Statistics Fundamentals

Statistics Fundamentals, available at $69.99, has an average rating of 4.6, with 213 lectures, 29 quizzes, based on 96 reviews, and has 12122 subscribers.

You will learn about Basic theories and Python coding for statistical analysis This course is ideal for individuals who are Anyone who wants to start studying statistics or Anyone who wants to brush up statistics It is particularly useful for Anyone who wants to start studying statistics or Anyone who wants to brush up statistics.

Enroll now: Statistics Fundamentals

Summary

Title: Statistics Fundamentals

Price: $69.99

Average Rating: 4.6

Number of Lectures: 213

Number of Quizzes: 29

Number of Published Lectures: 213

Number of Published Quizzes: 29

Number of Curriculum Items: 242

Number of Published Curriculum Objects: 242

Original Price: $174.99

Quality Status: approved

Status: Live

What You Will Learn

Basic theories and Python coding for statistical analysis

Who Should Attend

Anyone who wants to start studying statistics

Anyone who wants to brush up statistics

Target Audiences

Anyone who wants to start studying statistics

Anyone who wants to brush up statistics

Welcome to Statistics Fundamentals! This course is for beginners who are interested in statistical analysis. And anyone who is not a beginner but wants to go over from the basics is also welcome!

As a science field, statistics is a discipline that concerns collecting data, and mathematical analysis of the collected data, describing data and making inference from the data. Using statistical methods, we can obtain insights from data, and use the insights for answering various questions and decision making.

Statistical Analysis is now applied in various scientific and practical fields. It is essential in both natural science and social science. In business practice, statistical analysis is applied as business analytics such as human resource analytics and marketing analytics. And now, it is an essential tool in medical practice and government policymaking. Besides, baseball teams utilize it for strategy formation. It is well known a SABRmetrics.

However, if we do not use appropriate methods, statistical analysis will result in meaningless or misleading findings. To obtain meaningful insights from data, we need to learn statistics both in practical and theoretical viewpoints. This course intends to provide you with theoretical knowledge as well as Python coding. Theoretical knowledge enables us to implement appropriate analysis in various situations. And it can be a useful foundation for more advanced learning.

This course is a comprehensive program for learning the basics of statistics. It consists of the 9 sections. They cover theory and basic Python coding. Even if you do not have Python coding experience, I believe they are easy to follow for you. But this program is not a Python course, so how to install Python and construct environment is not covered in this course.

This course is designed for beginners, but by learning with this course, you will reach an intermediate level of expertise in statistics. Specifically, this course covers undergraduate level statistics. After enrollment, you can download the lecture presentations, Python code files, and toy datasets in the first lecture page.

I’m looking forward to seeing you in this course!

*In some videos, the lecturer says “ will be covered in later courses“, but it should be “later sections.”

Table of Contents

1. Introduction

2. Descriptive Statistics:

3. Probability

4. Probability Distribution

5. Sampling

6. Estimation

7. Hypothesis Testing

8. Correlation & Regression

9. ANOVA

Course Curriculum

Chapter 1: Introduction

Lecture 1: Lets Get Started with Python!

Lecture 2: 1-1 What is Statistics?

Lecture 3: 1-2 Types of Statistics

Lecture 4: 1-3 What is Data?

Lecture 5: 1-4 Stevens’ Typology

Lecture 6: 1-5 How to Distinguish?

Lecture 7: 1-6 Independent & Dependent Variables

Chapter 2: Descriptive Statistics

Lecture 1: 2-0 Introduction

Lecture 2: 2-1 Display Data 1: Frequency Table

Lecture 3: 2-2 Display Data 2: Create Frequency Table with Python

Lecture 4: 2-3 Display Data 3: Stem and Leaf Diagram

Lecture 5: 2-4 Display Data 4: Stem and Leaf Diagram with Python

Lecture 6: 2-5 Display Data 5: Histogram

Lecture 7: 2-6 Display Data 6: Create Histograms with Python

Lecture 8: 2-7 Display Data 7: Dot Plot

Lecture 9: 2-8 Central Tendency 1: Mean

Lecture 10: 2-9 Central Tendency 2: Median

Lecture 11: 2-10 Central Tendency 3: Mode

Lecture 12: 2-11 Central Tendency 4: Mean Median & Mode with Python

Lecture 13: 2-12 Central Tendency 5: Geometric Mean

Lecture 14: 2-13 Central Tendency 6: Harmonic Mean

Lecture 15: 2-14 Central Tendency 7: Trimmed Mean

Lecture 16: 2-15 Central Tendency 8: Moving Average

Lecture 17: 2-16 Central Tendency 9: Expected Value

Lecture 18: 2-17 Central Tendency 10: Proportions for Binary Data

Lecture 19: 2-18 Central Tendency 11: Various Means with Python

Lecture 20: 2-19 Variability 1: What is Variability?

