Data Research and Statistical Techniques

Data Research and Statistical Techniques


In this course, we’ll cover the fundamental research methodology and modelling concepts that are key to performing robust data analytics, visualisation, and data interpretation.

This course is for anyone who is interested in learning the essential research methods and tools used to draw conclusions from data.

The course will introduce you to the fundamental concepts of data research, such as quantitative and qualitative research techniques, experimental and non-experimental research approaches, and inferential tests of real-time scenarios.

As you work through the course, you’ll learn descriptive statistics, which summarise data from a sample using indexes, such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation).

The course consists of three modules and includes demonstrations, hands-on activities, and knowledge tests at the end of each section of the course.

Module 1: Data Research Techniques & Statistics for Data Science
  • Data Research Techniques;
  • Simple Descriptive Statistics;
  • Common Approaches to Sampling Data;
  • Inferential Statistics
Module 2: Statistical & Hypothesis Tests
  • Getting Started with Hypothesis Testing;
  • Using the One-Sample T-Test
Module 3: Data Research Techniques & Statistical Approaches
  • Data Research Exploration Techniques;
  • Data Research Statistical Approaches
Who is this course for?
The aim of the course is to ensure your understanding of the fundamental research methods and tools used to draw conclusions based on data.

This course is designed for those looking to embark on a career in data and analytics, or who need to have an understanding of key approaches to support them in their current role.

There are no prerequisites for studying this course.

Career path
To enter the field of data science, it’s essential to learn the techniques underpinning data research and statistics. A strong understanding of research methodology and modelling is key to performing
robust analytics and data interpretation.