Week 3 530 Discussion
Formulating your Brief
According to Kirk (2016), The essence of “Formulating Your Brief” is to “identify the context in which your work will be undertaken and then define its aims: it is the who, what, where, when and how.” It could be formal or informal as any project you think you must make it. This phase is where you create a vision for your work.
Kirk, A. (2016). Data Visualisation: A Handbook for Data Driven Design. Thousand Oaks, CA:Sage Publications, Ltd.
Why is it so important to formulate your brief for a data presentation? Discuss some ways you would implement to formulate an effective brief. What are some advantages to your methods? What are some disadvantages?
Reply to 2 – 3 of your classmates’ methods. Please provide a summary as to whether you agree/disagree with their advantages/disadvantages.
Discussion 2 (Chapter 3): Why are the original/raw data not readily usable by analytics tasks? What are the main data preprocessing steps? List and explain their importance in analytics. Note: The first post should be made by Wednesday 11:59 p.m., EST. I am looking for active engagement in the discussion. Please engage early and often. Your response should be 250-300 words. Respond to two postings provided by your classmates. There must be at least one APA formatted reference (and APA in-text citation) to support the thoughts in the post. Do not use direct quotes, rather rephrase the author’s words and continue to use in-text citations
Ranjith Kumar Yarlagadda
Formulating Brief in Data Visualization
A brief is a way of communicating to the audience or clients and stakeholders about a project or the objectives of a business that it aims at achieving at a given time. Formulating a brief helps in providing information to clients during data presentation. For the formulation brief, the correct information should be passed to the audience to understand the aim of that project and if they support the objectives. Formulating a brief is very important in data presentation because it is an opportunity for the presenter to pass on the correct information to the audience and make sure that they understand the information being passed quickly. They are also able to visualize what is being talked about (Kirk, 2016).
Data presentation mostly contains a large volume of data. So the right method of formulating a brief is essential to make sure that the target audience can visualize and understand the presented data with ease. It is vital to give the right information in the best manner to get the best results in a presentation. An effective formulating brief usually utilizes the relationship between the presenter and the audience to ensure that the right information is being conveyed easily (Brigham, 2016).
The formulating brief should be organized so that the data is clear and concise to draw the audience’s attention throughout the presentation and make them comprehend the data. Therefore, the method that a presenter chooses for formulating a brief is vital in the presentation’s success. Some of the ways that I would use as a presenter to implement formulation brief are tedtalk and charts to help in the visualization of data for easy understanding. Tedtalk is whereby the presenter talks to the audience, and at the same time, the conversation is backed up with data slides to make it easy for the audience to follow the conference and keep their focus (Kirk, 2016).
The advantage of a formulating brief is that the credibility of the speaker and make the data visualization very easy for the audience. Also, talking can be more convincing to clients. The disadvantages are that a presenter who has poor communication skills and lacks confidence can find it hard to convince the audience. The talking may get boring for the audience. Therefore they might lose focus and not reach the objective of the presentation. The use of charts can be beneficial in data visualization because of the different colors used in bars and graphs, which may appeal to the audience.
Charts and graphs have an advantage because they usually present data in a more friendly way, making it easy for the audience to visualize and interpret (Brigham, 2016). The benefits of using charts are that the graphs can sometimes camouflage the data. The audience misses the presentation’s objective, and maps are limited to the type of data they can present. It is essential to compare the advantages and disadvantages of the methods of formulating brief and then chose the best.
Brigham, T. J. (2016). Feast for the eyes: an introduction to data visualization. Medical reference services quarterly, 35(2), 215-223.
Kirk, A. (2016). Data Visualization : A Handbook for Data Driven Design. Thousand Oaks, CA: Sage Publications, Ltd.
Importance of formulating a brief:
A design brief offers the designers of a project the vital and important data required to meet their expectations. An actual information presentation brief will be that which makes use of the relationship that occurs between the presenter and the client to make sure that it puts the information in a clear and concise manner capable of drawing the attention of the audience or those the brief is proposed to help them understand the information (Kirk, 2019). The reason why it is vital to draft it before the information visualization is that it will help serve the basis for understanding the scope and purpose of presentation to avoid unnecessary data which could turn out to be inappropriate/misrepresentation to the audience.
