DataCamp

MARCH 22, 2021

The integrated use of data science and machine learning in healthcare has many applications for improving patient care, business processes and operations, and pharmaceuticals. But the healthcare industry faces considerable challenges in data quality and infrastructure, compliance and governance, and upskilling.

Data Science 105

Data Science Data Quality Machine Learning 105

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.

Data Science 145

Data Science Machine Learning Deep Learning Statistics 145

Octopai

MARCH 23, 2022

If you’re a conscientious data scientist, you’re going to clean up your data before using it to make models, predictions and recommendations. In the past, it’s been estimated that data scientists spend somewhere between 30% and 80% of their time just prepping and cleaning data. Data Supervision. Not really.

Data Quality 91

Data Quality Reporting Modeling Interactive 91

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.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

Analytics Vidhya

DECEMBER 30, 2022

This article was published as a part of the Data Science Blogathon. Introduction In machine learning, the data is an essential part of the training of machine learning algorithms. The amount of data and the data quality highly affect the results from the machine learning algorithms.

Machine Learning 273

Machine Learning Data Quality Data Science Publishing 273

KDnuggets

AUGUST 18, 2022

Just as Maslow identified a hierarchy of needs for people, data teams have a hierarchy of needs, beginning with data freshness; including volumes, schemas, and values; and culminating with lineage.

Data Quality 111

Data Quality Data Science 111

Analytics Vidhya

DECEMBER 28, 2022

This article was published as a part of the Data Science Blogathon. Choosing the best appropriate activation function can help one get better results with even reduced data quality; hence, […]. The post Sigmoid Function: Derivative and Working Mechanism appeared first on Analytics Vidhya.

Deep Learning 208

Deep Learning Data Quality Data Science Publishing 208

Analytics Vidhya

SEPTEMBER 2, 2021

This article was published as a part of the Data Science Blogathon Overview Running data projects takes a lot of time. Poor data results in poor judgments. Running unit tests in data science and data engineering projects assures data quality. Table of content Introduction […].

Testing 271

Testing Data Quality Data-driven Data Science 271

Jim Harris

MARCH 9, 2022

OCDQ Radio is an audio podcast about data quality and its related disciplines, produced and hosted by Jim Harris. Podcast Episodes on Big Data and Data Science. This post is part of my Best of OCDQ Radio series, organizing groups of episodes by topic(s).

Big Data 52

Big Data Data Science Data Quality Data Processing 52

KDnuggets

MARCH 16, 2022

How Long Does It Take to Learn Data Science Fundamentals?; Become a Data Science Professional in Five Steps; New Ways of Sharing Code Blocks for Data Scientists; Machine Learning Algorithms for Classification; The Significance of Data Quality in Making a Successful Machine Learning Model.

Data Science 72

Data Science Machine Learning Data Quality Modeling 72

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. It utilizes scientific approaches, frameworks, algorithms, and procedures to extract insight from a massive amount of data.

Data Science 85

Data Science Statistics IT Data Quality 85

Domino Data Lab

JULY 22, 2019

Paco Nathan presented, “Data Science, Past & Future” , at Rev. At Rev’s “ Data Science, Past & Future” , Paco Nathan covered contextual insight into some common impactful themes over the decades that also provided a “lens” help data scientists, researchers, and leaders consider the future.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

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.

Data Science 68

Data Science Big Data Data Strategy Risk 68

O'Reilly on Data

FEBRUARY 11, 2020

We suspected that data quality was a topic brimming with interest. The responses show a surfeit of concerns around data quality and some uncertainty about how best to address those concerns. Key survey results: The C-suite is engaged with data quality. Data quality might get worse before it gets better.

Data Quality 304

Data Quality Metadata Data Governance Publishing 304

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

CIO Business Intelligence

MAY 7, 2024

That’s according to a recent report based on a survey of CDOs by AWS in conjunction with the Chief Data Officer and Information Quality (CDOIQ) Symposium. The CDO position first gained momentum around 2008, to ensure data quality and transparency to comply with regulations following the housing credit crisis of that era.

Data Strategy 128

Data Strategy Data-driven Data Quality Strategy 128

Dataiku

JANUARY 19, 2022

Risks of failing to implement a data quality assurance policy can be far reaching, especially when it is a reactive versus proactive approach. Imagine incorrect or broken automated reports for stakeholders, causing not only hours of rework, but loss of confidence in the value of the data.

Data Science 64

Data Science Data Quality Analytics Risk 64

Rocket-Powered Data Science

FEBRUARY 15, 2023

These rules are not necessarily “Rocket Science” (despite the name of this blog site), but they are common business sense for most business-disruptive technology implementations in enterprises. Clean it, annotate it, catalog it, and bring it into the data family (connect the dots and see what happens).

Strategy 289

Strategy Experimentation Uncertainty Machine Learning 289

CIO Business Intelligence

MAY 24, 2022

In order to help maintain data privacy while validating and standardizing data for use, the IDMC platform offers a Data Quality Accelerator for Crisis Response. Cloud Computing, Data Management, Financial Services Industry, Healthcare Industry

Finance 137

Finance Management Metadata Data Quality 137

Cloudera

MAY 28, 2019

Cloudera customers can start building enterprise AI on their data management competencies today with the Cloudera Data Science Workbench (CDSW). Learn more about the Cloudera Data Science Workbench for the end-to-end ML workflow at our bi-weekly webinar series featuring live expert demos and Q&A. Stay tuned.

