Big Data Tools Deep Learning Programming Language 
Knowledge Hut
DECEMBER 26, 2023
Certain roles like Data Scientists require a good knowledge of coding compared to other roles. Data Science also requires applying Machine Learning algorithms, which is why some knowledge of programming languages like Python, SQL, R, Java, or C/C++ is also required.
Knowledge Hut
MAY 23, 2024
SAS: SAS is a popular data science tool designed by the SAS Institute for advanced analysis, multivariate analysis, business intelligence (BI), data management operations, and predictive analytics for future insights. A lot of MNCs and Fortune 500 companies are utilizing this tool for statistical modeling and data analysis.
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ProjectPro
JANUARY 19, 2022
Good skills in computer programming languages like R, Python, Java, C++, etc. Good knowledge of various machine learning and deep learning algorithms will be a bonus. Good knowledge of various machine learning and deep learning algorithms will be a bonus.
ProjectPro
FEBRUARY 25, 2022
The Need for MLOps: Understanding a Data Science Project’s Workflow A data science project involves the below-mentioned steps that you should follow in sequential order. These steps are: Cleaning the data and handling different file formats. Experience with using Big Data tools for a data science project deployment.
Knowledge Hut
JUNE 23, 2023
Data engineers make a tangible difference with their presence in top-notch industries, especially in assisting data scientists in machine learning and deep learning. Data warehousing to aggregate unstructured data collected from multiple sources.
AltexSoft
FEBRUARY 11, 2023
It serves as a foundation for the entire data management strategy and consists of multiple components including data pipelines; , on-premises and cloud storage facilities – data lakes , data warehouses , data hubs ;, data streaming and Big Data analytics solutions ( Hadoop , Spark , Kafka , etc.);
Knowledge Hut
JUNE 20, 2023
Skills A data engineer should have good programming and analytical skills with big data knowledge. A machine learning engineer should know deep learning, scaling on the cloud, working with APIs, etc. Examples Pull daily tweets from the data warehouse hive spreading in multiple clusters.
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