Online Introduction to Data Science

Modern businesses are always looking for ways to improve their strategies. "Which webpage attracts more readers?", "Does the new store layout affect sales?" or "What can we do to improve customer satisfaction?" are just a few examples of questions that sit behind decision making in companies.

To answer these questions we need a mix of analytical skills (to manipulate data), business acumen (to apply findings to real-world situations) and statistics (to separate what's essential from what's not).

This is the skillset of the ‘data scientist’, a new job role that has emerged to meet the increased demands and opportunities of the profusion of data generated by the web and modern technology. This course wrests data science back from the data scientist – it teaches the key elements of data science to allow business generalists to solve real business problems themselves. It also provides an accelerated way for those interested in a career in data science.

Highlights

Discover the skill set of a Data Scientist, a new role meeting the increased demands and opportunities of the web and modern technology:
• Use your analytical skills to manipulate data
• Develop business acumen, so findings are applicable in the real world
• Master statistics, to separate vital signals from irrelevant noise

Syllabus

Section 1: Introduction

Unit 1.1 - Introduction

An introduction to the course providing you with an understanding of what data science can do and the skills involved.

Section 2: Software Tools

Unit 2.1 - Software Tools Overview And Setup

The rationale for using Excel and R and how to set up so you are ready to work with them.

Unit 2.2 - Basics of Excel

Discover using Excel as a toolkit for the data scientist.

Unit 2.3 - Basics of R

Discover using R as a toolkit for the data scientist.

Unit 2.4 - Section Summary

A section review on the key elements of Excel and R that will enable you to manipulate and analyse data to develop insight.

Section 3: Understanding Data

Unit 3.1 - Initial Appraisal of a Data Set

How to get to grips with a new data set.

Unit 3.2 - Handling Big Data

Use R to examine a big data file in order to understand it, clean it and retrieve the information you are looking.

Unit 3.3 - Characterising a Data Set

How to characterise / summarise a new data set.

Unit 3.4 - Probability

The basics of probability, and how to calculate and combine probabilities.

Unit 3.5 - Section Summary

A section review on how we can understand, describe and interpret a data set.

Section 4: Inferences from Data

Unit 4.1 - Visualisation

How to understand a whole population by looking at sample data from it.

Unit 4.2 - Making Predictions

How to present data visually in order to allow for a greater understanding and insight.

Unit 4.3 - Decision Making

How to use data to inform decision making.

ABOUT THE AUTHORS, Dr Chris Littlewood & Matilde Castanheira

Dr Chris Littlewood (Head of Science)

Chris worked as a consultant at strategy boutique Mars & Co after completing degrees in physics and maths at Oxford and Cambridge and conducting research in particle physics at CERN. He has ten years experience in analysis and strategy development, across industries at Mars & Co and then in the rail sector. He leads on the science behind the product, particularly the filtering algorithm. He is a Fellow of the Royal Statistical Society.

Matilde Castanheira (Data Scientist)

Matilde’s role is to analyse all of our data to improve the algorithm that selects which modules each user might need and also to provide useful business improvements. She has a PhD in particle physics where she braved through terabytes of data (that means a lot of data!) to discover the small signal that was of interest to her chosen topic.

• This course is suitable to those new to data science who want to understand what data science can do and the skills involved.
• It's also suitable for those who want to develop a core toolkit of technical, statistical and analytical techniques that turn data into insight and support business decision making.

• No technical, software or analytical knowledge is needed beyond a grounding in basic maths.

• The course teaches the analytical and statistical skills to allow students to turn data into actionable insights.
• It also covers how to use an analytical toolkit consisting of widely available or free software (principally Microsoft Excel and the R programming language), to allow statistical analysis and visualization.

Take your first steps to become a Data Scientist

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