Advanced Exploratory Analysis Bootcamp By Spotle

What you'll learn
Unsupervised Learning
Exploratory Analysis
Cluster Analysis
K-means Clustering
Hierarchical Clustering
Factor Analysis
understanding, measuring and preparing data
Overview of R programming
Data visualization in R
Requirements
You will need to have a computer or a mobile handset with an internet connection
Description
Machine learning and data science have become key industry drivers in the global job and opportunity market. This course with mix of lectures from industry experts and Ivy League academics will help students, recent graduates and young professionals learn advanced exploratory analysis and its applications in business scenarios. In this course you will learn:1. Data science overview2. Types of machine learning3. Supervised and unsupervised machine learning and their differences4. Application of supervised and unsupervised machine learning5. Semi-supervised machine learning6. understanding, measuring and preparing data for analysis7. Cluster analysis8. Features of cluster analysis9. k-Means clustering10. Hierarchical clustering11. Hierarchical clustering case studies12. Factor analysis13. Overview of R Programming language14. Data visualization in RWhat is unsupervised learning?Unsupervised learning is the learning of a machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance.We will take an example to understand the unsupervised learning process. Let's say, you are traveling to Amazon. There are many animals, snakes, birds and insects that you have never ever seen in your life. Now, in there you see a new small bird that you have never seen before. No one tells you that it is a bird not a large size insect. You can still make out that it is a bird because it has feathers, it has beak, it can fly etc. No one has taught you about it by labeling it as a bird but you learn from unlabeled data. This is unsupervised learning. The phases of learning are pretty simple. You have input data, you have your algorithm that categorizes, and then you have the output.
Overview
Section 1: Introduction To Data Science
Lecture 1 Data Science Overview
Section 2: Unsupervised And Semi-supervised Learning
Lecture 2 Unsupervised Learning
Lecture 3 Semi-supervised Learning
Section 3: Understanding And Preparing Data
Lecture 4 Measuring Central Tendency
Lecture 5 Measuring Skewness And Kurtosis
Lecture 6 Missing Data Imputation - Part 1
Lecture 7 Missing Data Imputation - Part 2
Section 4: Cluster Analysis
Lecture 8 Introduction to Cluster Analysis
Lecture 9 Features Of Cluster Analysis
Section 5: K-means Clustering
Lecture 10 K-means Clustering
Section 6: Hierarchical Clustering
Lecture 11 Hierarchical Clustering - Part 1
Lecture 12 Hierarchical Clustering Case Studies
Lecture 13 Hierarchical Clustering - Part 2
Section 7: Factor Analysis
Lecture 14 Introduction To Factor Analysis
Lecture 15 Factor Analysis - Part 2
Section 8: Overview Of R For Data Science
Lecture 16 Introduction To R - Part 1
Lecture 17 Introduction To R - Part 2
Lecture 18 Data Visualization With R
Anyone with an interest in a rewarding career in Data Science


Homepage

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