Edureka

JULY 5, 2024

Tableau Prep is a fast and efficient data preparation and integration solution (Extract, Transform, Load process) for preparing data for analysis in other Tableau applications, such as Tableau Desktop. simultaneously making raw data efficient to form insights.

Data Preparation 40

Data Preparation Process BI ETL Tools 40

Data Engineering Podcast

APRIL 28, 2024

If you've learned something or tried out a project from the show then tell us about it! If you've learned something or tried out a project from the show then tell us about it! What are the features and focus of Pieces that might encourage someone to use it over the alternatives?

Building 147

Building Data Lake High Quality Data Machine Learning 147

AltexSoft

MAY 12, 2022

In this article, we’ll share what we’ve learnt when creating an AI-based sound recognition solutions for healthcare projects. Particularly, we’ll explain how to obtain audio data, prepare it for analysis, and choose the right ML model to achieve the highest prediction accuracy. Audio data preparation.

Machine Learning 122

Machine Learning Building Deep Learning Healthcare 122

Data Engineering Podcast

JULY 1, 2018

Summary Data is often messy or incomplete, requiring human intervention to make sense of it before being usable as input to machine learning projects. When is it necessary to include human intelligence as part of the data lifecycle for ML/AI projects? This is problematic when the volume scales beyond a handful of records.

Machine Learning 100

Machine Learning Metadata Data Preparation Data Collection 100

Knowledge Hut

OCTOBER 27, 2023

Microsoft created Power BI, a business analytics tool that enables users to visualize and analyze data from various sources quickly and interactively. It provides a wide range of features and functionalities, including data preparation, data modeling, data visualization, and collaboration tools.

BI 52

BI Project Datasets Healthcare 52

ProjectPro

AUGUST 9, 2021

Table of Contents Why Learn Python for Data Science? Top 20 Python Projects for Data Science Getting Started with Python for Data Science FAQs about data science projects Why Learn Python for Data Science? Python has come to command a celebrity status in data science over the years.

Data Science 52

Data Science Python Project Datasets 52

ProjectPro

AUGUST 24, 2021

Data professionals who work with raw data like data engineers, data analysts, machine learning scientists , and machine learning engineers also play a crucial role in any data science project. And, out of these professions, this blog will discuss the data engineering job role.

Data Engineer 52

Data Engineer Data Engineering Coding Project 52

ProjectPro

AUGUST 30, 2022

This article will walk you through how one can start by exploring a loan prediction system as a data science and machine learning problem and build a system/application for loan prediction using your own machine learning project. Top 5 Loan Prediction Datasets to Practice Loan Prediction Projects Univ.AI

Machine Learning 52

Machine Learning Coding Project Datasets 52

Data Engineering Podcast

JUNE 17, 2021

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode.

Unstructured Data 100

Unstructured Data Data Warehouse Metadata Media 100

ProjectPro

OCTOBER 6, 2021

Flexera’s State of Cloud report highlighted that 41% of the survey respondents showed the most interest in using Google Cloud Platform for their future cloud computing projects. Beginner Level GCP Sample Projects Ideas 1. Google Cloud Platform is an online vendor of multiple cloud services which can be used publicly.

Google Cloud 52

Google Cloud Project Data Lake Healthcare 52

Knowledge Hut

FEBRUARY 29, 2024

Machine learning website: machinelearningmastery.com You may also be interested in exploring  data science online training. Learn by Working on Projects Working on hands-on projects gives you a real understanding and learning of the topic. Hence it is always good to work on the project.

Data Science 98

Data Science Datasets Machine Learning Database Design 98

ProjectPro

NOVEMBER 15, 2021

.” From month-long open-source contribution programs for students to recruiters preferring candidates based on their contribution to open-source projects or tech-giants deploying open-source software in their organization, open-source projects have successfully set their mark in the industry.

Big Data 52

Big Data Project Metadata Programming Language 52

AltexSoft

AUGUST 25, 2021

There are two main steps for preparing data for the machine to understand. Any ML project starts with data preparation. Massive volumes of data are required for neural network training. What should it be like and how to prepare a great one? Preparing an NLP dataset. But what makes data great?

