Basic Statistics and Data Mining for Data Science [Video]

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Basic Statistics and Data Mining for Data Science [Video]

Jesus Salcedo

Enter the world of Statistics, Data Analysis and Data Science!

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Video Details

ISBN 139781788476782
Course Length2 hours and 58 minutes

Video Description

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.

Style and Approach

This is an application-oriented course with a practical approach. This course discusses situations in which you would use each statistical technique, the assumptions made by the method, how to set up the analysis, and how to interpret the results. No proofs will be derived, but rather the focus will be on the practical matters of data analysis in support of answering research questions.

Table of Contents

The Basics of Analyzing Data
The Course Overview
Basic Steps of Data Analysis
Measurement Level and Descriptive Statistics
Summarizing Individual Variables
Reasons for Summarizing Individual Variables
Obtaining Frequencies and Summary Statistics
Data Distributions
Visualizing Data
Understanding Inferential Statistics
Hypothesis Testing and Probability
Statistical Outcomes
Digging into Chi-square Tests of Independence
Chi-square Test Theory and Assumptions
Chi-square Test of Independence Example
Post-hoc Test Example
Clustered Bar Charts
Performing T-Tests
Independent Samples T-Test: Theory and Assumptions
Independent Samples T-Test Example
Paired Samples T-Test: Theory and Assumptions
Paired Samples T-Test Example
T-Test Error Bar Charts
Exploring ANOVA
One-way ANOVA Theory and Assumptions
One-way ANOVA Example
Post-hoc Test Example
ANOVA Error Bar Charts
Working with Correlation
Pearson Correlation Coefficient Theory and Assumptions
Pearson Correlation Coefficient Example
Scatterplots

What You Will Learn

  • Get familiar with the basics of analyzing data
  • Exploring the importance of summarizing individual variables
  • Use inferential statistics
  • Know when to perform the Chi-Square test
  • Differentiate between independent and paired samples t-tests
  • Understand when to use a one-way ANOVA and post-hoc tests
  • Get well-versed with correlations

Authors

Table of Contents

The Basics of Analyzing Data
The Course Overview
Basic Steps of Data Analysis
Measurement Level and Descriptive Statistics
Summarizing Individual Variables
Reasons for Summarizing Individual Variables
Obtaining Frequencies and Summary Statistics
Data Distributions
Visualizing Data
Understanding Inferential Statistics
Hypothesis Testing and Probability
Statistical Outcomes
Digging into Chi-square Tests of Independence
Chi-square Test Theory and Assumptions
Chi-square Test of Independence Example
Post-hoc Test Example
Clustered Bar Charts
Performing T-Tests
Independent Samples T-Test: Theory and Assumptions
Independent Samples T-Test Example
Paired Samples T-Test: Theory and Assumptions
Paired Samples T-Test Example
T-Test Error Bar Charts
Exploring ANOVA
One-way ANOVA Theory and Assumptions
One-way ANOVA Example
Post-hoc Test Example
ANOVA Error Bar Charts
Working with Correlation
Pearson Correlation Coefficient Theory and Assumptions
Pearson Correlation Coefficient Example
Scatterplots

Video Details

ISBN 139781788476782
Course Length2 hours and 58 minutes
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