Edureka

AUGUST 2, 2023

Due to its strong data analysis and manipulation skills, it has significantly increased its prominence in the field of data science. Python offers a strong ecosystem for data scientists to carry out activities like data cleansing, exploration, visualization, and modeling thanks to modules like NumPy, Pandas, and Matplotlib.

Programming Language 52

Programming Language Programming Scala Pharmaceutical 52

U-Next

SEPTEMBER 13, 2022

A multidisciplinary field called Data Science involves unprocessed data mining, its analysis, and discovering patterns utilized to extract meaningful information. The fundamental building blocks of Data Science are Statistics, Machine Learning, Computer Science, Data Analysis, Deep Learning, and Data Visualization. .

Machine Learning 40

Machine Learning Data Science Deep Learning Algorithm 40

Ascend.io

FEBRUARY 28, 2024

Data engineers implement sophisticated data cleansing, validation, and structuring techniques to ensure that the data fed into AI models is accurate and in the right format for analysis.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Edureka

FEBRUARY 8, 2024

Batch Processing- C-Series instances excel in scenarios that involve batch processing, where large amounts of data need to be processed in parallel. This is beneficial for tasks like data transformation, data cleansing, and data analysis. Accelerated Computing- Evaluate if GPU acceleration (e.g.,

AWS 52

AWS NoSQL Deep Learning Datasets 52

Knowledge Hut

JUNE 26, 2023

If you want to break into the field of data engineering but don't yet have any expertise in the field, compiling a portfolio of data engineering projects may help. Data pipeline best practices should be shown in these initiatives. Python source code for Big Data can be written.

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

Knowledge Hut

NOVEMBER 27, 2023

Developing technical skills is essential, starting with foundational knowledge in mathematics, including calculus and linear algebra, which underpin machine learning and deep learning concepts. Data cleansing / Data scrubbing Dealing with incongruous data, like misspelled categories or missing values.

Data Science 52

Data Science Datasets Machine Learning Portfolio 52

ProjectPro

DECEMBER 22, 2016

As a data analyst , I would retrain the model as quick as possible to adjust with the changing behaviour of customers or change in market conditions. 5) What is data cleansing? Mention few best practices that you have followed while data cleansing. How to save and reload a deep learning model in Pytorch?

Data Mining 52

Data Mining Data Cleanse Datasets Data Analysis 52