Statistics for Data Science and Business Analysis

Statistics for Data Science and Business Analysis

by 365 Careers Ltd.
Released August 2021
Publisher(s): Packt Publishing
ISBN: 9781789803259

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Video description

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

In Detail

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

Comprehensive

Practical

To the point

Packed with plenty of exercises and resources

Data-driven

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.

Audience

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.

Publisher resources

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Table of contents

  1. Chapter 1 : Introduction to the Course
    1. What Does the Course Cover?
    2. Understanding the Difference Between a Population and a Sample
  2. Chapter 2 : Descriptive Statistics Fundamentals
    1. The Various Types of Data We can Work With
    2. Levels of Measurement
    3. Categorical Variables and Visualization Techniques for Categorical Variables
    4. Numerical Variables and Using a Frequency Distribution Table
    5. Histogram Charts
    6. Cross Tables and Scatter Plots
    7. The Main Measures of Central Tendency: Mean, Median, Mode
    8. Measuring Skewness
    9. Measuring How Data is Spread Out: Calculating Variance
    10. Standard Deviation and Coefficient of Variation
    11. Calculating and Understanding Covariance
    12. The Correlation Coefficient
    13. Practical Example
  3. Chapter 3 : Inferential Statistics Fundamentals
    1. Introduction to Inferential Statistics
    2. What is a Distribution?
    3. The Normal Distribution
    4. The Standard Normal Distribution
    5. Understanding the central limit theorem
    6. Standard Error
    7. Working with Estimators and Estimates
  4. Chapter 4 : Confidence Intervals
    1. Confidence Intervals - an Invaluable Tool for Decision Making
    2. Calculating Confidence Intervals Within a Population with a Known Variance
    3. Confidence Interval Clarifications
    4. Student's T Distribution
    5. Calculating Confidence Intervals Within a Population with an Unknown Variance
    6. What is a Margin of Error and Why is it Important in Statistics?
    7. Calculating Confidence Intervals for Two Means with Dependent Samples
    8. Calculating Confidence Intervals for Two Means with Independent Samples (Part 1)
    9. Calculating Confidence Intervals for Two Means with Independent Samples (Part 2)
    10. Calculating Confidence Intervals for Two Means with Independent Samples (Part 3)
    11. Practical Example: Inferential Statistics
  5. Chapter 5 : Hypothesis Testing
    1. The Null and the Alternative Hypothesis
    2. Establishing a Rejection Region and a Significance Level
    3. Type I Error Versus Type II Error
    4. Test for the Mean; Population Variance Known
    5. What is P-Value and Why is it One of the Most Useful Tools for Statisticians?
    6. Test for the Mean; Population Variance Unknown
    7. Test for the Mean; Dependent Samples
    8. Test for the Mean; Independent Samples (Part 1)
    9. Test for the Mean; Independent Samples (Part 2)
    10. Practical Example: Hypothesis Testing
  6. Chapter 6 : The Fundamentals of Regression Analysis
    1. Introduction to Regression Analysis
    2. Correlation and Causation
    3. The Linear Regression Model Made Easy
    4. What is the Difference Between Correlation and Regression?
    5. A Geometrical Representation of the Linear Regression Model
    6. A Practical Example - Reinforced Learning
  7. Chapter 7 : Subtleties of Regression Analysis
    1. Decomposing the Linear Regression Model - Understanding its Nuts and Bolts
    2. What is R-Squared and How Does it Help Us?
    3. The Ordinary Least Squares Setting and its Practical Applications
    4. Studying Regression Tables
    5. The Multiple Linear Regression Model
    6. Adjusted R-Squared
    7. What Does the F-Statistic Show Us and Why Do We Need to Understand It?
  8. Chapter 8 : Assumptions for Linear Regression Analysis
    1. OLS Assumptions
    2. A1. Linearity
    3. A2. No Endogeneity
    4. A3. Normality and Homoscedasticity
    5. A4. No Autocorrelation
    6. A5. No Multicollinearity
  9. Chapter 9 : Dealing with Categorical Data
    1. Dummy Variables
  10. Chapter 10 : Practical Example: Regression Analysis
    1. Practical Example: Regression Analysis

Product information

  • Title: Statistics for Data Science and Business Analysis
  • Author(s): 365 Careers Ltd.
  • Release date: August 2021
  • Publisher(s): Packt Publishing
  • ISBN: 9781789803259