English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 3h 51m | 599 MB
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.
Table of Contents
1 Data science- Making sense out of chaos
2 Mapping
3 Filtering
4 Collecting
5 Sorting
6 Challenge- Combining data operations
7 Solution- Combining data operations
8 Reducing file size
9 Loading data from text files
10 Creating a person data class
11 Converting strings to data objects
12 What is data science anyway-
13 Loading tab-separated files
14 Loading CSVs
15 Converting CSVs to data objects
16 Challenge- Manipulating data
17 Solution- Manipulating data
18 Setting up JavaFX
19 Formatting data for a scatterplot
20 Displaying a scatterplot
21 Multiple datasets on a scatterplot
22 Calculating average MPG
23 Data science examples
24 Displaying a bar chart
25 Challenge- Displaying data on a bar chart
26 Solution- Displaying data on a bar chart
27 Building machine learning models
28 Supervised vs. unsupervised learning
29 Overfitting and how to avoid it
30 K-nearest neighbor basics
31 Loading flower data
32 Creating a DataItem interface
33 Calculating the closest data points
34 Data as a business asset
35 Implementing the DataItem interface
36 Letting your data points vote
37 Finishing your KNN classifier
38 Naive Bayes basics
39 Calculating the possible labels
40 Splitting your dataset by label
41 Calculating mean and standard deviation
42 Calculating datapoint probabilities
43 CRISP-DM- The data science cycle
44 Types of problems in data science
45 Data formatting in Java
46 More data formatting
47 Real-life data difficulties
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