Program Overview

Introduction to Statistical Data Science

The Introduction to Statistical Data Science program is designed to provide students with a comprehensive understanding of statistical data science concepts and techniques.

Course Agenda

The course agenda includes the following topics:

• Data Types:
  • Students will learn about the different types of data, including numerical, categorical, quantitative, qualitative, discrete, continuous, ordinal, and nominal.
• Descriptive Statistics:
  • Students will learn how to summarize the location, spread, and shape of data using simple metrics such as mean, median, variance, and more.
• Graphical Summaries:
  • Students will be introduced to different types of graphs, including bar charts, histograms, boxplots, and scatter plots, and will learn how to interpret and use them.
• Probability Distributions:
  • Students will learn how probability distributions can be used to describe the probability of outcomes in experiments or events.
• Statistical Testing:
  • Students will learn about statistical testing procedures, including important concepts such as p-values, and will learn how to draw conclusions from statistical tests.
  • Applications of statistical testing include:
    • Testing for deviation from a hypothesized value
    • Testing for differences between two or more groups
    • Testing for changes following an event or intervention
• Simple Linear Regression:
  • Students will learn about simple linear regression, a statistical modeling technique used to understand relationships between variables and make predictions.
• Python for Data Analysis:
  • Students will learn basic programming, data manipulation, data visualization, statistical testing, and linear regression using Python.

Bespoke Training Courses

The program also offers bespoke training courses for companies and organizations looking to up-skill their staff in data science. These courses can be tailored to meet the specific needs of the company or sector, using relevant examples, case studies, and datasets. The courses include access to online material and in-person workshops.

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