Learn Statistical Data Analysis with Python

Learn Statistical Data Analysis with Python, available at Free, has an average rating of 4.15, with 17 lectures, based on 77 reviews, and has 4523 subscribers.

You will learn about I can explain and calculate the importance of measures of central tendency. I can explain and calculate the importance of measures of dispersion. I can identify the relative strengths and weaknesses of the measures of tendency. I can identify the relative strengths and weaknesses of the measures of dispersion. I can create and interpret a histogram, a bar chart, a box plot, and a frequency table. I can identify and describe scatter plots and line graphs to determine the relationships between two variables. I can calculate and interpret the Pearson correlation coefficient to determine the relationships between two variables. This course is ideal for individuals who are This course is designed for professionals with an interest in getting hands-on experience with the respective data science techniques and tools. It is particularly useful for This course is designed for professionals with an interest in getting hands-on experience with the respective data science techniques and tools.

Enroll now: Learn Statistical Data Analysis with Python

Summary

Title: Learn Statistical Data Analysis with Python

Price: Free

Average Rating: 4.15

Number of Lectures: 17

Number of Published Lectures: 17

Number of Curriculum Items: 17

Number of Published Curriculum Objects: 17

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

I can explain and calculate the importance of measures of central tendency.
I can explain and calculate the importance of measures of dispersion.
I can identify the relative strengths and weaknesses of the measures of tendency.
I can identify the relative strengths and weaknesses of the measures of dispersion.
I can create and interpret a histogram, a bar chart, a box plot, and a frequency table.
I can identify and describe scatter plots and line graphs to determine the relationships between two variables.
I can calculate and interpret the Pearson correlation coefficient to determine the relationships between two variables.

Who Should Attend

This course is designed for professionals with an interest in getting hands-on experience with the respective data science techniques and tools.

Target Audiences

This course is designed for professionals with an interest in getting hands-on experience with the respective data science techniques and tools.

By the end of this course, you will have achieved the following learning outcomes:

I can explain and calculate the importance of measures of central tendency.
I can explain and calculate the importance of measures of dispersion.
I can identify the relative strengths and weaknesses of the measures of tendency.
I can identify the relative strengths and weaknesses of the measures of dispersion.
I can create and interpret a histogram, a bar chart, a box plot, and a frequency table.
I can identify and describe scatter plots and line graphs to determine the relationships between two variables.
I can calculate and interpret the Pearson correlation coefficient to determine the relationships between two variables.

These are some of the basics statistical data analysis techniques that you will get to use while working on data science projects. For example, in order to check for model assumptions while working on a predictive solution, you will need to apply the above techniques i.e. to test for normality of variables in a dataset, you can plot a histogram or a pair plot, to check for correlation, you can calculate the Pearson correlation coefficient etc.

In addition, these techniques will also be important while also working on data analysis projects where the creation of a descriptive analysis report will be a necessity.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: About the Resources

Lecture 1: About the Resources

Chapter 3: Statistical Data Analysis: Overview

Lecture 1: Statistical Data Analysis: Overview

Chapter 4: Overview: Univariate Analysis

Lecture 1: Overview: Univariate Analysis

Chapter 5: Bar Chart

Lecture 1: Bar Chart

Chapter 6: Histogram

Lecture 1: Histogram

Chapter 7: Frequency Table

Lecture 1: Frequency Table

Chapter 8: Pie Chart

Lecture 1: Pie Chart

Chapter 9: Box Plot

Lecture 1: Box Plot

Chapter 10: Measures of Central Tendency

Lecture 1: Measures of Central Tendency

Chapter 11: Measures of Dispersion

Lecture 1: Measures of Dispersion

Chapter 12: Overview: Bivariate Analysis

Lecture 1: Overview: Bivariate Analysis

Chapter 13: Scatterplot

Lecture 1: Scatterplot

Chapter 14: Pearson Correlation Coefficient

Lecture 1: Pearson Correlation Coefficient

Chapter 15: Pearson Correlation Coefficient – Visualisation

Lecture 1: Pearson Correlation Coefficient – Visualisation

Chapter 16: Line Graphs

Lecture 1: Line Graphs

Chapter 17: What's Next

Lecture 1: What's Next

Instructors

Valentine Mwangi
Data Science Curriculum Designer

Rating Distribution

1 stars: 5 votes
2 stars: 3 votes
3 stars: 13 votes
4 stars: 25 votes
5 stars: 31 votes

Frequently Asked Questions

How long do I have access to the course materials?

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

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Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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