Learn the statistics you need in the office: descriptive and inferential statistics, hypothesis testing, and regression analysis.
About This Video
- Learn and understand the fundamentals of statistics for data science and business analysis
- Practical tutorial with extensive case studies that will help to reinforce the learned concepts
- High-quality production – HD video and animations along with a knowledgeable instructor
This course will teach you fundamental skills that will enable you to understand complicated statistical analysis directly applicable to real-life situations. Modern software packages and programming languages are now automating most of these activities, but this course gives you something more valuable—critical thinking abilities. This course will help you understand the fundamentals of statistics, learn how to work with different types of data, calculate correlation and covariance, and more.
Careers in the field of data science are some of the most popular in the corporate world today. And, given that most businesses are starting to realize the advantages of working with the data at their disposal, this trend will only continue to grow.
The course has been designed as follows:
Easy to understand
To the point
Packed with plenty of exercises and resources
Introduces you to the statistical scientific lingo
Teaches you about data visualization
Shows you the main pillars of quant research
By the end of this course, you will acquire the fundamental skills that will enable you to understand complicated statistical analysis directly applicable to real-life situations. With this course, you will develop a habit of critical thinking that will take you miles ahead in your career.
Who this book is for
This course targets anyone who wants a career in data science or business intelligence; individuals who are passionate about numbers and quant analysis; anyone who wants to learn the subtleties of statistics and how it is used in the business world; people who want to learn the fundamentals of statistics; business analysts; and business executives.
Absolutely no prior experience is required for this course. We will start from the basics and gradually build up your knowledge. Everything is in the course.
Table of contents
- Chapter 1 : Introduction to the Course
Chapter 2 : Descriptive Statistics Fundamentals
- The Various Types of Data We can Work With
- Levels of Measurement
- Categorical Variables and Visualization Techniques for Categorical Variables
- Numerical Variables and Using a Frequency Distribution Table
- Histogram Charts
- Cross Tables and Scatter Plots
- The Main Measures of Central Tendency: Mean, Median, Mode
- Measuring Skewness
- Measuring How Data is Spread Out: Calculating Variance
- Standard Deviation and Coefficient of Variation
- Calculating and Understanding Covariance
- The Correlation Coefficient
- Practical Example
- Chapter 3 : Inferential Statistics Fundamentals
Chapter 4 : Confidence Intervals
- Confidence Intervals - an Invaluable Tool for Decision Making
- Calculating Confidence Intervals Within a Population with a Known Variance
- Confidence Interval Clarifications
- Student's T Distribution
- Calculating Confidence Intervals Within a Population with an Unknown Variance
- What is a Margin of Error and Why is it Important in Statistics?
- Calculating Confidence Intervals for Two Means with Dependent Samples
- Calculating Confidence Intervals for Two Means with Independent Samples (Part 1)
- Calculating Confidence Intervals for Two Means with Independent Samples (Part 2)
- Calculating Confidence Intervals for Two Means with Independent Samples (Part 3)
- Practical Example: Inferential Statistics
Chapter 5 : Hypothesis Testing
- The Null and the Alternative Hypothesis
- Establishing a Rejection Region and a Significance Level
- Type I Error Versus Type II Error
- Test for the Mean; Population Variance Known
- What is P-Value and Why is it One of the Most Useful Tools for Statisticians?
- Test for the Mean; Population Variance Unknown
- Test for the Mean; Dependent Samples
- Test for the Mean; Independent Samples (Part 1)
- Test for the Mean; Independent Samples (Part 2)
- Practical Example: Hypothesis Testing
- Chapter 6 : The Fundamentals of Regression Analysis
Chapter 7 : Subtleties of Regression Analysis
- Decomposing the Linear Regression Model - Understanding its Nuts and Bolts
- What is R-Squared and How Does it Help Us?
- The Ordinary Least Squares Setting and its Practical Applications
- Studying Regression Tables
- The Multiple Linear Regression Model
- Adjusted R-Squared
- What Does the F-Statistic Show Us and Why Do We Need to Understand It?
- Chapter 8 : Assumptions for Linear Regression Analysis
- Chapter 9 : Dealing with Categorical Data
- Chapter 10 : Practical Example: Regression Analysis
- Title: Statistics for Data Science and Business Analysis
- Release date: August 2021
- Publisher(s): Packt Publishing
- ISBN: 9781789803259
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