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

AltexSoft

MAY 14, 2021

Data cleansing. Before getting thoroughly analyzed, data ? In a nutshell, the data cleansing process involves scrubbing for any errors, duplications, inconsistencies, redundancies, wrong formats, etc. and as such confirming the usefulness and relevance of data for analytics. whether small or big ?

Big Data 78

Big Data Data Analytics IT NoSQL 78

ProjectPro

DECEMBER 22, 2016

If you are aspiring to be a data analyst then the core competencies that you should be familiar with are distributed computing frameworks like Hadoop and Spark, knowledge of programming languages like Python, R , SAS, data munging, data visualization, math , statistics , and machine learning.

Data Mining 52

Data Mining Data Cleanse Datasets Data Analysis 52

ProjectPro

JANUARY 31, 2023

Big Data Architect Interview Questions and Answers Following are the interview questions for big data architects that will help you ace your next job interview. Explain the data preparation process. Data preparation is one of the essential steps in a big data project. Steps for Data preparation.

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. There are three stages in this real-world data engineering project. Data ingestion: In this stage, you get data from Yelp and push the data to Azure Data lake using DataFactory.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Knowledge Hut

NOVEMBER 27, 2023

Additionally, proficiency in probability, statistics, programming languages such as Python and SQL, and machine learning algorithms are crucial for data science success. Through the article, we will learn what data scientists do, and how to transits to a data science career path.

Data Science 52

Data Science Datasets Machine Learning Portfolio 52