Process 139

Process Deep Learning Datasets Machine Learning 139

Knowledge Hut

MAY 1, 2024

The only constant through this realm of change is the data science project lifecycle. We will discuss briefly below on the critical areas of the project lifecycle. Data scientists want to ensure that the insights that come from this question are supported by data and will allow the business to achieve the desired results.

Data Science 94

Data Science Raw Data Manufacturing Retail 94

AltexSoft

OCTOBER 30, 2021

The movement of data from its source to analytical tools for end users requires a whole infrastructure, and although this flow of data must be automated, building and maintaining it is a task of a data engineer. Data engineers are programmers that create software solutions with big data.

Data Engineer 98

Data Engineer Data Engineering Engineering Machine Learning 98

Data Engineering Podcast

AUGUST 13, 2022

In this episode founder Shayan Mohanty explains how he and his team are bringing software best practices and automation to the world of machine learning data preparation and how it allows data engineers to be involved in the process. Data labeling is a large and competitive market.

Machine Learning 100

Machine Learning Pipeline-centric Database-centric MongoDB 100

Data Engineering Podcast

NOVEMBER 11, 2019

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode.

Designing 100

Designing Data Pipeline Programming Language Data 100

Cloudera

MARCH 31, 2021

In this first Google Cloud release, CDP Public Cloud provides built-in Data Hub definitions (see screenshot for more details) for: Data Ingestion (Apache NiFi, Apache Kafka). Data Preparation (Apache Spark and Apache Hive) . A provisioning Service Account with these roles assigned. Virtual Machines . Attached Disks.

Google Cloud 94

Google Cloud Cloud Amazon Web Services Cloud Storage 94

AltexSoft

MAY 27, 2022

Data preparation for LOS prediction. As with any ML initiative, everything starts with data. Inpatient data anonymization. It contains de-identified data associated with over 200,000 ICU patient stays and covers. As with MIMIC, you have to complete a training course and sign the data use agreement for the project.

Hospitality 98

Hospitality Medical Healthcare Algorithm 98

KDnuggets

NOVEMBER 21, 2019

This blog shows how text data representations can be used to build a classifier to predict a developer’s deep learning framework of choice based on the code that they wrote, via examples of TensorFlow and PyTorch projects.

Process 115

Process Deep Learning Data Coding 115

Cloudera

DECEMBER 16, 2022

This helps our customers quickly implement an unified data fabric architecture. 5-Integrated open data collection. This differentiator solves a major technical challenge for data projects. With Cloudera, enterprises can collect all data, and run all data workloads with a single data platform.

Database 92

Database Cloud Systems Management 92

Knowledge Hut

DECEMBER 22, 2023

Spotlight on Augmented Analytics Also hailed as the future of Business Intelligence, Augmented analytics employs machine learning/ artificial intelligence (ML/AI) techniques to automate data preparation, insight discovery and sharing, data science and ML model development, management and deployment. from 2018 to 2025.

Data Science 98

Data Science Deep Learning Machine Learning Business Intelligence 98

Knowledge Hut

MAY 1, 2024

It is important to make use of this big data by processing it into something useful so that the organizations can use advanced analytics and insights to their advant age (generating better profits, more customer-reach, and so on). These steps will help understand the data, extract hidden patterns and put forward insights about the data.

Algorithm 98

Algorithm Data Science Raw Data Computer Science 98

Confluent

JUNE 18, 2019

Software projects of all sizes and complexities have a common challenge: building a scalable solution for search. Disclaimer: There are nice projects around like PostgreSQL full-text search that might be enough for your use case, and you should certainly consider them. You might be wondering, is this a good solution? Direct indexing.

Architecture 97

Architecture Building Kafka Database-centric 97

KDnuggets

DECEMBER 4, 2019

Let’s say your project is humongous and needs data labeling to be done continuously - while you’re on-the-go, sleeping, or eating. I’m sure you’d appreciate User-generated Data Labeling. I’ve got 6 interesting examples to help you understand this, let’s dive right in!