How to formulate an effective brief:
For the implementation and design of an actual brief, several aspects must be considered. For one, the expectations of the audience must be defined first, such as what their weaknesses and strengths are in understanding the design brief (Kirk, 2019). In addition, I will ensure that I establish and consider early thoughts as far as the purpose of it is concerned as well as drafting what am intending to achieve. I will also ensure that the model of the brief suits the view of the audience. Further the way the brief is presented and communicated will also matter a lot. Therefore, I will ensure that the brief is obtained in the already trusted media outlets to remove any issues of mistrust and eliminate instances that would bring doubt.
Pros and cons:
The accurate brief when drafted in the correct manner will assist to win over the audience to support ideas outlined within the brief. But there is a weakness that comes with formulating or drafting the brief especially when individuals end up being tested by the brief for the various beliefs and opinions that they hold (Kirk, 2019). Therefore, this calls for a moderation of the brief presentation and formulation so as not to exaggerate on one party/prefer one party. Trying to please both ends it tends to be challenging and tedious.
Kirk, A. (2019). Data visualization: A handbook for data driven design. SAGE Publications.
Yoga Sree Kakarla
Week 3 Discussion
Raw data is defined as the primary data which is not processed by the computer. This kind of data may include numbers, figures, and characters that are stored in a file by a hard disk. The information that is entered into a database by the user is called raw data. Raw data can be entered by the user or sometimes created by the computer itself. This data cannot be processed by the computer. This is a kind of difference that is sometimes made between the computer data and information that is to be processed. Sometimes raw data undergo processing and this processing is termed as cookies. Raw data can be converted into useful data by some selective organizational methods (Perez et al., 2019).
These methods filter the raw data and present it in the form of useful data. The raw data does not give any information. There are many methods in converting raw data into useful information. The raw data is needed to be converted into useful data, because raw data may contain some harmful files or trash files. The raw data may sometimes infect the work of the system. There is also an advantage of raw data, the raw data is far better than a cooked data. This raw data helps the computer work fast, analyze the data accurately, and secures the information. The raw data is very important to be converted into useful information. This information is used in many ways. Business organizations use this information to gain knowledge and develop their working skills (Vrahatis et al., 2019).
Alexandropoulos, S. A. N., Kotsiantis, S. B., & Vrahatis, M. N. (2019). Data preprocessing in predictive data mining. The Knowledge Engineering Review, 34.
Sreekanth Reddy Male
week 3 Discussion
Nature of data, Statistical Modelling, and Visualization
The primary reason why analytics tasks cannot use raw or original data is that it is overly complicated, inaccurate, and misaligned, and dirty (Huang et al., 2016). Therefore, it is necessary to process and cleanse such data to ensure that data mining models access clean data. There are challenges faced while trying to use raw data in analytics tasks, including developing and utilizing data cleansing framework. Data cleansing frameworks must be put in place to ensure that the correct data is used appropriately to maximize the value of data being analyzed (Huang et al., 2016). Therefore, without these frameworks, raw data would lead to a decline in the data being analyzed. Additionally, information is never static. It should go through a data cleansing process to ensure the proper structure of the data and remove duplicates so that such data can be used in the data mining process.
Moreover, if the management analyzes incorrect data, there is a high probability of making bad strategic decisions. Such decisions would lead to either company failure or losses, which would adversely affect the future of the company (Huang et al., 2016). Besides, big data can lead to big organizational problems as it is unstructured and uncleaned. Such data has a high possibility of causing adverse impacts rather than benefits to the entire organization.
Some of the data preprocessing steps include dataset acquisition, splitting the dataset, categorizing the data, and identifying and handling missing values (Huang et al., 2016). Dataset acquisition is vital in data preprocessing as it prepares the data fully for analytics; the essential requirement is to acquire the datasets. Secondly, data preprocessing is frequently done using the python programming language, which helps initiate the preprocessing procedure by importing all necessary libraries required by the application (Huang et al., 2016). It is crucial to identify and handle the missing values during the preprocessing procedure, a process that can contribute to dataset processing. There should be the categorization of data being processed to ensure that there is an ease in future use. Lastly, the dataset has to be split into a test set and training set to isolate potential issues in the data and ensure that there is the standardization of the independent variable (Huang et al., 2016).
Huang, M. W., Lin, W. C., Chen, C. W., Ke, S. W., Tsai, C. F., & Eberle, W. (2016). Data preprocessing issues for incomplete medical datasets. Expert Systems, 33(5), 432-438.