Statistics

4.8/5

(2308)

Covering descriptive and inferential statistics, as well as hypothesis testing techniques and exercises, statistics puts the “scientist” in data scientist.

44 lessons 3h

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Course Overview

Statistics is the driving force in any quantitative career. It is the fundamental skill that a data scientist needs in order to understand and design statistical tests and analysis using modern software packages and programming languages. In this course, we start from the very basics and gradually build your statistical thinking. This, in turn, enables you to understand the more complex analyses carried out later in the program.

44 High Quality Lessons

25 Practical Tasks

3 Hours of Video

Certificate of Achievement

Skills you will gain

data analysisExcelTheory

What You'll Learn

This course begins with the very basics of statistics and builds up your arithmetic thinking. It gradually teaches you how to work with more complex analyses, statistical approaches, and hypothesis.

Distinguish between the diverse types of data and levels of measurement

Understand what a distribution is

Calculate confidence intervals

Perform hypothesis testing

Become familiar with the p-value

Curriculum

Introduction Free

2 Lessons 7 Min

In this part of the course, we will discuss why you need to learn statistics, and which are the key skills you will acquire by taking the course.
What does the course cover Free Population vs sample Free
Descriptive Statistics Fundamentals Free

13 Lessons 64 Min

Here, you will learn how to distinguish between the different types of data and levels of measurement. This will help you when calculating the measures of central tendency (mean, median, and mode) and dispersion indicators such as variance and standard deviation, as well as measures of the relationship between variables like covariance and correlation. To reinforce what you have learned, we will wrap up this section with a hands-on practical example.
Types of data and levels of measurement Free Levels of measurement Free Categorical Variables. Visualization techniques Free Numerical Variables. Frequency distribution table Free The histogram Free Cross table and scatter plot Free Mean, median, mode Free Skewness Free Variance Free Standard deviation and coefficient of variation Free Covariance Free Correlation Free Practical Example - Descriptive Statistics Free
Inferential Statistics Fundamentals

7 Lessons 22 Min

In this section, you will learn what a distribution is and what characterizes the normal distribution. We will introduce you to the central limit theorem and to the concept of standard error.
Introduction What is a distribution The Normal Distribution The Standard Normal Distribution Central limit theorem Standard error Estimators and estimates
Confidence Intervals

11 Lessons 55 Min

Here, you will learn how to calculate confidence intervals with known population and variance. We will introduce the Student T distribution, and you will learn how to work with smaller samples, as well as differences between two means (with dependent and independent samples). These tools are fundamental later on when we start applying each of these concepts to large datasets and use coding languages like Python and R. To reinforce what you have learned, we will wrap up this section with an easy-to-understand practical example.
Definition of confidence intervals Population variance known, z-score Confidence Interval Clarifications Student's T Distribution Population variance unknown, t-score Margin of error Confidence intervals. Two means. Dependent samples Confidence intervals. Two means. Independent samples (Part1) Confidence intervals. Two means. Independent samples (Part2) Confidence intervals. Two means. Independent Samples (Part 3) Practical Example - Confidence Intervals
Hypothesis testing

11 Lessons 53 Min

In this section, you will learn how to perform hypothesis testing, as well as the difference between a null and alternative hypothesis. We will discuss rejection and significance levels, and type I and type II errors. The lessons will teach you how to test for the mean when the population variance is known and unknown, as well as how to test for the mean when you are dealing with dependent and independent samples. You will also become familiar with the p-value. To consolidate the new knowledge, we will conclude with a practical example.
Null vs Alternative Further Reading on Null and Alternative Hypothesis Rejection region and significance level Type I error vs type II error Test for the mean. Population variance known p-value 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

Student feedback

4.8/5

2308 ratings

5 stars

1952 (85%)

4 stars

278 (12%)

3 stars

60 (3%)

2 stars

12 (1%)

1 star

6 (0%)

Eric Mbogu

07.07.2022

Overall, a good course. Given I didn't know what to expect, and a complete newbie to this, I felt this would be a good investment. While in some respects it has been, I did find it wasn't entirely beginner friendly. Some concepts were not properly explained, while some were glossed over(i.e null hypothesis). Given what I've had to learn in other places, I just felt it wasn't done well here. Sometimes it made me question myself when in fact it wasn't entirely my fault. I was very dissatisfied with its delivery as I could tell the author of the course wasn't thinking about his/her audience. If the author indeed was then the course clearly isn't for me; I am not confident recommending it to friends as their money will be best spent elsewhere as total newbies. It has led me to approach other courses in the career track with a bit of apprehension, thus forcing me to supplement them with other resources elsewhere on the internet. I hope this gets revised in the future as there is indeed some potential. The graphics really do help visually but depth, clarity and simplicity in content delivery is just as important. Thanks.

Frank Peluso

08.01.2023

The material covers the basics without getting too deep into the math behind the concepts but still enough to give you a good foundation for seeking out more detailed explanations on your own. Everything is clearly explained and easy to understand, and the practical exercises reinforce what is covered and keep you constantly engaged.

Sahar ElMukashfi

18.11.2022

Many thanks for the instructor who make Statistics a joyful science. A very-well structured course that introduce concepts very smoothly with variety real-world examples & practice for concreate understanding in addition to and many exercises with the solution to check your answer, I had massively enjoyed the course so Thanks again.

Irakli Skhirtladze

10.11.2022

Descriptive statistics was easy to understand as I had the topic somewhat learned in high school. However, later topics were harder and I wish they were explained in an easier language and with better examples. Overall, I liked the course, but I feel it can be more beginner friendly.

Mayank Mittal

05.01.2023

Nice explanations and each lesson has its own exercise to get more familiar with formula Statistics has soo many formulas which do create confusion and hard to remember but Instructor tries to simplify and overall a Fabulous course (expecting same Statistics course in Python too )