Databricks - Applied Data Science for Data Analysts 

Course introduction

Review of Data Science

Review of Machine Learning

Data Science Process vs. Machine Learning Workflow

Introduction to Databricks (Optional)

Introduction to the Platform (Optional)

Introduction to Apache Spark (Optional)

Introduction to Delta Lake (Optional)

Before you begin

Hands-on with Databricks Lab (Optional)

Course Introduction and Prerequisites

Lesson Introduction

Exploring Data

Visualizing Data

Introduction to K-means Clustering

Applied K-means Clustering

Identifying the Number of Clusters

Identifying the Number of Clusters Demo

Utilizing Clusters

Lesson Introduction

Feature Relationships

Correlation Matrix

Introduction to Principal Components Analysis

Applied Principal Components Analysis

PCA for Feature Relationships

PCA for Dimensionality Reduction

K-means Clustering Lab

Principal Components Analysis Lab

Exploring and Visualizing Data

K-means Clustering

K-means Clustering Lab Results

Feature Correlation

Principal Components Analysis

PCA Lab Results

Lesson Introduction

Introduction to Feature Engineering

Common Feature Improvements

Handling Missing Values

Imputing Missing Values

Feature Scaling

Converting Feature Types

Representing Categorical Features

One-hot Encoding

Lesson Introduction

Problems with High Dimensions and Dimensionality Reduction

A Review of Feature Importance

Linear Regression Coefficients and P-values

Introduction to Feature Selection

Regularization

Regularized Regression

Applied Regularized Regression

Feature Engineering Lab

Feature Selection Lab

Feature Engineering Concepts

Missing Values

Feature Engineering Lab Results

Dimensionality and Feature Importance

Feature Selection in Linear Regression

Feature Selection Lab Results

Lesson Introduction

A Review of Decision Trees

Algorithm Selection

String Indexing Categorical Features

Decision Tree Pruning

Lesson Introduction

Introduction to Ensemble Modeling

Bootstrap Sampling Training Data

Applied Random Forest

Lesson Introduction

A Review of Classification Evaluation Metrics

A Review of Assigning Classes

Oversampling and Undersampling Classes

Weighting Classes in Random Forest

Feature Engineering in Decision Trees

Preventing Overfitting

Applied Decision Trees Lab

Aggregating Bootstrapped Results

Random Forest Algorithm

Applied Random Forest Lab

Problems with Class Imbalance

Label-based Bootstrap Sampling

Label-based Evaluation Weighting

Label Imbalance Lab

Algorithm Selection and Decision Trees

Categorical Features

Applied Decision Trees Lab Results

Tree-based Ensemble Modeling

Bootstrap Aggregation

Applied Random Forest Lab Results

Classification Evaluation

Label Imbalance and Sampling

Label Imbalance Lab Results

Lesson Introduction

Introduction to Hyperparameters

Hyperparameters in Tree-based Models

Optimizing Hyperparameters

Grid Search for Hyperparameter Optimization

Validation Set

Grid-search for Random Forests

Lesson Introduction

A Review of Model Generalization

Validation Set Limitations

Introduction to Cross-Validation

K-fold Cross-Validation with Random Forest

Other Cross-Validation Strategies

Hyperparameter Search Lab

Cross-Validation Lab

Hyperparameters in Tree-based Models

Grid Search

Hyperparameter Search Lab Results

Model Generalization and Validation Set

Cross-Validation

Cross-Validation Lab Results