Analytics Task and Data Mining Discussion Paper

Analytics Task and Data Mining Discussion Paper

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Description/Paper Instructions

Analytics Task and Data Mining Discussion Paper

Analytics, Task, Data, Mining, Discussion, Paper

Please answer the below two discussion questions and answers should be 250-300 words. Reply to the below resposes.

Discussion 1 (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.

Your response should be 250-300 words. Respond to two postings provided by your classmates.

Response 1: Kareem

The data analysis of historical data may be a challenging task due to the complexity of data in today’s world. There are many different reasons for this. The first problem is that the data may be very old, and there may be some outliers of historical data. These data may not be used in decision-making applications.

The second reason is that the data may be very clean. In a future world, when more accurate data is available, this may benefit decision-making applications (Dawson et al., 2019). A fundamental problem with the use of original data is the inability to store the original data in the same format required by today’s analytical engines.

These data are often in old formats that do not support the sophisticated formats found in today’s analytics environments. The lack of capability to easily convert these data into high-level data sets has created a bottleneck for analytics. The data are not ready for analysis because the data structure is not well defined, and the structure does not make sense.

For instance, a categorical variable can be written in many different ways that vary from the basic binary variable to the more complex case of the dependent variable. The raw data are not easily usable for any analyzers because the raw data are not available for analysis (Dawson et al., 2019).

Data Consolidation

Data consolidation enables one to take back all records with a similar name in different data sources. It can be very important as it can delete useless data or duplicates that are irrelevant to the company (Sharda et al., 2020). Data consolidation often leads to removing duplicate information, which may be useful for a certain business. Sometimes all data in one place can be retrieved, sometimes, not so much. A common method of data consolidation is to consolidate records between two or more data sources.

Data Cleaning

Data Cleaning is a processing step that performs the clean-up of the original data for specific reporting, analytical processing, etc. This makes it a data transformation step of an analytics platform.

Data Transformation

Data transformation is a key step in the primary data preprocessing steps. It enables the data processing on the selected data elements. Data transformation is similar to data mining, except that in this process, the transformation is carried out across all the data of the target data (Sharda et al., 2020).

Data Reduction

The main objective of the data reduction step in the primary data preprocessing steps is to eliminate unnecessary data, thus reducing the amount of data required for the main data transformation step. An important aspect of Data Reduction is separating the data from the process.

Response 2: Dependra

Raw data can be defined as a collection of records, entities, facts, etc. It could be an address, location, coordinates, date, time, amounts, etc. It could be represented numerically or alphabetically, or both. Nowadays, various digital assets like audio, video, and image can be considered data(Kirk, 2019).

Raw data is unstructured and might consist of stuff that might not be necessary for our analysis. It is undeniable that data is fuel to analytics, but data quality, authenticity, richness, and consistency must be factored in to perform analysis. The analysis is done to serve a purpose, and the data we feed for analysis should back the purpose. The raw data should be processed to support our model/algorithm in advancing steps of analysis.

To harness our raw data and make it more concise, it has to undergo a transformation process. In this process, we structured our raw data, arranged data, get familiar with variables and their data type. Then we look for any outside entities in our raw data, check for any invalid ranges in data and deal with it, look for a missing field, eliminate duplication of data, etc.

Also, we look for. After this preprocessing or transformation process, raw data will be more consistent, rich, concise, and up to date (Sharda et al., 2019). This preprocessing of raw data before analysis will also ensure data credibility, data validity, and data relevancy for our project (Sharda et al., 2019). The cleaning of raw data can be done in the following way:

• Find and replace
• Sort and filter, find data, isolate, and modify it.
• Eliminate unnecessary data. (Kirk, 2019).

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

Discussion 2 (Chapter 4): What are the privacy issues with data mining? Do you think they are substantiated?

Your response should be 250-300 words. Respond to two postings provided by your classmates.

Response 1 : Aslam

The process that generates the power of AI is the building of models based on datasets (Sharda, Delen & Turban, 2020). Therefore, it is data that makes AI what it is. With machine learning, computer systems are programmed to learn from data that is input without being continually reprogrammed.

In other words, they continuously improve their performance on a task—for example, playing a game—without additional help from a human. Machine learning is being used in a wide range of fields: art, science, finance, healthcare—you name it. And there are different ways of getting machines to learn. Some are simple, such as a basic decision tree, and some are much more complex, involving multiple layers of artificial neural networks.

Just as machine learning is considered a type of AI, deep learning is often considered to be a type of machine learning—some call it a subset. While machine learning uses simpler concepts like predictive models, deep learning uses artificial neural networks designed to imitate the way humans think and learn.

You may remember from high school biology that the primary cellular component and the main computational element of the human brain is the neuron and that each neural connection is like a small computer. The network of neurons in the brain is responsible for processing all kinds of input: visual, sensory, and so on.

Whereas with machine learning systems, a human needs to identify and hand-code the applied features based on the data type (for example, pixel value, shape, orientation), a deep learning system tries to learn those features without additional human intervention. Take the case of a facial recognition program.

The program first learns to detect and recognize edges and lines of faces, then more significant parts of the faces, and then finally the overall representations of faces. The amount of data involved in doing this is enormous, and as time goes on and the program trains itself, the probability of correct answers (that is, accurately identifying faces) increases.

And that training happens through the use of neural networks, similar to the way the human brain works, without the need for a human to recode the program (Sharda, Delen & Turban, 2020).

