Learning Outcomes

Introduction to Datasets

Measuring Centre of a Dataset

Comparing Univariate Data in a Dataset

Introduction to Multivariate Data

Transformations to Achieve Linearity

Lesson Summary

To start, this module will introduce you to data exploration. You will learn about the important metrics used in measuring the centre of a data set. You will also be introduced to how transformations are used as a technique for linear and non-linear models.

Methods of Data Collection

Planning and Conducting Surveys

Planning and Conducting Experiments

To start, this module will introduce you to how samples are selected from populations and conclusions made about those populations based on the samples. The module also introduces how surveys are used as an instrument for asking questions from a selected group of people.

Diploma in AP Statistics - First Assessment

In order to pass this assessment, you will need to score 80 per cent or above. Remember, if you do not achieve the required standard after the first attempt, you can re-take the assessment until a successful outcome is achieved.

Introduction to Probability

Discrete Random Variables and their Probability Distributions

Normal Distribution

Sample Distributions

Central Limit Theorem

To start, this module will introduce you to several methods used in interpreting relative frequency. The module also covers the notion of dependence versus independence in probability and how they are used in statistics

Introduction to Estimation

Tests of Significance

Large Sample Confidence Interval for a Proportion

Test for a Mean

To start, this module will introduce you to the relationship between estimation and statistical inferences. You will learn about estimating population parameters and margin errors. You will also be introduced to the properties of the point estimator.

Diploma in AP Statistics - Second Assessment

Diploma in AP Statistics - Course Assessment

An introductory college-level course that looks at probability in data exploration and dataset characterization.

Diploma in AP Statistics

This course covers anticipating patterns involving the exploration of random phenomena using probability and simulation. It also describes data exploration as a statistical concept in data analysis where analysts use visualization and statistical techniques to describe dataset characterizations. By the end of this course, you should be ready to apply statistical reasoning in various fields of learning.

Data Science

Machine Learning

Data Science Opportunities

R and R Studio Essentials

R Studio and Tidyverse

In this module on Introduction to Data Science, you will learn about the data science life-cycle stages and the fundamentals of machine learning. You will also gain insight into the milestones and current trends in data science and study the examples of Tidyverse R packages.

Data Types in R

Vectors in R

Vector Coercion and Naming

Matrices and Lists

Fundamentals of Data Frames

Helper Functions and Tibbles

In this module on Data Types and Structures in R, you will learn about the common data types in R and how to represent their missing values. In addition, gain insight into data frame helper functions and examples of one-dimensional data types in R.

Intermediate R Overview

Operators and Statements

Loops, Functions and Packages

Factors and Support

Functional Programming

Data Import, Export and Databases

In this module on Intermediate R, you will learn how to install R packages and all the components of these packages. You will also master the three ways of creating a date or time in R and the process of importing and exporting data in R.

Advanced Diploma in Data Science with R - First Assessment

This assessment enables you to review your learning so you can determine your knowledge and understanding of Modules 1 - 3. You must score 80% or more to pass this assessment.

Data Manipulation

Tidy Data and Pipe Operator

Filter and Select Verbs

Mutate, Arrange and Summarize Verb

Data Pivoting and String Manipulation

Web Scrapping and JSON Passing

In this module on Data Manipulation in R, you will learn about the examples of {stringr} verbs that work with patterns and the classification of tidy data. You will also learn how to use the key {dplr} verbs and the pipe operator to manipulate data in R.

Introduction to Data Visualization

Aesthetics Mappings and Layering

Single and Two Variable Plots

Layering and Styling

Using R Markdown

In this module on Python Libraries, you will learn how to plot single and two variables in R. You will also learn how to use the ggplot function to create objects and coordinate the system.

Introduction to R Shiny

Basic Web App

R Shiny Examples

Career in Data Science

Branding and Networking

In this module on Object-Oriented Programming, you will learn about the two main components of an R Shiny app and the examples of R Shiny output functions. You will also discover the advantages and disadvantages of using freelance websites and master the art of writing a resume.

Advanced Diploma in Data Science with R - Second Assessment

This assessment enables you to review your learning so you can determine your knowledge and understanding of Modules 4 - 6. You must score 80% or more to pass this assessment.

Advanced Diploma in Data Science with R - Course Assessment

In this free online course, learn about the important concepts and essential factors involved in data science with R.

Advanced Diploma in Data Science with R

Discover the fundamental aspects of how data science helps businesses make objective and influential decisions in this free online data science with R course. Master the real-world applications of all the data various business sectors accumulate and use it for your clients or employers. You will also gain practical experience on how to discover patterns from raw data and the many career opportunities available in the field of data science.

Foundations of Mathematics for Quantum Computing

Core Equations Defining Quantum States

Equations of Quantum Operations and Dynamics

Equations Behind Quantum Algorithms

Mathematical Equations Underlying Quantum Computing - Assessment

Learn the mathematical foundations of quantum computing, including qubits, Hilbert spaces, and quantum algorithms.

Mathematical Equations Underlying Quantum Computing

Mathematical equations form the foundation for understanding quantum computing. The course explores how quantum states are represented and evolve over time, how quantum gates operate using matrices, and how measurements collapse states into specific outcomes. Key equations, including the Schrödinger and Lindblad equations and illustrate system dynamics. Learners gain both theoretical insights and practical understanding of real quantum systems.

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