Dataconomy

MAY 16, 2023

They employ statistical and mathematical techniques to uncover patterns, trends, and relationships within the data. Data scientists possess a deep understanding of statistical modeling, data visualization, and exploratory data analysis to derive actionable insights and drive business decisions.

Data Scientist 156

Data Scientist ML ML Machine Learning 156

Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Snorkel AI

JULY 3, 2023

As the algorithms we use have gotten more robust and we have increased our compute power through new technologies, we haven’t made nearly as much progress on the data part of our jobs. Because of this, I’m always looking for ways to automate and improve our data pipelines. So why should we use data pipelines?

Exploratory Data Analysis 52

Exploratory Data Analysis Data Pipeline Machine Learning Machine Learning 52

Mlearning.ai

JULY 11, 2023

Before diving into the world of data science, it is essential to familiarize yourself with certain key aspects. The process or lifecycle of machine learning and deep learning tends to follow a similar pattern in most companies. Moreover, tools like Power BI and Tableau can produce remarkable results.

Data Science 40

Data Science Exploratory Data Analysis Azure Power BI 40

Snorkel AI

JULY 3, 2023

As the algorithms we use have gotten more robust and we have increased our compute power through new technologies, we haven’t made nearly as much progress on the data part of our jobs. Because of this, I’m always looking for ways to automate and improve our data pipelines. So why should we use data pipelines?

Data Pipeline 52

Data Pipeline Exploratory Data Analysis Data Scientist Machine Learning 52

Snorkel AI

JULY 3, 2023

As the algorithms we use have gotten more robust and we have increased our compute power through new technologies, we haven’t made nearly as much progress on the data part of our jobs. Because of this, I’m always looking for ways to automate and improve our data pipelines. So why should we use data pipelines?

Data Pipeline 52

Data Pipeline Exploratory Data Analysis Data Scientist Machine Learning 52

Pickl AI

SEPTEMBER 5, 2024

This section explores popular software and frameworks for Data Analysis and modelling is designed to cater to the diverse needs of Data Scientists: Azure Data Factory This cloud-based data integration service enables the creation of data-driven workflows for orchestrating and automating data movement and transformation.

Azure 52

Azure Data Scientist Data Science Machine Learning 52