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 …
Basic Statistics and Data Mining for Data Science
Video description
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
In Detail
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.
Audience
This course is for developers who are interested in entering the field of data science and are looking for a guide to the statistical concepts.
Chapter 4 : 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
Chapter 5 : 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
Chapter 6 : Exploring ANOVA
One-way ANOVA Theory and Assumptions
One-way ANOVA Example
Post-hoc Test Example
ANOVA Error Bar Charts
Chapter 7 : Working with Correlation
Pearson Correlation Coefficient Theory and Assumptions
Pearson Correlation Coefficient Example
Scatterplots
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