Fundamentals of Data Science

Fundamentals of Data Science is a three day overview course which blends discussion and group exercises to explore the field of data science with applied real world examples and projects.

Teaching begins with a conceptual introduction to science, data science, big data and machine learning; followed by a litany of real-world data science and machine learning examples.

The remainder is divided into two parts: introduction to Python, and the application of the language in building a Machine Learning model (including data preparation, statistical tests, feature engineering, modelling and evaluation).

Delegates attending this course should have technical skills such as application or SQL programming – expose to Python or R would be advantageous.

01 Introduction to Data Science

• To better understand whether you want to become a data scientist
• To work with colleagues who are data scientists
• To judge whether your team (or organization) could benefit from data science

02 Data Science Project Examples

03 Jupyter Notebook

• Practical Lab Activities

04 Introduction to Python

• Practical Lab Activities

05 Introduction to Datasets in Python

• Practical Lab Activities

06 Introduction to Machine Learning

• To understand what these are, what the different types are and some examples of what they are used for.

07 Introduction to Dataframes and Databases with Python

• Practical Lab Activities

08 Exploratory Data Analysis with Python

• Practical Lab Activities

09 Introduction to Big Data

• To better understand what skills need to be developed
• To choose the right tools to suit organisational needs
• To develop a strategy for working with Big Data