Analysis And Analysis On Data Cleaning Essay

Data collection has been around for years in one form or another. The implementation of the No Child Left Behind Act stimulated dedicated educators to learn the correlation between data driven decision-making and successful school improvement plans. The legislative goal was to ensure academic success across all socioeconomic frontiers. Districts across the country were steered into driving their instruction with data and teacher collaboration. This has lead to districts that have successfully found the correlation between data driven decision-making and success.

Regulation placed upon the healthcare system only seek to improve safety and security of the patients we care for. The enactment of the Health Insurance Portability and Accountability Act (HIPPA) and the enactment of Meaningful Use Act the United States government has set strict regulations on the security of health information and has allotted for stricter penalties for non-compliance. The advancement of electronic health record (EHR) systems has brought greater fluidity and compliance with healthcare but has also brought greater security risk of protected information. In order to ensure compliance with government standards organizations must adapt

For this assignment we needed to compute the mean, median, and mode for five quantitative variables. The five that were computed in this assignment were number of total prior arrests, number of prior misdemeanors, number of total prior convictions, number of prior felony arrests, and number of drug convictions. The mean is defined as the average in a group of numbers, the median is the middle number in a group, and the mode is the most frequently occurring number in the group.

Data can be stored in data warehouses. However, they need data cleansing to standardise the data.

After entering the data, it must be reviewed to catch any errors. Cleaning the data is done with basic steps. Of course the data is imported from the source, but there should always be some backup involved in order to keep the integrity of the data. In some cases the next step would be manipulation or spellchecking. This depends on what kind of data is involved. However, in this case spell checking is not necessary, but manipulation is. For instance with manipulation, they may have to add a column or two, or even add a zero randomly for missing data. Below is numbers 6-10 on the data set, the data has been cleaned up by changing the number 6 to the number 5 thus correcting the errors Sally made. Where a question wasn’t answered a zero was added to indicate missing data.

There are several important steps to consider when designing a database, as a well-designed database should be deployed and not only support the accuracy and integrity of business information but also avoid redundant data and assist with has enterprise level reporting tasked. If we analyze the

For example, using the attached pivot table Kathy is able to see the top 10 inventory items by dollar value at each location. Analyzing this information can help Kathy determine if inventory will need to move from one location to another to avoid spoiling. The ability to quickly visualize this information is extremely important to making improved decisions.

For this component, we plan to optimize the DSS in three ways: 1) by enhancing the knowledge representation of the patient database; 2) by adding a probabilistic component to the classification system; 3) by improving the prediction accuracy of the classification system through the creation of statistically coherent committees. We propose to revisit the machine learning choices made in our preliminary study and integrate into the classification process descriptive features represented in different formats. We hypothesize that the information they carry may be important to consider in conjunction with the information carried by other features. This is particularly important given the sensitivity of the new task that we are planning to study.

“If a Bag is purchased, a Blush is also purchased at that same transaction.” (“If Bag, then Blush.”) While Bag is antecedent, Blush represents consequent.

Data is very important thing in every business, especially in today’s dynamic world where optimal use of data leads to success in shorter span of time as lots of companies are struggling for truthful and accurate data. These data must be analyzed in exact time and in a proper way so that the decision is more effective, but the data we receive are very redundant and carry lot of space in our system. This creates a challenge for the Analytics people to remove the redundancy and bring out only those relevant data that aids in decision making process. Master Data Management is a solution for such Analyst who wants to eliminate the redundant and inconsistent data of the organization (Vinculum, 2016).

The promise of data-driven decision-making is now being recognized broadly, and there is growing enthusiasm for the notion of ``Big Data.’’ While the promise of Big Data is real -- for example, it is estimated that Google alone contributed 54 billion dollars to the US economy in 2009 -- there is currently a wide gap between its potential and its realization.

Business analysis is a significant aspect of any business and company. This is mainly because change is the only constant thing that needs to be constantly dealt with. Change can happen in both your target market and in the industry your business or company belongs to, and for your business to survive and succeed despite these changes, proper business analysis must be conducted at the right time. In such a cutthroat business environment, business analysis is essential in order to maintain competitiveness.

Before a data set can be mined, it first has to be ?cleaned?. This cleaning process removes errors, ensures consistency and takes missing values into account. Next, computer algorithms are used to ?mine? the clean data looking for unusual patterns. Finally, the patterns are interpreted to produce new knowledge.3

Due to the rapid growth in the use of Internet and its connected tools, an enormous amount of data are being produced on a daily basis. The concept of big data arrives when we were unable to manage this huge data with traditional methods. Big data is a mechanism of capturing, storing and analyzing the big datasets and also an idea of extracting some value from it. It is very handful while determining the root causes of failures, issues and defects in near-real time, creating coupons and other sales offers according to the customers shopping patterns, detecting any suspicious and fraudulent activities in real-time. As it is very advantageous, it also has some issues. Some of the common issues can be characterized into heterogeneity, complexity, timeless, scalability and privacy. The most important and significant challenge in the big data is to preserve privacy information of the customers, employees, and the organizations. It is very sensitive and includes conceptual, technical as well as legal significance.

Data Warehousing and Data Mining has always been associated with manufacturing companies, where sales and profit is the main driving force. Subsequently Higher Education has grown throughout the years; this growth is predominately associated with the increase of online institutions. This growth has resulted in higher education to adapt to a more business like institution (Lazerson, 2000).