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 277

Deep Learning Data Quality Data Science Publishing 277

KDnuggets

JANUARY 17, 2022

Incorrect or unclean data leads to false conclusions. The time you take to understand and clean the data is vital to the outcome and quality of the results. Data Quality always takes the win against complex fancy algorithms.

Data Quality 153

Data Quality Data Science 153

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 291

Machine Learning Data Quality Data Science Publishing 291

DataKitchen

APRIL 13, 2021

Piperr.io — Pre-built data pipelines across enterprise stakeholders, from IT to analytics, tech, data science and LoBs. Prefect Technologies — Open-source data engineering platform that builds, tests, and runs data workflows. Genie — Distributed big data orchestration service by Netflix. Data breaks.

Testing 300

Testing Machine Learning Consulting Data Science 300

KDnuggets

NOVEMBER 16, 2023

Learn how to perform data quality checks using pandas. From detecting missing records to outliers, inconsistent data entry and more.

Data Quality 129

Data Quality Data Science 129

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

O'Reilly on Data

MARCH 24, 2020

Why is high-quality and accessible data foundational? The assumed value of data is a myth leading to inflated valuations of start-ups capturing said data. Generating data with a pre-specified analysis plan and running that analysis is good. Re-analyzing existing data is often very bad.”

Machine Learning 364

Machine Learning Statistics Data Quality Data Collection 364

O'Reilly on Data

SEPTEMBER 24, 2018

Data science is the sexy thing companies want. The data engineering and operations teams don't get much love. The organizations don’t realize that data science stands on the shoulders of DataOps and data engineering giants. They know how to operate the big data frameworks. They're right.

Enterprise 260

Enterprise Big Data Data Quality Unstructured Data 260

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 Machine Learning Data Quality 105

AWS Big Data

DECEMBER 4, 2024

SageMaker Lakehouse enables seamless data access directly in the new SageMaker Unified Studio and provides the flexibility to access and query your data with all Apache Iceberg-compatible tools on a single copy of analytics data. Having confidence in your data is key.

Data Analytics 102

Data Analytics Analytics Data Lake Data Quality 102

Jen Stirrup

NOVEMBER 15, 2022

How can systems thinking and data science solve digital transformation problems? Understandably, organizations focus on the data and the technology since data retrieval is often viewed as a data problem. However, the thrust here is not to diminish data science or data engineering.

Data Science 145

Data Science Digital Transformation Data-driven Measurement 145

CIO Business Intelligence

DECEMBER 11, 2024

This approach is repeatable, minimizes dependence on manual controls, harnesses technology and AI for data management and integrates seamlessly into the digital product development process. The higher the criticality and sensitivity to data downtime, the more engineering and automation are needed.

Management 111

Management Data Governance Data Science Reporting 111

datapine

NOVEMBER 27, 2019

Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2020 will be the year of data quality management and data discovery: clean and secure data combined with a simple and powerful presentation. 1) Data Quality Management (DQM).

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

KDnuggets

NOVEMBER 22, 2022

Ensuring data quality guarantees more data-informed decisions. Hence, this article highlights the common data quality issues and ways to overcome them.

Data Quality 108

Data Quality Data Science 108

CIO Business Intelligence

MARCH 25, 2025

Data debt that undermines decision-making In Digital Trailblazer , I share a story of a private company that reported a profitable year to the board, only to return after the holiday to find that data quality issues and calculation mistakes turned it into an unprofitable one.

Risk 140

Risk Cost-Benefit Data-driven Digital Transformation 140

AWS Big Data

JANUARY 15, 2025

For container terminal operators, data-driven decision-making and efficient data sharing are vital to optimizing operations and boosting supply chain efficiency. Two use cases illustrate how this can be applied for business intelligence (BI) and data science applications, using AWS services such as Amazon Redshift and Amazon SageMaker.

IoT 111

IoT Machine Learning Metadata Data-driven 111

Rocket-Powered Data Science

MARCH 20, 2023

Data collections are the ones and zeroes that encode the actionable insights (patterns, trends, relationships) that we seek to extract from our data through machine learning and data science. Datasphere is not just for data managers.

