super data science

0:12:48

Deep Learning Simplified: How NEURAL NETWORKS Work? [Real-World Example]

0:00:48

Welcome to the Super Data Science Podcast

0:05:01

What is the future of data science?

0:00:39

SuperDataScience 2.0 New Platform - Learn Data Science

0:59:32

825: Data Contracts: The Key to Data Quality — with Chad Sanderson

0:00:46

SuperDataSceince Platform Review

0:07:09

How Gradient Boosting REALLY Works in Machine Learning

0:07:46

694: CatBoost: Powerful, efficient ML for large tabular datasets — with Jon Krohn (@JonKrohnLearns)

0:14:00

Extract, Analyze & Visualize Financial Data to Compare by Year (Python Step-by-Step)

0:05:09

What is an Analytics Engineer, with Colleen Fotsch

1:07:53

657: How to Learn Data Engineering — with Andreas Kretz (@andreaskayy)

0:05:55

Master Ensemble Models: Bagging vs Boosting in Machine Learning EXPLAINED

1:31:57

793: Bayesian Methods and Applications — with Alexandre Andorra

0:06:10

760: Humans Love AI-Crafted Beer — with Jon Krohn (@JonKrohnLearns)

1:21:40

833: The 10 Reasons AI Projects Fail — with Dr. Martin Goodson

0:04:28

SDS 552: The Most Popular SuperDataScience Episodes of 2021 — with Jon Krohn

0:07:12

What Makes a Good AI Startup Founder? (with Shaun Johnson)

1:02:37

696: Brain-Computer Interfaces and Neural Decoding — with Prof. Bob Knight

0:00:58

Why All Data Scientist Should Use R

0:05:39

The Secret to Landing Your First Data Job, with Colleen Fotsch

0:59:07

897: How to Enable Enterprise AI Transformation, with Strategy Consultant — with Diane Hare

0:00:37

The Importance of Data Quality in Data Science

0:00:37

The Future of Data Science Collaboration: Zerve's Approach

0:00:36

What’s Ahead for AI and Data Science Teams