Statistics

Statistics is the driving force in any quantitative career. It is the fundamental skill data scientists need to be able to understand and design statistical tests and analyses performed by modern software packages and programming languages. In this course we start from the very basics of statistics and gradually build up your statistical thinking enabling you to understand the more complex analyses carried out later in the program.

Sign up to
preview the course
for FREE!

Create a free account and start learning data science today.

create free account
Our graduates work at exciting places:
walmart
tesla
paypal
citibank
booking.com

Section 1

Introduction

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.

FREE Welcome to Statistics
FREE Population vs sample

Section 2

Descriptive Statistics Fundamentals

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 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.

FREE Types of data and 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, standard deviation, and coefficient of variation
Show all lessons
FREE Covariance
FREE Correlation
FREE Practical Example - Descriptive Statistics
Show fewer lessons

Section 3

Inferential Statistics Fundamentals

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.

Premium course icon Introduction
Premium course icon What is a distribution
Premium course icon The Normal Distribution
Premium course icon The Standard Normal Distribution
Premium course icon Central limit theorem
Premium course icon Standard error
Premium course icon Estimators and estimates

Section 4

Confidence Intervals

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.

Premium course icon Definition of confidence intervals
Premium course icon Population variance known, z-score
Premium course icon Confidence Interval Clarifications
Premium course icon Student's T Distribution
Premium course icon Population variance unknown, t-score
Premium course icon Margin of error
Premium course icon Confidence intervals. Two means. Dependent samples
Premium course icon Confidence intervals. Two means. Independent samples
Show all lessons
Premium course icon Practical Example - Confidence Intervals
Show fewer lessons

Section 5

Hypothesis testing

In this section, you will learn how to perform hypothesis testing and what is 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.

Premium course icon Null vs Alternative
Premium course icon Rejection region and significance level
Premium course icon Type I error vs type II error
Premium course icon Test for the mean. Population variance known
Premium course icon p-value
Premium course icon Test for the mean. Population variance unknown
Premium course icon Test for the mean. Dependent samples
Premium course icon Test for the mean. Independent samples
Show all lessons
Premium course icon Practical Example - Hypothesis Testing
Show fewer lessons
MODULE 1

Data Science Fundamentals

This course is part of Module 1 of the 365 Data Science Program. The complete training consists of four modules, each building up on your knowledge from the previous one. Whereas the other three modules are designed to improve upon your technical skillset, Module 1 is designed to help you create a strong foundation for your data science career. You will understand the core principles of probability, statistics, and mathematics; you will also learn how to visualize your data.

See All Modules

Trust the other 276,000 students

Ready to start?
Sign up today for FREE!

Whether you want to scale your career or transition into a new field, data science is the number one skillset employers look for. Grow your analytics expertise and get hired as a data scientist!