Challenge: Combining data operations - Java Tutorial

Contents

• Introduction Introduction

  • Data science: Making sense out of chaos

    50s
• 1. Data Science Basics 1. Data Science Basics

  • What is data science anyway?

    4m 57s
  • Data science examples

    4m
  • Data as a business asset

    2m 57s
  • CRISP-DM: The data science cycle

    6m 24s
  • Types of problems in data science

    7m 31s
• 2. Representing Data in Java 2. Representing Data in Java

  • Data formatting in Java

    7m 13s
  • More data formatting

    5m 43s
  • Real-life data difficulties

    5m 30s
• 3. Data Manipulation Techniques 3. Data Manipulation Techniques

  • Mapping

    5m 55s
  • Filtering

    5m 21s
  • Collecting

    3m 21s
  • Sorting

    4m 54s
  • Challenge: Combining data operations

    36s
  • Solution: Combining data operations

    1m 54s
• 4. Loading Data in Java 4. Loading Data in Java

  • Reducing file size

    5m 20s
  • Loading data from text files

    3m 29s
  • Creating a person data class

    3m 56s
  • Converting strings to data objects

    5m 39s
  • Loading tab-separated files

    9m 20s
  • Loading CSVs

    5m 21s
  • Converting CSVs to data objects

    7m 4s
  • Challenge: Manipulating data

    2m 1s
  • Solution: Manipulating data

    3m 37s
• 5. Data Visualization with JavaFX 5. Data Visualization with JavaFX

  • Setting up JavaFX

    5m 38s
  • Formatting data for a scatterplot

    7m 55s
  • Displaying a scatterplot

    5m 22s
  • Multiple datasets on a scatterplot

    6m 35s
  • Calculating average MPG

    4m 46s
  • Displaying a bar chart

    4m 17s
  • Challenge: Displaying data on a bar chart

    1m 15s
  • Solution: Displaying data on a bar chart

    4m 8s
• 6. Modeling and Machine Learning 6. Modeling and Machine Learning

  • Building machine learning models

    4m 24s
  • Supervised vs. unsupervised learning

    3m 9s
  • Overfitting and how to avoid it

    7m 12s
• 7. K-Nearest Neighbors (KNN) 7. K-Nearest Neighbors (KNN)

  • K-nearest neighbor basics

    7m 35s
  • Loading flower data

    6m 45s
  • Creating a DataItem interface

    4m 56s
  • Calculating the closest data points

    4m 22s
  • Implementing the DataItem interface

    2m 13s
  • Letting your data points vote

    4m 19s
  • Finishing your KNN classifier

    3m 31s
• 8. Naive Bayes 8. Naive Bayes

  • Naive Bayes basics

    6m 30s
  • Calculating the possible labels

    5m 14s
  • Splitting your dataset by label

    6m 53s
  • Calculating mean and standard deviation

    5m 7s
  • Calculating datapoint probabilities

    6m 14s