Enter the world of Statistics, Data Analysis and Data Science!
About This Video
- This comprehensive video tutorial will ensure that you build on your knowledge of statistics and learn how to apply it in the field of data science
- You’ll learn when to use different statistical techniques, how to set up different analyses, and how to interpret the results
- This video course follows a step-by-step approach to ensure that you get the basics right
Data science is an ever-evolving field, with exponentially growing popularity. Data science includes techniques and theories extracted from the fields of statistics, computer science, and most importantly machine learning, databases, and visualization.
This video course consists of step-by-step introductions to analyze data and the basics of statistics. The first chapter focuses on the steps to analyze data and which summary statistics are relevant given the type of data you are summarizing. The second chapter continues by focusing on summarizing individual variables and specifically some of the reasons users need to summarize variables. This chapter also illustrates several procedures, such as how to run and interpret frequencies and how to create various graphs. The third chapter introduces the idea of inferential statistics, probability, and hypothesis testing.
The rest of the chapters show you how to perform and interpret the results of basic statistical analyses (chi-square, independent and paired sample t-tests, one-way ANOVA, post-hoc tests, and bivariate correlations) and graphical displays (clustered bar charts, error bar charts, and scatterplots). You will also learn when to use different statistical techniques, how to set up different analyses, and how to interpret the results.
Table of Contents
- Chapter 1 : The Basics of Analyzing Data
- Chapter 2 : Summarizing Individual Variables
- Chapter 3 : Understanding Inferential Statistics
- Chapter 4 : Digging into Chi-square Tests of Independence
Chapter 5 : Performing T-Tests
- Independent Samples T-Test: Theory and Assumptions 00:09:39
- Independent Samples T-Test Example 00:05:48
- Paired Samples T-Test: Theory and Assumptions 00:05:49
- Paired Samples T-Test Example 00:02:19
- T-Test Error Bar Charts 00:02:36
- Chapter 6 : Exploring ANOVA
Chapter 7 : Working with Correlation
- Pearson Correlation Coefficient Theory and Assumptions 00:10:11
- Pearson Correlation Coefficient Example 00:04:04
- Scatterplots 00:06:27
- Title: Basic Statistics and Data Mining for Data Science
- Release date: December 2017
- Publisher(s): Packt Publishing
- ISBN: 9781788476782