Data Science Dojo

OCTOBER 31, 2024

Key Skills: Mastery in machine learning frameworks like PyTorch or TensorFlow is essential, along with a solid foundation in unsupervised learning methods. Stanford AI Lab recommends proficiency in deep learning, especially if working in experimental or cutting-edge areas.

Data Science 195

Data Science Data Scientist Machine Learning Machine Learning 195

phData

JUNE 26, 2023

Two tools that have significantly impacted the data analytics landscape are KNIME and Tableau. Tableau, owned by Salesforce, is a leading tool for data visualization, allowing users to create interactive dashboards and reports for better data understanding and decision-making.

Tableau 52

Tableau Data Preparation Machine Learning Machine Learning 52

ODSC - Open Data Science

APRIL 6, 2023

Think of Tableau, Power BI, and QlikView. These are used to extract, transform, and load (ETL) data between different systems. This allows for it to be integrated with many different tools and technologies to improve data management and analysis workflows. Data integration tools allow for the combining of data from multiple sources.

SQL 98

SQL Data Scientist Database Data Science 98

Data Science Dojo

JULY 3, 2024

Tools like Tableau, Power BI, and Python libraries such as Matplotlib and Seaborn are commonly taught. Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

ODSC - Open Data Science

APRIL 3, 2023

Data Wrangling: Data Quality, ETL, Databases, Big Data The modern data analyst is expected to be able to source and retrieve their own data for analysis. Competence in data quality, databases, and ETL (Extract, Transform, Load) are essential. As you see, there are a number of reporting platforms as expected.

Analytics 52

Analytics Analytics Data Analyst Data Science 52

Pickl AI

JULY 25, 2023

They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Machine Learning: Supervised and unsupervised learning techniques, deep learning, etc. Data Visualization: Matplotlib, Seaborn, Tableau, etc. ETL Tools: Apache NiFi, Talend, etc. Read more to know.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

APRIL 26, 2024

Data Warehousing and ETL Processes What is a data warehouse, and why is it important? Explain the Extract, Transform, Load (ETL) process. The ETL process involves extracting data from source systems, transforming it into a suitable format or structure, and loading it into a data warehouse or target system for analysis and reporting.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52