Data 108

Data Project Data Preparation Data Science 108

Knowledge Hut

JANUARY 29, 2024

On the other hand, data science is a technique that collects data from various resources for data preparation and modeling for extensive analysis. Cloud Computing provides storage, scalable compute, and network bandwidth to handle substantial data applications. Should I Learn Cloud Computing or Data Science?

Cloud Computing 98

Cloud Computing Data Science Cloud Amazon Web Services 98

Hevo

JUNE 6, 2023

Data preparation is generally the most difficult, expensive, and time-consuming task in a typical analytics project. Data sets may include fragmented and incomplete data, data with the absence of any structural consistency, etc.

BI 52

BI Data Preparation Project Data 52

Knowledge Hut

APRIL 22, 2024

With virtual environments, you may create a separate area on your server for your Python projects, allowing each of them to have a unique set of dependencies that won't interfere with any others. Using the most recent Python version while running older Python scripts and testing functionality for newer projects is advantageous.

Python 98

Python Programming Language Data Science Programming 98

ProjectPro

FEBRUARY 8, 2023

Let us dive deeper into this data integration solution by AWS and understand how and why big data professionals leverage it in their data engineering projects. Then Redshift can be used as a data warehousing tool for this. You will use Python to build tables with crawlers in the AWS Glue Data Catalog.

AWS 98

AWS Scala Metadata Data Lake 98

Cloudera

APRIL 10, 2021

When working on complex, or rigorous enterprise machine learning projects, Data Scientists and Machine Learning Engineers experience various degrees of processing lag training models at scale. While model training on small data can typically take minutes, doing the same on large volumes of data can take hours or even weeks.

Machine Learning 64

Machine Learning Data Science Deep Learning Utilities 64

DataKitchen

SEPTEMBER 20, 2021

Data professionals could do amazing things, but not while held back by bureaucratic processes. An organization is unlikely to surpass competitors and overcome obstacles while its highly trained data scientists are hand-executing data preparation, ingestion and other pipelines. . Prepackaged data sets.

Data Pipeline 98

Data Pipeline Process Data Cleanse Architecture 98

AltexSoft

MAY 12, 2021

Otherwise, let’s proceed to the first and most fundamental step in building AI-fueled computer vision tools — data preparation. Computer vision requires plenty of quality data, diverse in gender, race, and geography. Data de-identification / anonymization. Medical image databases: abundant but hard to access.

Medical 72

Medical Healthcare Datasets Machine Learning 72

Knowledge Hut

OCTOBER 4, 2023

Power BI has allowed me to contribute to various pragmatic projects across various domains, from data loading to visualization. I have read that the global data sphere will hold around 80zb of data in 2021. It will ingest the data through Power BI and leverage the complete power of machine learning for easy collaboration.

BI 52

BI Systems Raw Data Certification 52

Towards Data Science

JUNE 27, 2023

Erroneous typesets directly impact data preparation decisions, cause incompatibilities between different chunks of data, and even when passing silently, they might compromise certain operations that output nonsensical results in return.

Pipeline-centric 98

Pipeline-centric Data Science Machine Learning Datasets 98

Knowledge Hut

MARCH 19, 2024

Data Preparation: The Machine Learning Engineer Software engineers get, clean, and process data so that it can be used in machine learning models. Machine Learning Fundamentals: Leverage the knowledge of machine learning algorithms, techniques, and frameworks from online courses , tutorials, and projects.

Software Engineer 52

Software Engineer Software Engineering Machine Learning Engineering 52

AltexSoft

JUNE 14, 2021

Engage data scientists to make the proof of concept and carry out A/B tests. Or your data quality may be low to reach project feasibility. We’ve talked about that while explaining how to approach ROI calculations with machine learning projects. You’ll need a data engineering team for that.

Insurance 88

Insurance Medical Machine Learning Data Collection 88

DataKitchen

SEPTEMBER 18, 2024

According to DataKitchen’s 2024 market research, conducted with over three dozen data quality leaders, the complexity of data quality problems stems from the diverse nature of data sources, the increasing scale of data, and the fragmented nature of data systems.

Data 70

Data Data Lake Manufacturing Machine Learning 70