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

ODSC - Open Data Science

MARCH 12, 2025

20212024: Interest declined as deep learning and pre-trained models took over, automating many tasks previously handled by classical ML techniques. While traditional machine learning remains fundamental, its dominance has waned in the face of deep learning and automated machine learning (AutoML).

Data Science 52

Data Science Machine Learning Machine Learning Data Engineering 52

ODSC - Open Data Science

APRIL 6, 2023

One set of tools that are becoming more important in our data-driven world is BI tools. Think of Tableau, Power BI, and QlikView. These are used to extract, transform, and load (ETL) data between different systems. Each of these creates visualizations and reports based on data stored in a database.

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. ETL Tools: Apache NiFi, Talend, etc. TensorFlow, Scikit-learn, Pandas, NumPy, Jupyter, etc. Read more to know.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

AUGUST 9, 2024

Understanding ETL (Extract, Transform, Load) processes is vital for students. Unsupervised Learning Exploring clustering techniques like k-means and hierarchical clustering, along with dimensionality reduction methods such as PCA (Principal Component Analysis). Students should learn about neural networks and their architecture.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52