by James D. Miller | Released April 2018 | ISBN: 9781789345339

Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural NetworksAbout This VideoNo need to take a degree in statistics, just go through this course and get a strong statistics base for data science and real-world programs;Implement statistics in data science tasks such as data cleaning, mining, and analysis step by stepLearn all about probability, statistics, numerical computations, and more with the help of R programsIn DetailDo you wish to be a data scientist but don't know where to begin? Want to implement statistics for data science? Want to get acquainted with R programs? Want to learn about the logic involved in computing statistics? If so, then this is the course for you.This course will take you through an entire statistics odyssey, from knowing very little to becoming comfortable with using various statistical methods with data science tasks. It starts off with simple statistics and then moves on to statistical methods that are used in data science algorithms. R programs for statistical computation are clearly explained along with the logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks.By the end of the course, you will be comfortable with performing various statistical computations for data science programmatically. Show and hide more

1. Chapter 1 : Declaring the Objectives
   • The Course Overview 00:03:45
   • Key Objectives of Data Science 00:04:44
   • Common Terminology 00:13:54
2. Chapter 2 : A Developer's Approach to Data Cleaning
   • Understanding Basic Data Cleaning 00:03:21
   • R and Common Data Issues 00:17:47
3. Chapter 3 : Data Mining and the Database Developer
   • Data Mining 00:03:03
   • Choosing R for Data Mining 00:08:12
   • Using R 00:05:41
4. Chapter 4 : Statistical Analysis for the Database Developer
   • Data Analysis 00:04:47
   • Statistical Analysis 00:05:20
   • Establishing the Nature of Data 00:03:56
5. Chapter 5 : Database Progression to Database Regression
   • Introduction to Statistical Regression 00:05:32
   • Project Profitability 00:08:13
6. Chapter 6 : Regularization for Database Improvement
   • Statistical Regularization 00:06:12
   • Improving Data or a Data Model 00:03:21
   • Using R for Statistical Regularization 00:03:27
7. Chapter 7 : Database Development and Assessment
   • Assessment and Statistical Assessment 00:03:10
   • Development 00:03:13
   • R and Statistical Assessment 00:04:07
8. Chapter 8 : Databases and Neural Networks
   • Defining Neural Network 00:05:38
   • R-Based Neural Networks 00:04:31
9. Chapter 9 : Boosting Your Database
   • Definition and Purpose 00:07:14
   • Statistical Boosting 00:06:57
10. Chapter 10 : Database Structures and Machine Learning
    • Data Structures and Data Models 00:03:33
    • Machine Learning 00:04:04
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