Response 2: Nikita

Artificial intelligence (AI) can be a computer, machine, or computer-controlled robot designed to mimic human intelligence to perform tasks that humans usually do. It uses algorithms to replicate the human mind and act, think, respond, and speak like humans. AI is growing in manufacturing, service, healthcare, and government industries, changing how we interact daily. Machine learning and deep learning are artificial tools. It’s essential to understand the difference between each other.

Machine learning is a sub-branch of AI; it uses automated algorithms, historical data, and labels to identify patterns to make decisions with less human intervention. An example can be speech recognition, which can translate the speech into words.

Deep learning is a subset of machine learning that utilizes multi-layer artificial neural networks and computer-intensive training. Deep learning can determine the relations between data with experience. It is a technology behind autonomous cars, virtual assistants, facial recognition, and its architecture are capable of more complex works.

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.

RUBRIC QUALITY OF RESPONSE NO RESPONSE POOR / UNSATISFACTORY SATISFACTORY GOOD EXCELLENT Content (worth a maximum of 50% of the total points) Zero points:  Student failed to submit the final paper. 20 points out of 50:  The essay illustrates poor understanding of the relevant material by failing to address or incorrectly addressing the relevant content; failing to identify or inaccurately explaining/defining key concepts/ideas; ignoring or incorrectly explaining key points/claims and the reasoning behind them; and/or incorrectly or inappropriately using terminology; and elements of the response are lacking. 30 points out of 50:  The essay illustrates a rudimentary understanding of the relevant material by mentioning but not full explaining the relevant content; identifying some of the key concepts/ideas though failing to fully or accurately explain many of them; using terminology, though sometimes inaccurately or inappropriately; and/or incorporating some key claims/points but failing to explain the reasoning behind them or doing so inaccurately.  Elements of the required response may also be lacking. 40 points out of 50:  The essay illustrates solid understanding of the relevant material by correctly addressing most of the relevant content; identifying and explaining most of the key concepts/ideas; using correct terminology; explaining the reasoning behind most of the key points/claims; and/or where necessary or useful, substantiating some points with accurate examples.  The answer is complete. 50 points:  The essay illustrates exemplary understanding of the relevant material by thoroughly and correctly addressing the relevant content; identifying and explaining all of the key concepts/ideas; using correct terminology explaining the reasoning behind key points/claims and substantiating, as necessary/useful, points with several accurate and illuminating examples.  No aspects of the required answer are missing. Use of Sources (worth a maximum of 20% of the total points). Zero points:  Student failed to include citations and/or references. Or the student failed to submit a final paper. 5 out 20 points:  Sources are seldom cited to support statements and/or format of citations are not recognizable as APA 6th Edition format. There are major errors in the formation of the references and citations. And/or there is a major reliance on highly questionable. The Student fails to provide an adequate synthesis of research collected for the paper. 10 out 20 points:  References to scholarly sources are occasionally given; many statements seem unsubstantiated.  Frequent errors in APA 6th Edition format, leaving the reader confused about the source of the information. There are significant errors of the formation in the references and citations. And/or there is a significant use of highly questionable sources. 15 out 20 points:  Credible Scholarly sources are used effectively support claims and are, for the most part, clear and fairly represented.  APA 6th Edition is used with only a few minor errors.  There are minor errors in reference and/or citations. And/or there is some use of questionable sources. 20 points:  Credible scholarly sources are used to give compelling evidence to support claims and are clearly and fairly represented.  APA 6th Edition format is used accurately and consistently. The student uses above the maximum required references in the development of the assignment. Grammar (worth maximum of 20% of total points) Zero points:  Student failed to submit the final paper. 5 points out of 20:  The paper does not communicate ideas/points clearly due to inappropriate use of terminology and vague language; thoughts and sentences are disjointed or incomprehensible; organization lacking; and/or numerous grammatical, spelling/punctuation errors  10 points out 20:  The paper is often unclear and difficult to follow due to some inappropriate terminology and/or vague language; ideas may be fragmented, wandering and/or repetitive; poor organization; and/or some grammatical, spelling, punctuation errors 15 points out of 20:  The paper is mostly clear as a result of appropriate use of terminology and minimal vagueness; no tangents and no repetition; fairly good organization; almost perfect grammar, spelling, punctuation, and word usage. 20 points:  The paper is clear, concise, and a pleasure to read as a result of appropriate and precise use of terminology; total coherence of thoughts and presentation and logical organization; and the essay is error free. Structure of the Paper (worth 10% of total points) Zero points:  Student failed to submit the final paper. 3 points out of 10: Student needs to develop better formatting skills. The paper omits significant structural elements required for and APA 6th edition paper. Formatting of the paper has major flaws. The paper does not conform to APA 6th edition requirements whatsoever. 5 points out of 10: Appearance of final paper demonstrates the student’s limited ability to format the paper. There are significant errors in formatting and/or the total omission of major components of an APA 6th edition paper. They can include the omission of the cover page, abstract, and page numbers. Additionally the page has major formatting issues with spacing or paragraph formation. Font size might not conform to size requirements.  The student also significantly writes too large or too short of and paper 7 points out of 10: Research paper presents an above-average use of formatting skills. The paper has slight errors within the paper. This can include small errors or omissions with the cover page, abstract, page number, and headers. There could be also slight formatting issues with the document spacing or the font Additionally the paper might slightly exceed or undershoot the specific number of required written pages for the assignment. 10 points: Student provides a high-caliber, formatted paper. This includes an APA 6th edition cover page, abstract, page number, headers and is double spaced in 12’ Times Roman Font. Additionally, the paper conforms to the specific number of required written pages and neither goes over or under the specified length of the paper.
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