Description

Is statistics a driving force in the industry you want to enter? Do you want to work as a marketing analyst, a business intelligence analyst, a data analyst, or a data scientist? Well then, you've come to the right place!

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. The code files and all related files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Statistics-for-Data-Science-and-Business-Analysis-V-

What You Will Learn

Understand the fundamentals of statistics
Work and plot with different types of data
Calculate the measures of central tendency, asymmetry, and variability
Calculate correlation and covariance
Perform hypothesis testing and make data-driven decisions
Carry out regression analysis along with using dummy variables

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.

Approach

A complete course (packed with extensive case studies, complete training, superb course materials, quiz questions, handouts, course notes, an HD video, and animations) that covers major statistical topics and helps you become a marketing analyst, a business intelligence analyst, a data analyst, or a data scientist.

Key Features

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

Github Repo

https://github.com/PacktPublishing/Statistics-for-Data-Science-and-Business-Analysis-V-

About the Author

365 Careers Ltd.

365 Careers' courses have been taken by more than 203,000 students in 204 countries. People working at world-class firms such as Apple, PayPal, and Citibank have completed 365 Careers trainings. By choosing 365 Careers, you make sure you will learn from proven experts who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time. If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers' courses are the perfect place to start.

Course Outline

1. Introduction to the Course

1. What Does the Course Cover?

This video describes about what the course is about and other details.

2. Understanding the Difference Between a Population and a Sample

This video helps in understanding the difference between a population and a sample. Furthermore, you will know the difference between a random sample and a representative sample.

2. Descriptive Statistics Fundamentals

1. The Various Types of Data We can Work With

This video explains about the various types of data to work with.

2. Levels of Measurement

In this video, the other classification of variables is shown - levels of measurement.

3. Categorical Variables and Visualization Techniques for Categorical Variables

This video looks into techniques for visualizing categorical variables; namely frequency distribution tables, bar charts, pie charts, and Pareto diagrams.

4. Numerical Variables and Using a Frequency Distribution Table

In this video, the frequency distribution table is explained.

5. Histogram Charts

Learn how to illustrate data with histograms.

6. Cross Tables and Scatter Plots

In this video, you will explore the different ways to demonstrate relationship between variables.

7. The Main Measures of Central Tendency: Mean, Median, Mode

This video will introduce you to the three measures of central tendency - mean, median, and mode.

8. Measuring Skewness

This video explains the most commonly used tool to measure asymmetry - skewness, and its relationship with the mean, median, and mode.

9. Measuring How Data is Spread Out: Calculating Variance

In this video, you will learn how to calculate variance.

10. Standard Deviation and Coefficient of Variation

This video explains standard deviation and coefficient of variation.

11. Calculating and Understanding Covariance

This video continues with the most common measure of interconnection between variables: covariance.

12. The Correlation Coefficient

This video explains correlation coefficient - the quantitative representation of correlation between variables.

13. Practical Example

This video contains a practical example on descriptive statistics.

3. Inferential Statistics Fundamentals

1. Introduction to Inferential Statistics

This video provides an introduction to inferential statistics.

2. What is a Distribution?

This video explains what a distribution is, what types of distributions are there, and how this helps us better understand statistics.

3. The Normal Distribution

This video introduces the normal distribution and its great importance to statistics as a field.

4. The Standard Normal Distribution

This video explains the standard normal distribution by deriving it from the normal distribution through the method of standardization. You will also be elaborated on its use for testing.

5. Understanding the central limit theorem

This video is about the central limit theorem - one of the most important statistical concepts.

6. Standard Error

This video introduces the standard error - an important ingredient for making predictions.

7. Working with Estimators and Estimates

This video explores the estimators and estimates, and differentiates between the two concepts.

4. Confidence Intervals

1. Confidence Intervals - an Invaluable Tool for Decision Making

This video explains confidence intervals in detail.

2. Calculating Confidence Intervals Within a Population with a Known Variance

In this video, you will see the first example of the use of confidence intervals and introduce the concept of the z-score.

3. Confidence Interval Clarifications

This video deep dives into the explanation of confidence intervals.

4. Student's T Distribution

This video is about the inception of the student's T distribution - a valuable tool when working with small samples.

5. Calculating Confidence Intervals Within a Population with an Unknown Variance

This video is about calculating confidence intervals within a population with an unknown variance.

