Data Science for Java Developers

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Shaun Wassell

3:51:13

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01 Introduction

001 Data science Making sense out of chaos.mp4

00:50

02 Data Science Basics

001 What is data science anyway .mp4

04:57

002 Data science examples.mp4

04:00

003 Data as a business asset.mp4

02:57

004 CRISP-DM The data science cycle.mp4

06:24

005 Types of problems in data science.mp4

07:31

03 Representing Data in Java

001 Data formatting in Java.mp4

07:13

002 More data formatting.mp4

05:43

003 Real-life data difficulties.mp4

05:30

04 Data Manipulation Techniques

001 Mapping.mp4

05:55

002 Filtering.mp4

05:21

003 Collecting.mp4

03:21

004 Sorting.mp4

04:54

005 Challenge Combining data operations.mp4

00:36

006 Solution Combining data operations.mp4

01:54

05 Loading Data in Java

001 Reducing file size.mp4

05:20

002 Loading data from text files.mp4

03:29

003 Creating a person data class.mp4

03:56

004 Converting strings to data objects.mp4

05:39

005 Loading tab-separated files.mp4

09:20

006 Loading CSVs.mp4

05:21

007 Converting CSVs to data objects.mp4

07:04

008 Challenge Manipulating data.mp4

02:01

009 Solution Manipulating data.mp4

03:37

06 Data Visualization with JavaFX

001 Setting up JavaFX.mp4

05:38

002 Formatting data for a scatterplot.mp4

07:55

003 Displaying a scatterplot.mp4

05:22

004 Multiple datasets on a scatterplot.mp4

06:35

005 Calculating average MPG.mp4

04:46

006 Displaying a bar chart.mp4

04:17

007 Challenge Displaying data on a bar chart.mp4

01:15

008 Solution Displaying data on a bar chart.mp4

04:08

07 Modeling and Machine Learning

001 Building machine learning models.mp4

04:24

002 Supervised vs. unsupervised learning.mp4

03:09

003 Overfitting and how to avoid it.mp4

07:12

08 K-Nearest Neighbors (KNN)

001 K-nearest neighbor basics.mp4

07:35

002 Loading flower data.mp4

06:45

003 Creating a DataItem interface.mp4

04:56

004 Calculating the closest data points.mp4

04:22

005 Implementing the DataItem interface.mp4

02:13

006 Letting your data points vote.mp4

04:19

007 Finishing your KNN classifier.mp4

03:31

09 Naive Bayes

001 Naive Bayes basics.mp4

06:30

002 Calculating the possible labels.mp4

05:14

003 Splitting your dataset by label.mp4

06:53

004 Calculating mean and standard deviation.mp4

05:07

005 Calculating datapoint probabilities.mp4

06:14

Description

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.

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Java

Data Science

Shaun Wassell

Instructor's Courses

"I love seeing people go from earning peanuts to being able to comfortably take their families on multiple vacations — just because they were willing to learn something 'nerdy!'"

Shaun brings nearly 10 years of software development experience to his training. Prior to joining CBT Nuggets, he was a senior full-stack developer. His interest in technology started as a child because he wanted to create video games and his parents bought him GameMakerStudio. When Shaun isn’t creating training, he enjoys gardening, distance running, investing, and learning foreign languages.

Certifications: None

Areas of expertise: Web development, programming, data science

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