# Introduction to Data Science

Discover the skill set of a Data Scientist, a new role meeting the increased demands and opportunities of the web and modern technology.

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.

## Highlights

In this course you will learn:

•    How to use your analytical skills to manipulate data.
•    Develop business acumen, so findings are applicable in the real world.
•    Master statistics.
•    How to separate vital signals from irrelevant noise.
•    The basics of Excel and R.

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## Syllabus

### Section 1: Introduction

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#### 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

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#### 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.

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#### Unit 2.2 - Basics of Excel

Discover using Excel as a toolkit for the data scientist.

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#### Unit 2.3 - Basics of R

Discover using R as a toolkit for the data scientist.

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#### 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

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#### Unit 3.1 - Initial Appraisal of a Data Set

How to get to grips with a new data set.

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#### 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.

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#### Unit 3.3 - Characterising a Data Set

How to characterise / summarise a new data set.

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#### Unit 3.4 - Probability

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

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#### Unit 3.5 - Section Summary

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

### Section 4: Inferences from Data

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#### Unit 4.1 - Visualisation

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

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#### Unit 4.2 - Making Predictions

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

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#### Unit 4.3 - Decision Making

How to use data to inform decision making.

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#### Unit 4.4 - Section & Course Summary

A section and course review on how to draw robust business conclusions from data.

## Pricing

Pricing is for 12 months access.

## Requirements

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

Completion Time: 3 hours 55 minutes (average)