# Statistics for Data Analysts and Scientists

Ultimate course to master practical and business applications of essential statistical tests and concepts

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## Statistics for Data Analysts and Scientists

### What you’ll learn

Develop a deep understanding of key statistical concepts, such as homoscedasticity of variance, multicollinearity, and homogeneity of variance
Gain the skills to apply statistical tests and concepts to real-world situations and communicate insights to key stakeholders
Understand how statistical tools can be used to gain insights into complex data sets and how these insights can be used to drive critical business decisions
Learn through practical examples, and case studies that will help them understand how statistical tests can be applied in real-world situations
Gain the confidence and expertise to excel as a data analyst or scientist, and apply statistical methods to a wide range of data-driven challenges
Understand the practical and business applications of essential statistical tests, including the Chi Square test, t-tests, correlation, Regression, etc
Learn how to conduct and interpret key statistical tests, including one-sample t-test, independent sample test, correlation and linear regression, OneWay ANOVA

### Requirements

No programming or prior experience of statistics required

### Description

Welcome to “Statistics for Data Analysts and Scientists” – the ultimate course to help you master the practical and business applications of essential statistical tests and concepts!Are you struggling to make sense of statistical tests like the Chi-Square test of independence, t-tests, correlation, and Analysis of Variance (ANOVA)? Are you looking to understand how these tests can be applied in real-world situations, and how they can be used to drive critical business decisions?This comprehensive course is designed to equip you with the knowledge and skills to excel as a data analyst or scientist. You will learn how to conduct and interpret key statistical tests such as the one-sample t-test, independent sample t-test, dependent sample t-test, correlation, simple and multiple linear regression, and one-way ANOVA. You will also gain a deep understanding of key statistical concepts like homoscedasticity of variance, multicollinearity, and homogeneity of variance.With an exciting and engaging teaching style, this course will take you on a journey of discovery that will transform your understanding of statistics. You will learn through a combination of theory, practical examples, and case studies that will help you understand how statistical tests can be applied in real-world situations.By the end of this course, you will have the confidence and expertise to apply statistical tests and concepts to drive critical business decisions. You will be able to use statistical tools to gain insights into complex data sets, and you will be equipped with the skills to communicate these insights to key stakeholders.So, what are you waiting for? Sign up for “Statistics for Data Analysts and Scientists” today, and take the first step towards becoming a master of statistical analysis!

### Overview

Section 1: Introduction

Lecture 1 Welcome to the Course

Lecture 2 Introduction to Statistics

Lecture 3 Statistical Scale of Measurement

Lecture 4 Common Terms in Statistics

Section 2: Course Resources

Lecture 6 Course Resources

Lecture 7 Acknowledgement

Section 3: Understanding Inferential Statistics

Lecture 8 Intro to Inferential Statistics

Section 4: Chi Square Test of Independence

Lecture 9 Chi Square Test of Independence Overview

Lecture 10 Running the Test

Lecture 11 Interpret the Results of Chi Square Test of Independence

Section 5: One Sample t-test

Lecture 12 One Sample t-test Overview

Lecture 13 Running the Test

Lecture 14 Interpret the Results of One Sample t-test

Section 6: Independent Sample t-test

Lecture 15 Independent Sample t-test Overview

Lecture 16 Running the Test

Lecture 17 Interpret the Results of Independent Sample t-test

Section 7: Dependent (Paired) Sample t-test

Lecture 18 Dependent or Paired Sample t-test Overview

Lecture 19 Running the Test

Lecture 20 Interpret the Results of Dependent Sample t-test

Section 8: Correlation Analysis

Lecture 21 Pearson Correlation Analysis Overview

Lecture 22 Running the Test

Lecture 23 Interpret the Results of Correlation Analysis

Section 9: Simple Linear Regression Analysis

Lecture 24 Simple Linear Regression Analysis Overview

Lecture 25 Running the Test

Lecture 26 Interpret the Results of Simple Linear Regression Analysis

Section 10: Multiple Linear Regression Analysis

Lecture 27 Multiple Linear Regression Overview

Lecture 28 Running the Test

Lecture 29 Interpret the Results of Multiple Linear Regression Analysis

Section 11: One Way Analysis of Variance

Lecture 30 One Way Analysis of Variance Test Overview

Lecture 31 Running the Test

Lecture 32 Interpret the Results of One Way Analysis of Variance Test

Data analysts who want to improve their statistical skills and knowledge,Scientists who need to understand and analyze statistical data in their research,Business professionals who use data to drive decision-making and want to gain a deeper understanding of statistical concepts and tests,Students studying statistics, data science, or a related field who want to develop a strong foundation in statistical methods,Anyone who is interested in learning more about statistics and how it can be applied in practical settings

#### Course Information:

Udemy | English | 2h 30m | 1.11 GB
Created by: Olanrewaju Oyinbooke

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