This session will introduce you to the module organization, structure and assessments.

In this session, you will be introduced to the concepts of population and sample as terms that refer to the ideal case of infinite data available and to the concrete data you are dealing with. Moreover, you will learn how variables of your data can be classified.

In this session, you will be introduced to two important statistics measures of a variable: the central tendency and the variability. You will learn how to calculate the mean, median and mode as measures of central tendency as well as to calculate the range, variance and standard deviation to get the variability of a given variable.

In this session, you will be introduced to the concepts of absolute, relative and cumulative frequency of a variable to understand how often do observed values of a variable appear in your data. Moreover, you will learn different ways of representing frequencies in statistics.

In this session, you will get a better understanding of your data using different ways of representing your data in a graph. In this sense, you will be introduced to bar and line charts, histograms or box plots among other existing types of graphs.

In this session, you will be introduced to the statistical concept of probability. In statistical terms, you will learn how to measure the probability of having a concrete fact of interest.

In this session, you will be introduced to permutations and combinations of objects chosen from a sample space, since a preliminary knowledge of combinatorics is necessary for a good command of statistics.

In this session, you will be introduced to the statistical definition of conditional probability. Commonly, an event will happen linked to another event thus requiring to re-define the concept of probability in such a way that these two events are considered.

In this session, you will learn different distributions from which a variable can be drawn according to statistics. In particular, you will learn the main features of the well-known normal distribution (bell shape). Moreover, you will learn the concept of z-scores as well as how to calculate it given a normal distribution of a variable.

In this session, you will be introduced to the concept of hypothesis testing. There exists many statistical test to test different hypothesis from which you will learn how to perform a t-statistic to test whether the mean of two given distributions are equal or not. Moreover, you will learn how to extend this statistical test to perform a paired samples t-test.

In this session, you will receive a basic introduction to two well-known statistical tests used to test hypothesis related to the variance of two given distributions: ANOVA and F-test.

In this week's sessions, we will review the principles of hypothesis testing and statistical significance. We will also review tests of mean differences including the independent samples t-test. In addition to this we will cover more advanced techniques, including the One Way Analysis of Variance (ANOVA) and the Chi Square test.

In this session, you will learn to study the relationship between two variables in your data. You will be introduced to the Pearson correlation coefficient to measure how two variables correlate between each other. Moreover, you will learn the concept of prediction where a dependent variable wants to be predicted from a set of independent variables. You will be introduced to the simplest statistical predictive model: linear regression.

In this session, you will learn how to summarize results of any case study base on statistical concepts learned so far. Moreover, you will be prepared on how to report statistical findings of your research study.