap of data science skills so they don’t have to wait for IT or data scientists to help them produce a report or perform analytics.

Predictive Analytics 52

Predictive Analytics Predictive Modeling Analytics Prescriptive Analytics 52

Dataiku

AUGUST 11, 2020

It’s no secret that marketing today relies heavily on data analytics and data science. In this blogpost, we are going to address yet another interesting application of data science in marketing, which is marketing attribution. Part 3 will focus on model performance and comparison. Let’s get started!

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Marketing Data-driven Data Science Modeling 100

Data Science 101

DECEMBER 11, 2019

This weeks guest post comes from KDD (Knowledge Discovery and Data Mining). Every year they host an excellent and influential conference focusing on many areas of data science. Honestly, KDD has been promoting data science way before data science was even cool. 1989 to be exact. The details are below.

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KDD Knowledge Discovery Data mining Statistics 81

bridgei2i

APRIL 28, 2020

And so that process with curation or identifying which data potentially is a leading indicator and then test those leading indicators. It takes a lot of data science, a lot of data curation, a lot of data integration that many companies are not prepared to shift to as quickly as the current crisis demands.

Enterprise 103

Enterprise Data Analytics Analytics Forecasting 103

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 Advanced data preparation.

Prescriptive Analytics 52

Prescriptive Analytics Predictive Modeling Digital Transformation Business Objectives 52

CIO Business Intelligence

MAY 9, 2024

They should lead the efforts to tie AI capabilities to data analytics and business process strategies and champion an AI-first mindset throughout the organization. And they should have a proficiency in data science and analytics to effectively leverage data-driven insights and develop AI models.

Risk Management 138

Risk Management Risk Cost-Benefit Technology 138

CIO Business Intelligence

AUGUST 9, 2022

These generalists are often responsible for every step of the data process, from managing data to analyzing it. Dataquest says this is a good role for anyone looking to transition from data science to data engineering, as smaller businesses often don’t need to engineer for scale. Data engineer vs. data architect.

Analytics 130

Analytics Data Science Statistics Unstructured Data 130

FineReport

MARCH 24, 2024

Rapid technological advancements and extensive networking have propelled the evolution of data analytics, fundamentally reshaping decision-making practices across various sectors. In this landscape, data analysts assume a pivotal role, tasked with interpreting data to drive informed decision-making.

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Statistics Data mining Visualization Reporting 52

Smart Data Collective

DECEMBER 21, 2022

The IT and cybersecurity sectors are heavily dependent on people with an expertise in data science. The Bureau of Labor Statistics estimates that there are nearly 106,000 employed data scientists in the United States. You have so many opportunities available to you when you get a degree in data science or a related field.

IT 110

IT Big Data Data Science Data-driven 110

Analytics Vidhya

SEPTEMBER 26, 2023

Data analytics transforms raw data into actionable insights that drive informed decision-making, enhance performance, and create value. Yet, organizations vary in their data analytics maturity and capabilities. Wondering how you can monitor your current position and elevate your data analytics journey?

Analytics 184

Analytics Strategy Data Analytics Data Science 184

Smart Data Collective

SEPTEMBER 16, 2020

Predictive analytics, sometimes referred to as big data analytics, relies on aspects of data mining as well as algorithms to develop predictive models. These predictive models can be used by enterprise marketers to more effectively develop predictions of future user behaviors based on the sourced historical data.

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Predictive Analytics Analytics Predictive Modeling Data mining 121

CIO Business Intelligence

OCTOBER 24, 2023

As CIOs prepare for the next wave of digital transformation, they must demonstrate shorter-term business impacts from technology investments and achieve larger innovation goals that evolve the organization’s business model.

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