Knowledge Hut

DECEMBER 22, 2023

Statistics are important for analyzing and interpreting the data. Programming: There are many programming languages out there that were created for different purposes. Some offer great productivity and performance to process significant amounts of data, making them better suitable for data science.

Portfolio 98

Portfolio Data Science Programming Language Scala 98

Data Engineering Podcast

NOVEMBER 18, 2019

Summary With the constant evolution of technology for data management it can seem impossible to make an informed decision about whether to build a data warehouse, or a data lake, or just leave your data wherever it currently rests. Raghu Murthy, founder and CEO of Datacoral built data infrastructures at Yahoo!

Data Lake 100

Data Lake Scala Data Warehouse Hadoop 100

ProjectPro

MARCH 7, 2016

Acquiring big data analytics certifications in specific big data technologies can help a candidate improve their possibilities of getting hired. It is necessary for individuals to bridge the wide gap between the academia big data programs and the industry practices.

Big Data 40

Big Data Certification Hadoop Big Data Skills 40

Edureka

JANUARY 18, 2024

Explore real-world examples, emphasizing the importance of statistical thinking in designing experiments and drawing reliable conclusions from data. Programming A minimum of one programming language, such as Python, SQL, Scala, Java, or R, is required for the data science field.

Data Science 40

Data Science Deep Learning NoSQL Machine Learning 40

AltexSoft

OCTOBER 30, 2021

ML models are designed by data scientists, but data engineers deploy those into production. They set up resources required by the model, create pipelines to connect them with data, manage computer resources, and monitor and configure the model’s performance. Managing data and metadata. Programming.

Data Engineering 98

Data Engineering Data Engineer Engineering Machine Learning 98