Data Science 48

Data Science Machine Learning Data Strategy Cost-Benefit 48

CIO Business Intelligence

JUNE 14, 2023

Certification of Professional Achievement in Data Sciences The Certification of Professional Achievement in Data Sciences is a nondegree program intended to develop facility with foundational data science skills. How to prepare: No prior computer science or programming knowledge is necessary.

Big Data 126

Big Data Data Analytics Analytics Predictive Modeling 126

Dataiku

DECEMBER 30, 2022

In helping organizations around the globe set up and implement their data science and AI strategies, we often hear teams say that they’re waiting to figure out their data first before beginning to generate value with advanced analytics and AI — whether they’re referring to data quality, data silos, or centralization in a data lake.

Data Lake 64

Data Lake IT Data Quality Data Science 64

O'Reilly on Data

JUNE 18, 2019

As model building become easier, the problem of high-quality data becomes more evident than ever. Even with advances in building robust models, the reality is that noisy data and incomplete data remain the biggest hurdles to effective end-to-end solutions. Data integration and cleaning.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

Alation

JUNE 9, 2023

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. Here are a few of the major takeaways that surfaced from the event.

Data Analytics 52

Data Analytics Analytics Data Quality Data-driven 52

CIO Business Intelligence

MARCH 24, 2023

The Business Application Research Center (BARC) warns that data governance is a highly complex, ongoing program, not a “big bang initiative,” and it runs the risk of participants losing trust and interest over time. Informatica Axon Informatica Axon is a collection hub and data marketplace for supporting programs.

Data Governance 142

Data Governance Management Metadata Data Quality 142

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 128

Analytics Data Science Statistics Unstructured Data 128

Dataiku

MAY 5, 2021

Regularly thrown around are the myriad of reasons that data science and machine learning (ML) projects fail, with some of the popular ones including data quality issues (missing or incomplete data, for example), problems around tooling (i.e.,

Digital Transformation 59

Digital Transformation Data Quality Machine Learning Data Science 59

KDnuggets

NOVEMBER 8, 2023

The Density Kernel Depth (DKD) method provides a nuanced approach to detect outliers in functional data, offering clarity in complex datasets and ensuring data quality. Its application becomes crucial as we navigate increasingly intricate data landscapes.

Data Quality 80

Data Quality IT Data Science 80

Dataiku

MARCH 20, 2019

Data science is a team sport that needs to be collaborative to be successful, but data leaders and practitioners often disagree on where exactly responsibility for data quality and data science activation is housed.

ROI 65

ROI Data Quality Data Science Enterprise 65

CIO Business Intelligence

APRIL 17, 2024

Informatica, according to Park, offers a broader set of tools than Mulesoft, especially in the areas of data quality, data security, and a variety of master data management offerings.

Cost-Benefit 128

Cost-Benefit B2B Marketing Enterprise 128

CIO Business Intelligence

DECEMBER 19, 2023

In the 2023 State of Data Science and Machine Learning Report , only 18% of respondents said that at least half their machine learning models make it into production. Third, in the CDO Agenda: 2024: Navigating Data and Generative AI Frontiers , 57% of respondents haven’t changed their data environments to support generative AI.

Digital Transformation 142

Digital Transformation Unstructured Data Machine Learning Risk Management 142

KDnuggets

NOVEMBER 22, 2022

Leverage precise data to discover business opportunities, make strategic decisions, and increase ROI with a powerful data quality platform.

ROI 93

ROI Data Quality Data Science 93

Jen Stirrup

SEPTEMBER 30, 2021

Although CRISP-DM is not perfect , the CRISP-DM framework offers a pathway for machine learning using AzureML for Microsoft Data Platform professionals. AI vs ML vs Data Science vs Business Intelligence. They may also learn from evidence, but the data and the modelling fundamentally comes from humans in some way.

Business Intelligence 135

Business Intelligence Data mining Machine Learning Testing 135

IBM Big Data Hub

JULY 13, 2023

When we talk about data integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and security of an organization’s data. Together, these factors determine the reliability of the organization’s data. Data quality Data quality is essentially the measure of data integrity.

Data Quality 67

Data Quality Data Integration Metadata Cost-Benefit 67

Alation

AUGUST 30, 2022

These range from data sources , including SaaS applications like Salesforce; ELT like Fivetran; cloud data warehouses like Snowflake; and data science and BI tools like Tableau. This expansive map of tools constitutes today’s modern data stack. In 2022.3, In 2022.3,

Metadata 52

Metadata Data Quality Data Governance Machine Learning 52

CIO Business Intelligence

NOVEMBER 22, 2022

Data observability becomes business-critical Data observability extends the concept of data quality by closely monitoring data as it flows in and out of the applications. CIOs should first understand the different approaches to observing data and how it differs from quality management,” he notes.

Data Strategy 145

Data Strategy Strategy Enterprise Consulting 145

Andrew White

JULY 23, 2019

Here is a snapshot from our growing new set of data and analytics case studies. D&A Strategy: Continuously Market-Tested Data & Analytics Strategy (UrbanShopping*) 710519. Analytics, BI and Data Science: Peer-Based Analytics Learning (ABB) 710371. Data Quality Score (TE Connectivity) 705649.

Snapshot 73

Snapshot Analytics Machine Learning Data Quality 73