**Overview of this Lecture / week **

This week we will look at some of the typical data preparations steps that you will need to perform. It would be great if data was in a clean state. Sadly this is never true. You are always going to have inconsistencies in the data. You will also have attributes/variables/columns/features that contain null values. What does a null value really mean? For machine learning we never want to have null values, but what can we do about it. Well we have a number of ways of working out what a possible value should be. Other things we have to do is integrate data from different sources, do some dimensionality reduction, etc, etc.

There are lots and lots of things you can do to prepare and format the data. You will cover many different techniques in other modules. No one approach is correct but with practice you will learn what works best for your data and scenario. We will cover the main ones in this module.

**Notes**

Click here to download Week 3 notes.

L3 - DM Data Prep**Videos of Notes**

**Related Videos**

**Lab Exercises**

This weeks lab involves loading data into your SAS Enterprise Miner workspace, and using the features in the tool to explore the data. Remember all Data Science tools and languages just gives you more data. It is your job as the data scientist to put meaning to it, by taking your domain and business knowledge and applying it to the statistical outputs from exploring the data.

Task 1 (you should have completed this last week. Skip to Task 2 if already completed)

Create a SAS EM Project for your Lab work. Call it ‘My Lab Work’

Create and Open a SAS EM Project

Task 2 – Access the SAS data sets

How to access the SAS Data Sets for your Lab work

Task 3 – Accessing and Analysing your data

Lab 1 – Accessing and Analysing your Data – start at section 2.3 on Page 13

Refer back to Task 2 for the location of all data sets for the SAS exercises.

Lab1_Accessing_and_Assaying_Prepared_Data

Task 4 – Optional – Load your own data into SAS Enterprise Miner

You can load your own data into SAS Enterprise Miner. The following two guides will show you how to do this.

How to load your own data sets into SAS OnDemand

SAS Guide for loading your own data using SAS Studio

**What to prepare for next week**

Make sure you complete all the steps in the lab document before next week.

The future lab exercises for SAS Enterprise Miner are dependent on you completing this weeks tasks

Next week will involve some exercises using R. Make sure you have installed R and are familiar with the environment, installing packages and writing some basic R code.

**Additional Reading Materials**

Han & Kamber Book Chapter – Data Processing

One-page Survival Guide to Data Science with R

Reducing Dimensionality from Dimensionality Reduction Techniques

Some interesting and inaccurate Correlations

Seven Techniques for Data Dimensionality Reduction