Back to Basics - Data Science

Statistics

• Introduction to Probability:
• Probability Distributions:
• Expected Value: Code | Slides | Video
• Bayes Theorem:
• Central Limit Theorem: Code | Slides | Video
• Confidence Interval: Code | Slides | Video
• Hypothesis Testing: Code | Slides | Video
• p-value:
• Type I and Type II Errors: Code | Slides | Video
• Power of a Test:
• t-test: Code | Slides | Video
• ANOVA: Code | Slides | Video
• Chi-Square Test:
• Linear Regression:

Machine Learning

• Logistic Regression:
• Decision Trees:
• Random Forests:
• Gradient Boosting:
• Support Vector Machines:
• K-Nearest Neighbors:
• K-Means Clustering:
• Hierarchical Clustering:
• Principal Component Analysis:
• Singular Value Decomposition:

Deep Learning

• Neural Networks:
• Convolutional Neural Networks:

Natural Language Processing

• Word Embeddings:
• Word2Vec:
• GloVe:
• FastText:
• BERT:

LeetCode (Grind75)

• Two Sum

R

• tidyverse:
• ggplot2:
• dplyr:
• tidyr:
• purrr:
• readr:
• tibble:
• stringr:

Python

• numpy:
• pandas:
• matplotlib:
• seaborn:
• scikit-learn:
• scipy:
• decorator: