# 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.

##### Our graduates work at exciting places:     ## 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

## 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 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.

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
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FREE Covariance
FREE Correlation
FREE Practical Example - Descriptive Statistics
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## 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. Introduction What is a distribution The Normal Distribution The Standard Normal Distribution Central limit theorem Standard error Estimators and estimates

## 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. 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
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## Hypothesis testing

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 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
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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 upon your knowledge from the previous one. Whereas the other three modules are designed to improve upon your technical skill set, 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.

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