6. What is a Margin of Error and Why is it Important in Statistics?

This video explains about margin of error and its importance in statistics.

7. Calculating Confidence Intervals for Two Means with Dependent Samples

This video shows real life examples of confidence intervals on dependent samples.

8. Calculating Confidence Intervals for Two Means with Independent Samples (Part 1)

This is the first of the three-part video, where you'll calculate confidence intervals for two means with independent samples.

9. Calculating Confidence Intervals for Two Means with Independent Samples (Part 2)

This is the second of the three-part video, where you'll calculate confidence intervals for two means with independent samples.

10. Calculating Confidence Intervals for Two Means with Independent Samples (Part 3)

This is the third of the three-part video, where you'll calculate confidence intervals for two means with independent samples.

11. Practical Example: Inferential Statistics

This video contains a practical example on inferential statistics.

5. Hypothesis Testing

1. The Null and the Alternative Hypothesis

This video explains null and alternative hypotheses. You will be shown different examples and explained how to form hypotheses that are later to be tested.

2. Establishing a Rejection Region and a Significance Level

This video explains the rationale behind testing. You will establish a rejection region and a significance level.

3. Type I Error Versus Type II Error

In this video, you'll learn about Type I error versus Type II error.

4. Test for the Mean; Population Variance Known

This video is about test for the mean when population variance is known.

5. What is P-Value and Why is it One of the Most Useful Tools for Statisticians?

In this video, you'll learn about p-value and understand why it is one of the most useful tools for statisticians.

6. Test for the Mean; Population Variance Unknown

In this video, using the new p-value notion, some t-tests are performed.

7. Test for the Mean; Dependent Samples

In this video, the mean for two dependent samples are tested.

8. Test for the Mean; Independent Samples (Part 1)

This is the first of the two-part video, where the means for two dependent samples are tested.

9. Test for the Mean; Independent Samples (Part 2)

This is the second of the two-part video, where the means for two dependent samples are tested.

10. Practical Example: Hypothesis Testing

This video is a practical example on hypothesis testing.

6. The Fundamentals of Regression Analysis

1. Introduction to Regression Analysis

In this video, you will learn the theoretical foundations of simple regression analysis.

2. Correlation and Causation

In this video, you will learn about correlation and causation.

3. The Linear Regression Model Made Easy

In this video, you will learn about the linear regression model.

4. What is the Difference Between Correlation and Regression?

In this video, you will learn about correlation and regression.

5. A Geometrical Representation of the Linear Regression Model

In this video, the geometrical representation of linear regression model is shown.

6. A Practical Example - Reinforced Learning

In this video, you will learn a practical example of reinforced learning.

7. Subtleties of Regression Analysis

1. Decomposing the Linear Regression Model - Understanding its Nuts and Bolts

This video explains about decomposing the linear regression model - understanding its nuts and bolts.

2. What is R-Squared and How Does it Help Us?

In this video, you will learn about R-squared and its features.

3. The Ordinary Least Squares Setting and its Practical Applications

In this video, you will learn about ordinary least squares setting and its practical applications.

4. Studying Regression Tables

In this video, you'll learn about regression tables.

5. The Multiple Linear Regression Model

In this video, you will learn about multiple linear regression models.

6. Adjusted R-Squared

Learn about adjusted R-squared.

7. What Does the F-Statistic Show Us and Why Do We Need to Understand It?

Learn about F-statistic in detail.

8. Assumptions for Linear Regression Analysis

1. OLS Assumptions

This video explains OLS assumptions in detail.

2. A1. Linearity

In this video, the first assumption of OLS, linearity, is explained.

3. A2. No Endogeneity

In this video, the second assumption of OLS, no endogeneity, is explained.

4. A3. Normality and Homoscedasticity

In this video, the third assumption of OLS, normality and homoscedasticity, are explained.

5. A4. No Autocorrelation

In this video, the fourth assumption of OLS, no autocorrelation, is explained.

6. A5. No Multicollinearity

This video is about the final assumption-no multicollinearity.

9. Dealing with Categorical Data

1. Dummy Variables

In this video, you'll learn about dummy variables.

10. Practical Example: Regression Analysis

1. Practical Example: Regression Analysis

This video shows a practical example of regression analysis.

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Statistics for Data Science and Business Analysis

By Packt

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