# Statistics

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

with Iliya Valchanov

Start course#### 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.

#### Skills you will gain

#### 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.

#### Curriculum

- Introduction Free2 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 Free13 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 Fundamentals7 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 Intervals11 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 testing11 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

#### “Statistics is the foundation of any quantitative career. This course is where you start. And it is the perfect beginning! In no time, you will acquire the fundamental skills that will enable you to understand complicated statistical analysis directly applicable to real-life situations.”

##### Iliya Valchanov

##### Co-founder at 365 Data Science

##### Statistics

with Iliya Valchanov