Data Warehouse 325

Data Warehouse Metadata Digital Transformation Machine Learning 325

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 98

Data Quality Reporting Modeling Interactive 98

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 279

Machine Learning Data Quality Statistics Modeling 279

Dataiku

MARCH 19, 2024

Generative AI is rapidly transforming the data science landscape. Its ability to create synthetic data promises exciting possibilities for data augmentation and improved model performance.

Data Quality 80

Data Quality Data Science Modeling IT 80

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 112

Data Quality Data Science 112

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 86

Data Science Big Data Data Strategy Risk 86

O'Reilly on Data

MARCH 18, 2020

By contrast, AI adopters are about one-third more likely to cite problems with missing or inconsistent data. The logic in this case partakes of garbage-in, garbage out : data scientists and ML engineers need quality data to train their models. This is consistent with the results of our data quality survey.

Enterprise 349

Enterprise Deep Learning Data Governance Risk 349

CIO Business Intelligence

NOVEMBER 13, 2024

Too much data science for too little gain There are so many clients who just want to do AI, any AI, and haven’t carefully thought through the use cases. “Now we have to go back and audit everything,” he says. Fortunately, this problem was caught in time. “It

IT 141

IT Consulting ROI Cost-Benefit 141

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

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 83

Data Science Machine Learning Data Quality Modeling 83

O'Reilly on Data

DECEMBER 19, 2018

We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline. In a previous post , we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure.

Machine Learning 279

Machine Learning Software Metadata Testing 279

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 290

Strategy Experimentation Uncertainty Machine Learning 290

O'Reilly on Data

JULY 15, 2019

Regulators behind SR 11-7 also emphasize the importance of data—specifically data quality , relevance , and documentation. While models garner the most press coverage, the reality is that data remains the main bottleneck in most ML projects. Gary Kazantsev on how “Data science makes an impact on Wall Street”.

Machine Learning 52

Machine Learning Management Enterprise Risk Management 52

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 63

Data Quality Data Integration Metadata Cost-Benefit 63

Smarten

APRIL 25, 2024

How Can I Ensure Data Quality and Gain Data Insight Using Augmented Analytics? There are many business issues surrounding the use of data to make decisions. One such issue is the inability of an organization to gather and analyze data.

Data Quality 64

Data Quality Analytics Machine Learning Visualization 64

DataKitchen

MAY 10, 2021

And the worst part – data errors take the fun out of data science. Remember your first data science courses? You probably imagined your career would be about helping drive insights with data instead of having to sit in endless meetings discussing analytics errors and painstaking corrective actions.

Testing 214

Testing Manufacturing Data Quality Statistics 214

IBM Big Data Hub

JANUARY 5, 2023

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

Data Architecture 52

Data Architecture Data Quality Strategy Data Lake 52

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 140

Finance Management Metadata Machine Learning 140

O'Reilly on Data

OCTOBER 13, 2020

Therefore, the PM should consider the team that will reconvene whenever it is necessary to build out or modify product features that: ensure that inputs are present and complete, establish that inputs are from a realistic (expected) distribution of the data, and trigger alarms, model retraining, or shutdowns (when necessary).

Management 314

Management Machine Learning Metrics Modeling 314

O'Reilly on Data

DECEMBER 12, 2019

Residual plots place input data and predictions into a two-dimensional visualization where influential outliers, data-quality problems, and other types of bugs often become plainly visible. Small residuals usually mean a model is right, and large residuals usually mean a model is wrong.

Machine Learning 282

Machine Learning Modeling Testing Risk Management 282

CIO Business Intelligence

MARCH 9, 2022

They conveniently store data in a flat architecture that can be queried in aggregate and offer the speed and lower cost required for big data analytics. On the other hand, they don’t support transactions or enforce data quality. Each ETL step risks introducing failures or bugs that reduce data quality. .

Data Lake 119

Data Lake Unstructured Data Data Warehouse Big Data 119