Knowledge Hut

FEBRUARY 1, 2024

You cannot expect your analysis to be accurate unless you are sure that the data on which you have performed the analysis is free from any kind of incorrectness. Data cleaning in data science plays a pivotal role in your analysis. It’s a fundamental aspect of the data preparation stages of a machine learning cycle.

Data Science 52

Data Science Process Data Cleanse Datasets 52

Knowledge Hut

JANUARY 30, 2024

Whether it's aggregating customer interactions, analyzing historical sales trends, or processing real-time sensor data, data extraction initiates the process. Utilizes structured data or datasets that may have already undergone extraction and preparation. Analyzing and deriving valuable insights from data.

ETL Tools 52

ETL Tools Database-centric Data Mining Raw Data 52

Knowledge Hut

NOVEMBER 27, 2023

This data may come from surveys, or through popular automatic data collection methods, like using cookies on a website. Class-label the observations This consists of arranging the data by categorizing or labelling data points to the appropriate data type such as numerical, or categorical data.

Data Science 52

Data Science Datasets Machine Learning Portfolio 52

AltexSoft

MAY 14, 2021

Big Data analytics processes and tools. Data ingestion. The process of identifying the sources and then getting Big Data varies from company to company. It’s worth noting though that data collection commonly happens in real-time or near real-time to ensure immediate processing. Data cleansing.

Big Data 78

Big Data Data Analytics IT NoSQL 78

ProjectPro

JANUARY 31, 2023

There are three steps involved in the deployment of a big data model: Data Ingestion: This is the first step in deploying a big data model - Data ingestion, i.e., extracting data from multiple data sources. It ensures that the data collected from cloud sources or local databases is complete and accurate.

Big Data 52

Big Data Hadoop Relational Database AWS 52

ProjectPro

AUGUST 24, 2021

This project is an opportunity for data enthusiasts to engage in the information produced and used by the New York City government. Learn how to use various big data tools like Kafka, Zookeeper, Spark, HBase, and Hadoop for real-time data aggregation. There are three stages in this real-world data engineering project.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

ProjectPro

OCTOBER 20, 2021

This would include the automation of a standard machine learning workflow which would include the steps of Gathering the data Preparing the Data Training Evaluation Testing Deployment and Prediction This includes the automation of tasks such as Hyperparameter Optimization, Model Selection, and Feature Selection. Explain further.

Machine Learning 52

Machine Learning Algorithm Data Science Government 52