Learn the basics of
Probability and Statistical Corporate Course Online

Develop your team’s skills in probability and statistical analysis, as well as their understanding behind behaviour trends in this field for corporate probability and statistics.

Key Learning Objectives:
  • Apply fundamental theories, terminology and related statistical skills
  • Master SAS as a tool for statistical analysis
  • Identify and apply statistical models
  • Convey results to the intended audience
Probability & Statistical Analysis
Ideal for:
Anyone in business analysis, or in financial roles such as chartered accountant, economist, financial managers or traders, machine learning engineers or research scientists.
Course outcome:
Provide teams with the building blocks to gain a more thorough understanding of probability and stats.
Course outline:
08

Weeks

02

Modules

16

Lessons

Course accreditation:​
Upon completion of this course in probability and statistics your employee will receive an accredited certificate assessed by global academic partners, Austin Peay State University and the CPD Certification Service.

Certified by:

Globally recognised by:

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Course details

Leadership & Management

MODULE 1

Diploma in Probability & Statistical Analysis

1.Introduction to Statistical Analysis

Lesson 1 will, in part, discuss what the difference between this course and the data analysis course is and who this course is aimed at. We will understand which fundamental concepts you need to lay for a good understanding of Statistical Analysis. Thereafter, we will introduce the tool we will utilize throughout this module, called SAS Studio. We will end the lesson with a fun practical demonstration in SAS.

2.Understanding the Data

This lesson will be all about study design, data types and a sneak peak into summarizing data with the measures of central location. Understanding the study design and the type of scales that are used to measure data, is a crucial step in analysing the data accurately. Only after we know the pros and cons of the way the data was gathered, can we start describing the data.

3.Summarising Data

This lesson will mainly focus on the different methods of summarising data. The previous lesson introduced measures of central tendency to the student. Lesson 3 will elaborate on that concept with measures of spread as well as various ways of visualising data through plots in SAS Studio. Each topic will be consolidated through a practical demonstration in SAS Studio.

4.Probability Theory

This lesson will introduce the student to the concepts of probability theory. This lesson includes concepts like samples and populations. The lesson will define the basic definitions and rules of probability. This lesson will end by touching on more advanced concepts like mutually exclusive events, independent events, non independent events, and non mutually exclusive events.

5.Distributions

Lesson 5 aims to initiate the student's understanding of random variables and a various number probability distributions known and identified. Together with these concepts, this lesson will showcase the famous Central Limit Theorem (a fundamental concept to the understanding of sample sizes to be discussed in the next lesson).

6.Sample Sizes and Sampling

This lesson will answer the well known question of "how many observations does the study need in order to be statistically significant?". Many studies skip this fundamental step and end up not being able to prove statistical significance as a result. Lesson 6 will understand what it means for a result to be statistically significant and why it is so important for the sample to be large enough.

7.Hypothesis Testing

Lesson 7 will focus on questions about a single group. This lesson starts to uncover the concepts of inferential statistics; statistical methods used to draw conclusions from the sample in order to make conclusion about the population. Prior lessons focused on descriptive statistics, because they helped the student to describe and summarise the data through various methods like plots and summary statistics.

8.Hypothesis Testing Continued

This lesson will continue the concepts of hypothesis testing we introduced in lesson 7. We explore and investigate hypothesis testing concepts surrounding small and large samples. By the end of this lesson, you will be able to identify which procedure to use for a variety of different scenarios. Lesson 8 will end by tying together the practical concepts explored throughout Module 1 and offer some new tips and tricks for data management in SAS Studio not yet explored.

1.Cleaning and Merging Data

The first lesson of this module is focused on enhancing your knowledge on cleaning and merging data. Lesson 1 will take it back to more basic principles, but nonetheless some of the most important principles of an analysts' work cycle, cleaning and merging data. It is estimated that up to 60% of an analysts' time is spent on cleaning data, therefore this step in the journey is crucial to manage efficiently.

2.Managing Data

Lesson 2 will teach you how to effectively manage your data. Creating new variable and transforming variables all form apart of this step of the process.

3.Working with Dates and Times

Working with dates and times is a vital skill in your Probability and Statistical Analysis journey, this lesson is geared to help you master this skill effectively. Dates and times are concepts integrated into most data sets and working with them, might be considered an art form for some. In lesson 3, the student will learn tips and tricks to deal with dates and times in SAS Studio in order to make their handling as painless as possible.

4.Introducing Linear Regression

Halfway through module 2, our focus is shifts to modelling data, by introducing linear regression, the most well-known modelling procedure.

5.Linear Regression Continued

Understanding linear regression requires a little bit more time, in part 2 of this lesson, we delve a little deeper into this complex topic.

6.Multiple Linear Regression

Multiple regression is an extension of linear regression using multiple explanatory variables, this lesson is focused on understanding this in more detail.

7.Introducing Logistic Regression

Logistic regression usually uses a logistic function to model a binary dependent variable, we explore this concept in this lesson and identify where more complex extensions exist.

8.Logistic Regression Continued

We end off module 2 by delving into the finer details of logistic regression before moving to our more advanced concepts in module 3.