Fix broken links

Fix broken links: Data Science in the cloud, Data Preparation, and Ethics. 
Minor format modification.

4-Data-Science-Lifecycle/14-Introduction/README.md

@@ -28,7 +28,7 @@ Defining the goals of the project will require deeper context into the problem o
 Questions a data scientist may ask:
 -	Has this problem been approached before? What was discovered?
 -	Is the purpose and goal understood by all involved?
--	Where is there ambiguity and how to reduce it?
+-	Is there ambiguity and how to reduce it?
 -	What are the constraints?
 -	What will the end result potentially look like?
 -	How much resources (time, people, computational) are available?
@@ -62,16 +62,18 @@ Considerations of how and where the data is stored can influence the cost of its
 Here’s some aspects of modern data storage systems that can affect these choices: 
 
 **On premise vs off premise vs public or private cloud**
-On premise refers to hosting managing the data on your own equipment, like owning a server with hard drives that store the data, while off premise relies on equipment that you don’t own, such as a data center. The public cloud is a popular choice for storing data that requires no knowledge of how or where exactly the data is stored, where public refers to a unified underlying infrastructure that is shared by all who use the cloud. Some organizations have strict security policies that require that they have complete access to the equipment where the data is hosted and will rely on a private cloud that provides its own cloud services. You’ll learn more about data in the cloud in [later lessons](5-Data-Science-In-Cloud).
 
-**Cold vs hot data** 
+On premise refers to hosting managing the data on your own equipment, like owning a server with hard drives that store the data, while off premise relies on equipment that you don’t own, such as a data center. The public cloud is a popular choice for storing data that requires no knowledge of how or where exactly the data is stored, where public refers to a unified underlying infrastructure that is shared by all who use the cloud. Some organizations have strict security policies that require that they have complete access to the equipment where the data is hosted and will rely on a private cloud that provides its own cloud services. You’ll learn more about data in the cloud in [later lessons](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/5-Data-Science-In-Cloud).
+
+**Cold vs hot data**
+
 When training your models, you may require more training data. If you’re content with your model, more data will arrive for a model to serve its purpose. In any case the cost of storing and accessing data will increase as you accumulate more of it. Separating rarely used data, known as cold data from frequently accessed hot data can be a cheaper data storage option through hardware or software services. If cold data needs to be accessed, it may take a little longer to retrieve in comparison to hot data.
 
 ### Managing Data
-As you work with data you may discover that some of the data needs to be cleaned using some of the techniques covered in the lesson focused on [data preparation](2-Working-With-Data\08-data-preparation) to build accurate models.  When new data arrives, it will need some of the same applications to maintain consistency in quality. Some projects will involve use of an automated tool for cleansing, aggregation, and compression before the data is moved to its final location. Azure Data Factory is an example of one of these tools.
+As you work with data you may discover that some of the data needs to be cleaned using some of the techniques covered in the lesson focused on [data preparation](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/2-Working-With-Data/08-data-preparation) to build accurate models.  When new data arrives, it will need some of the same applications to maintain consistency in quality. Some projects will involve use of an automated tool for cleansing, aggregation, and compression before the data is moved to its final location. Azure Data Factory is an example of one of these tools.
 
 ### Securing the Data
-One of the main goals of securing data is ensuring that those working it are in control of what is collected and in what context it is being used. Keeping data secure involves limiting access to only those who need it, adhering to local laws and regulations, as well as maintaining ethical standards, as covered in the [ethics lesson](1-Introduction\02-ethics). 
+One of the main goals of securing data is ensuring that those working it are in control of what is collected and in what context it is being used. Keeping data secure involves limiting access to only those who need it, adhering to local laws and regulations, as well as maintaining ethical standards, as covered in the [ethics lesson](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/1-Introduction/02-ethics). 
 
 Here’s some things that a team may do with security in mind:
 - Confirm that all data is encrypted