Data Science - Working with Data
Learn about data types, data sampling methods and data preparation techniques using R and Python programming in data science.
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The course Data Science - Working with Data will introduce you to methods for preparing data, how to differentiate between continuous and categorical variables, and what quantization and scaling involve.
The course begins by introducing you to the data flow in Azure ML, you will learn about batch and real time processing, and the different types of joins you can use on your data. You will learn about R and Python programing languages and how they can be used in a data science project.
Next, you will be introduced to data sampling and preparation. You will learn about continuous and categorical variables, and what quantization can do for your data. The course will teach you about data munging which is the process of manually converting or mapping data from one "raw" form into another format, and how it is the most time-consuming part of a data science project. You will also learn about handling errors and outliers in your project. Finally, you will learn about scaling using either Python, R or Azure ML module for scaling.
This course will be of great interest to those who wish to learn about data science.
Prerequisites: To complete this course successfully you need a basic knowledge of mathematics, including linear algebra. Additionally, some programming experience, ideally in either R or Python, is assumed and you will need to have completed the previous course Introduction to Data Science.
Having completed this course you will be able to: - Describe the flow of data in a Azure ML experiment. - Discuss the differences between using R and Python. - Identify which programming language suits you better R or Python. - Describe installing both R and Python in your Azure ML environment. - Discuss data preparation also known as data munging to prepare data for your project. - Explain what quantizing your variables is and does. - Explain how to deal with missing values in your data sets. - Describe why you should scale your variables.
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This Course has been revised!
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