Data Science for Java Developers

Learn how to turn data into information, using one of the world's most popular programming languages.

Introduction

• Data science: Making sense out of chaos

1. Data Science Basics

• What is data science anyway?
• Data science examples
• Data as a business asset
• CRISP-DM: The data science cycle
• Types of problems in data science

2. Representing Data in Java

• Data formatting in Java
• More data formatting
• Real-life data difficulties

3. Data Manipulation Techniques

• Mapping
• Filtering
• Collecting
• Sorting
• Challenge: Combining data operations
• Solution: Combining data operations

4. Loading Data in Java

• Reducing file size
• Loading data from text files
• Creating a person data class
• Converting strings to data objects
• Loading tab-separated files
• Loading CSVs
• Converting CSVs to data objects
• Challenge: Manipulating data
• Solution: Manipulating data

5. Data Visualization with JavaFX

• Setting up JavaFX
• Formatting data for a scatterplot
• Displaying a scatterplot
• Multiple datasets on a scatterplot
• Calculating average MPG
• Displaying a bar chart
• Challenge: Displaying data on a bar chart
• Solution: Displaying data on a bar chart

6. Modeling and Machine Learning

• Building machine learning models
• Supervised vs. unsupervised learning
• Overfitting and how to avoid it

7. K-Nearest Neighbors (KNN)

• K-nearest neighbor basics
• Loading flower data
• Creating a DataItem interface
• Calculating the closest data points
• Implementing the DataItem interface
• Letting your data points vote
• Finishing your KNN classifier

8. Naive Bayes

• Naive Bayes basics
• Calculating the possible labels
• Splitting your dataset by label
• Calculating mean and standard deviation
• Calculating datapoint probabilities