Introduction

ABCs of data science is intended for anyone who wants to learn more about data science, regardless of skill level. It aims to give readers a high level overview of various data science concepts, so that they can explore these topics further.

Please leave a comment on the blogs if you think I’m missing something or if there is something you want me to cover. You can also follow the ABC’s of data science twitter account or the RSS feed to get notified when new posts are available.

• A is for Artificial Intelligence
• B is for Bias
• C is for Clustering
• D is for Deep Learning
• E is for Embeddings
• F is for F1 Score
• G is for Gradient Descent
• H is for HDBSCAN
• I is for Interpretability
• J is for Jaccard Metric
• K is for K-fold Cross-Validation
• L is for Labelling Data
• M is for Munging Data
• N is for Natural Language Processing
• O is for Outlier Detection
• P is for Pandas
• Q is for Q-learning
• R is for Reproducibility
• S is for Supervised Learning
• T is for Transfer Learning
• U is for UMAP
• V is for Visualization
• W is for Wasserstein GANs
• X is for XGBoost
• Y is for You Should Talk to Your Clients
• Z is for Zero to Done

Posts

• Z is for Zero to Done

  Feb 6, 2021

• Y is for You Should Talk to Your Clients

  Feb 5, 2021

• X is for XGBoost

  Feb 5, 2021

• W is for Wasserstein GANs

  Feb 4, 2021

• V is for Visualization

  Feb 3, 2021

• U is for UMAP

  Feb 1, 2021

• T is for Transfer Learning

  Jan 31, 2021

• S is for Supervised Learning

  Jan 17, 2021

• R is for Reproducibility

  Dec 28, 2020

• Q is for Q-learning

  Dec 27, 2020

• P is for Pandas

  Oct 25, 2020

• O is for Outlier Detection

  Oct 23, 2020

• N is for Natural Language Processing (NLP)

  Oct 18, 2020

• M is for Munging Data

  Oct 10, 2020

• L is for Labelling Data

  Oct 9, 2020