A Practical Introduction to Data Science

Course Aim/Overview

This course will involve coding with the Python programming language, which is one of two primary languages for professionals working in data science (the other being R). 

This course is aimed at beginners, so template code will be provided and lots of non-coding activities will be provided.  Some simple statistics will be explained and used, but no previous knowledge is expected.

• What is Data Science?  Understand what data science is and what it is used for.  Understand the various data roles that exist and what they entail.
• Machine Learning: Understand how machines learn from data using algorithms.
• Predict A Sporting Event Outcome: A simple activity to show how algorithms can be used to predict who will win a match.
• Predict a Category: Build a classification model to predict what someone might do, e.g. can we predict whether someone will buy a product?
• Predict a Numerical Value: Build a regression model to predict a value, e.g. can we predict the happiness level of a country?
• Visualisation: Use visualisation to understand and explain data patterns.
• Data Science Competition: Compete against your fellow students to build a model to make predictions based on some data. You will be provided with a scenario and the data.

This course is available at our London campus or online.

 

Why this course?

The volume of data being generated has grown massively in the world in recent years and continues to grow.  Our interactions at work are increasingly data-driven, with organisations needing to understand and improve their business performance and interact more effectively with their customers.  In addition, our personal lives are increasingly being affected by the processing of our data, from our financial transactions to interactions with social and media platforms to personal fitness, we generate huge quantities of data and trust organisations to use that data responsibly.

This explosion in data has been accompanied by an explosion in jobs in the data space.  Data engineers, data analysts, data scientists and a whole host of other roles are now commonplace in organisations.

This course has been developed to provide a gentle introduction to data science for those starting their exploration of the power of data and data science.

 

Who is the course for?

• Individuals wanting to start out on a career in data analysis, data science or data engineering. g. pre-university students, career changers
• Individuals working with data specialists who want to better understand what they do to improve their interactions with them. g. project managers, software developers
• Individuals curious about how the science of data works, to better understand how their data is processed at a technical level

 

Course materials

• Students should bring a laptop (Windows, Mac, or Linux).
• Students may be required to install software, so administrative privileges on the computer may be required. Alternatively, students should have a Google account to use the online platform.  Instructions will be sent before the course.
• Course notes and code will be provided.

 

Prerequisites

• You should be comfortable using your computer to install programs, download files, organise files etc.
• No specific programming experience is needed, but any exposure to programming or scripting would be useful

 

Essential reading

Whilst not essential, students may want to read some of these very accessible books on data, algorithms, and statistics to get a feel for the background of the subject:

• Outnumbered: From Facebook and Google to Fake News and Filter-bubbles – The Algorithms That Control Our Lives
  David Sumpter
• Factfulness: Ten Reasons We’re Wrong About The World – And Why Things Are Better Than You Think
  Hans Rosling, Ola Rosling
• How to Make the World Add Up: Ten Rules for Thinking Differently About Numbers
  Tim Harford

 

Learning outcomes

On completion of this course, students will be able to:

• Understand the power of data for modelling and decision making
• Understand the typical tasks and activities of a data scientist
• Find and prepare data sets for modelling
• Write Python code to analyse data
• Write Python code to visualise data