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import java.util.Scanner; // Import the Scanner class for input
//create a Printvertical class
class PrintVertical {
//static method that prints the sentence vertically
stati...
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Campbellsville University Data Completeness Checking for Missing Information Analysis
This week our topic shifts to working with data. In this week’s discussion post, think about the topics that Kirk (2019) briefly went over in the textbook. When you work with data, it can come to you in a variety of formats: scraps of paper, spreadsheets, or databases, to name a few. The way the data is presented in any of the formats also varies significantly. There are two main reasons that you work with data before visualizing it: exploration and cleaning.
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Provide two different aspects of data that need to be checked for when you clean the data.
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Keep in mind there are many things that could be checked or cleaned, but they don't need to be done everytime. For example, analyzing the data distribution is important for certain types of analysis, but it's not necessary something that has to be cleaned. It is not needed when conducting visual analysis. It could serve other benefits, but it's not needed.
References
Kirk, A. (2019). Data analysis: A handbook for data driven design. Sage.
Zumel, N. (2016, March). Preparing data for analysis using R. Github. https://winvector.github.io/DataPrep/EN-CNTNT-Whitepaper-Data-Prep-Using-R.pdf
General forum requirements:
Three posts are required: the initial post and two peer responses.
Initial posts are needed by Friday at 11:59 PM ET.
Peer responses must be sustantive: connect with the discussion topics and what your peer has contributed - Sunday 11:59 PM
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Most Popular Content
NCSU BinaryTree Implementation Linked Nodes Pointers & Abstract Data Type Exercise
Set up your project with the given starting code. The files should compile and run, but either no tests will berun, or tes ...
NCSU BinaryTree Implementation Linked Nodes Pointers & Abstract Data Type Exercise
Set up your project with the given starting code. The files should compile and run, but either no tests will berun, or tests will run but be failing.2. For this project, start by uncommenting the first TEST_CASE in the assg11-tests.cpp file. These are the unittests to test the functionality of your BinaryTree insert() function, the class recursive method you are toimplement.13. Add the insert() public and private prototypes to the BinaryTree.hpp header file.4. Add a stub for your two insert() member function to the BinaryTree.cpp implementation file. The publicinsert() is a void function so it can initially be empty. The private version of the function should return apointer to a BinaryTreeNode, so you should create a new node and return it initially from this function.5. Your code should compile and run now. Make sure after adding the public and private stub methods your codecompiles and runs. However, your unit tests will be failing initially.6. Incrementally implement the functionality of your insert() member functions. Most of the work is actuallydone by the private insert() function. You should try to add no more than 2 or 3 lines of code, and thenmake sure your program still compiles and runs. Start by adding code to get the first failing test to pass. Thenonce that test passes, move on to the next failing t
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This week our topic shifts to working with data. In this week’s discussion post, think about the topics that Kirk ...
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This week our topic shifts to working with data. In this week’s discussion post, think about the topics that Kirk (2019) briefly went over in the textbook. When you work with data, it can come to you in a variety of formats: scraps of paper, spreadsheets, or databases, to name a few. The way the data is presented in any of the formats also varies significantly. There are two main reasons that you work with data before visualizing it: exploration and cleaning.
More often than not, data is examined for cleanliness with visual analysis. However, that doesn't seem to align with what has been covered so far. When Kirk (2019) discusses the process of visual analysis, most of the processes apply to a finished product, something you'll present to others. The processes are not necessarily important for visualization you use for your own understanding.
There are many ways that could explore data; for this forum we're going to focus more on collection and cleaning.
Considering the textbook and Zumel (2016), consider the different elements that need to be investigated everytime you clean data. Discuss the folllowing:
Provide two different aspects of data that need to be checked for when you clean the data.
Provide real examples and explain why it's necessary everytime you clean data and why it's mportant.
Keep in mind there are many things that could be checked or cleaned, but they don't need to be done everytime. For example, analyzing the data distribution is important for certain types of analysis, but it's not necessary something that has to be cleaned. It is not needed when conducting visual analysis. It could serve other benefits, but it's not needed.
References
Kirk, A. (2019). Data analysis: A handbook for data driven design. Sage.
Zumel, N. (2016, March). Preparing data for analysis using R. Github. https://winvector.github.io/DataPrep/EN-CNTNT-Whitepaper-Data-Prep-Using-R.pdf
General forum requirements:
Three posts are required: the initial post and two peer responses.
Initial posts are needed by Friday at 11:59 PM ET.
Peer responses must be sustantive: connect with the discussion topics and what your peer has contributed - Sunday 11:59 PM
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