Data Science for Java Developers

Course details

Learning the basics of data science and how to apply them in Java opens up a world of possibilities for you, in terms of building software and job opportunities. In this course, instructor Shaun Wassell takes you through the skill sets required for data science, shows you how to visualize data in Java, and explores different methods of turning data into information. Shaun introduces some basic concepts and examples of data science, then walks you through the process of representing data in Java and some difficulties you may encounter. He discusses data manipulation techniques like mapping, filtering, collecting, and sorting. Shaun describes how to find, gather, clean, manipulate, and store data, so that you can start doing useful things with it. Next, he shows you the fun part: different methods you can use to turn data into information. Shaun covers Nearest-Neighbor, Bayes, linear regression, decision trees, clustering, and more.

Concepts

0. Introduction

01 - Data science - Making sense out of chaos

1. Data Science Basics

02 - What is data science anyway
03 - Data science examples
04 - Data as a business asset
05 - CRISP-DM - The data science cycle
06 - Types of problems in data science

2. Representing Data in Java

07 - Data formatting in Java
08 - More data formatting
09 - Real-life data difficulties

3. Data Manipulation Techniques

10 - Mapping
11 - Filtering
12 - Collecting
13 - Sorting
14 - Challenge - Combining data operations
15 - Solution - Combining data operations

4. Loading Data in Java

16 - Reducing file size
17 - Loading data from text files
18 - Creating a person data class
19 - Converting strings to data objects
20 - Loading tab-separated files
21 - Loading CSVs
22 - Converting CSVs to data objects
23 - Challenge - Manipulating data
24 - Solution - Manipulating data

5. Data Visualization with JavaFX

25 - Setting up JavaFX
26 - Formatting data for a scatterplot
27 - Displaying a scatterplot
28 - Multiple datasets on a scatterplot
29 - Calculating average MPG
30 - Displaying a bar chart
31 - Challenge - Displaying data on a bar chart
32 - Solution - Displaying data on a bar chart

6. Modeling and Machine Learning

33 - Building machine learning models
34 - Supervised vs. unsupervised learning
35 - Overfitting and how to avoid it

7. K-Nearest Neighbors (KNN)

36 - K-nearest neighbor basics
37 - Loading flower data
38 - Creating a DataItem interface
39 - Calculating the closest data points
40 - Implementing the DataItem interface
41 - Letting your data points vote
42 - Finishing your KNN classifier

8. Naive Bayes

43 - Naive Bayes basics
44 - Calculating the possible labels
45 - Splitting your dataset by label
46 - Calculating mean and standard deviation
47 - Calculating datapoint probabilities