Lecture 21: 2-20 Variability 2: Range and Residual

Lecture 22: 2-21 Variability 3: Mean Absolute Deviation

Lecture 23: 2-22 Variability 4: Variance

Lecture 24: 2-23 Variability 5: Standard Deviation

Lecture 25: 2-24 Variability 6: Coefficient of Variation

Lecture 26: 2-25 Variability 7: Variability with Python

Lecture 27: 2-26 Relative Position 1: Percentile

Lecture 28: 2-27 Relative Position 2: Interquartile Range

Lecture 29: 2-28 Relative Position 3: The Empirical Rule

Lecture 30: 2-29 Relative Position 4: Chebyshevs Theorem

Lecture 31: 2-30 Relative Position 5: Relative Position with Python

Lecture 32: 2-31 Data Visualization 1: Why Visualization?

Lecture 33: 2-32 Data Visualization 2: Box Plot

Lecture 34: 2-33 Data Visualization 3: Box Plot with Python

Lecture 35: 2-34 Data Visualization 4: Bar Chart

Lecture 36: 2-35 Data Visualization 5: Bar Plot with Python

Lecture 37: 2-36 Data Visualization 6: Pie Chart

Lecture 38: 2-37 Data Visualization 7: Pie Chart with Python

Lecture 39: 2-38 Data Visualization 8: Line Plot

Lecture 40: 2-39 Data Visualization 9: Line Plot with Python

Lecture 41: 2-40 Data Visualization 10: Cross Tabulation Table

Lecture 42: 2-41 Data Visualization 11: Stacked Bar Chart

Lecture 43: 2-42 Data Visualization 12: Crosstab and Stacked Bar Chart with Python

Lecture 44: 2-43 Data Visualization 13: Mosaic Plot with Python

Lecture 45: 2-44 Data Visualization 14: Ternary Plot

Lecture 46: 2-45 Data Visualization 15 Ternary Plot with Python

Chapter 3: Probability

Lecture 1: 3-0 Introduction

Lecture 2: 3-1 Permutation & Combination 1: Factorial

Lecture 3: 3-2 Permutation & Combination 2: Permutation

Lecture 4: 3-3 Permutation & Combination 3: Combination

Lecture 5: 3-4 Permutation & Combination 4: Permutation and Combination with Python

Lecture 6: 3-5 Set Theory 1: Experiment & Event

Lecture 7: 3-6 Set Theory 2: Set

Lecture 8: 3-7 Set Theory 3: Event & Element

Lecture 9: 3-8 Set Theory 4: Venn Diagram

Lecture 10: 3-9 Set Theory 5: Complementary Event

Lecture 11: 3-10 Set Theory 6: Intersection

Lecture 12: 3-11 Set Theory 7: Union

Lecture 13: 3-12 Set Theory 8: Set Difference

Lecture 14: 3-13 Set Theory 9: Set in Python

Lecture 15: 3-14 Probability Theory 1: What is Probability?

Lecture 16: 3-15 Probability Theory 2: Calculate Probability

Lecture 17: 3-16 Probability Theory 3: Combination & Probability

Lecture 18: 3-17 Probability Theory 4: Statistical Independence

Lecture 19: 3-18 Probability Theory 5: Expected Value

Lecture 20: 3-19 Conditional Probability 1: What is Conditional Probability?

Lecture 21: 3-20 Conditional Probability 2: Statistical Independence

Lecture 22: 3-21 Conditional Probability 3: Multiplication Theorem

Lecture 23: 3-22 Conditional Probability 4: Simpsons Paradox

Lecture 24: 3-23 Conditional Probability 5: Conditional Probability with Python

Lecture 25: 3-24 Conditional Probability 6: Bayes Theorem

Lecture 26: 3-25 Conditional Probability 7: Bayes Theorem with Python

Chapter 4: Probability Distribution

Lecture 1: 4-0 Introduction

Lecture 2: 4-1 Random Variable

Lecture 3: 4-2 Discrete Probability Distribution

Lecture 4: 4-3 Continuous Probability Distribution

Lecture 5: 4-4 Probability Density Function

Lecture 6: 4-5 Cumulative Distribution Function

Lecture 7: 4-6 Expected Value of Random Variables

Lecture 8: 4-7 Variance of Random Variables

Lecture 9: 4-8 Find Variance from Expected Value

Instructors

Takuma Kimura
Scientist of Organizational Behavior & Business Analytics

Rating Distribution

1 stars: 1 votes

2 stars: 4 votes

3 stars: 9 votes

4 stars: 35 votes

5 stars: 47 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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