Data Analytics in TED Talks Coursework

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According to TED managers, the presented data has been analyzed, and constructive information has been extracted from the analysis to inform better decision making. From the provided data, it is clear that the Statistics has been pulled from a data warehouse, which comprises a storehouse where historical data is stored with analytical intentions for the purpose of decision-making (Almeida, 2017). A warehouse stores data that is integrated from primary informational collecting systems. In a statistics mine, raw information is categorized by using both a primary and secondary key in that the respective statistics can be located by fields and records. For this reason, this paper will address the examination of excel based analysis.

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Prescriptive Analysis for Lesson One

Based on TED managers’ desire to be enlightened on the length of time a video takes before being posted online, the statistics provided a master data that involved various records. For this reason, prescriptive analytics was the core analytical technique to be used in availing information from historical indicators. Prescriptive technology is a methodology employed by businesses, via analyzing a collection of measurements, to address issues affecting the business based on several reasons (Rainer & Prince, 2019). It is, therefore, essential in data management and knowledge management. According to knowledge management systems, the technique is adapted to provide solutions that prevent a business from addressing operational, tactical, and even strategic objectives (Rainer & Prince, 2019).

Therefore, through the provision of the average number of days, maximum and minimum number of days provided, the clarity concerning possible reasons for the delay of posting videos online after production can be addressed. This might be due to a lack of enough staffing and manual tools of labor.

Descriptive Analysis for Lesson Two

Moreover, their second request concerning their desire to be informed on whether views and comments were considered on short or long videos based on time illustrated the aspect of descriptive analytics. Rainer and Prince (2019) argue that descriptive analytics is a method in most business ventures to define possible concealed trends based on historical data provision. From the historical data provided by TED managers, the comparative analysis seeks to address whether shorter or extensive video uploads are considered through the number of viewers and the statistics of comments. Based on the comparison analysis, it was clear that the historical data provided informed that longer videos have more comments and views than the shorter ones. Thus, TED managers should consider producing lengthy videos as appendices 2A, and 2B approves that lengthy videos have more view and comments, respectively.

Predictive Analysis for Lesson Three

Finally, predictive analysis is also featured in the historical statistics provided by TED managers in their quest to use the number of views and comments to inform their most insightful years. Business managers use the predictive-analytical technique to make future decisions concerning business activity patterns from past events (Rainer & Prince, 2019). The method uses historical records to determine future business events and decisions based on past trends concerning external business environments. In this view, TED business managers should review their performance between the years 2006 to 2010 consecutively, for strategic decisions as these years have greater views and comments.

The presented historical data comprises fields, records, and files that are mostly in figures, as shown in the appendices below. Based on this reason, the statistics could be addressed by applying tools such as graphs, pie charts, and, most possible, figurative communication (Almeida, 2017). The application of graphs and pie charts is more applicable when figures and numbers form the data. These mechanisms present a better visual to any manager in the event of immediate decision making. It is arguably the best format to be applied in numerical-based data by Rainer and Prince (2019). The argument is that, based on knowledge management systems, decision making in enterprise ventures should be quick, precise, and objective. It can only happen once numerical established historical data is expressed in easily interpretive means that adapts the use of tools such as harts and graphical expressions.

Finally, through the application of data mining techniques under the directives of machine learning technology, the descriptive, predictive, and prescriptive information could have been effectively presented. The technologies mentioned above reduce human error (Rainer & Prince, 2019). The expertise uses analytical data means to eliminate potential mortal inaccuracy. Generally, through this assignment, the three analytical procedures have been available as the techniques that evaluate data. Similarly, historical data is always integrated and stored in a warehouse for future retrieval and analytical interests. Generally, through these techniques, TED online-based casts have been analyzed.

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In general, new speaker’s presentations should follow the general standards and be concise, logical, and audience-oriented. For the first 5-10 minutes, speakers are recommended to introduce themselves, thank viewers for their attention, and introduce the topic of their presentation. Subsequently, managers may advice new speakers to emphasize the significance of the topic and describe underlying issue within the next range of 11-15 minutes. For subsequent several minutes, the method and algorithm of research may be presented in order to show how the described problem may be addressed and reduced. Finally, for several last minutes, speakers may present the results of the research and its practical application for subsequent studies.

References

Almeida, F. L. (2017). Benefits, challenges, and tools of big data management. Journal of Systems Integration, 8(4), 12-20. Web.

Rainer, K. & Prince, B. (2019). Introduction to information systems (8th ed). Wiley Global Education.

Appendices

Appendix 1

21586

Total No, of Days
Total no, of days /Total no, of filmsAverage formula
Observation of Greatest valueMaximum no, Formula
Observation of lowest valueMinimum no, formula
216Average
2307Maximum no, of days
1Minimum no, of days

Appendix 2 A

# Comments# Views (million)Length (minutes)
455347.2319
229043.1621
193034.3118
192731.1720
35422.2717
29721.5910
287721.1918
67220.6922
15020.4810
84619.7910

Appendix 2B

# Comments# Views (million)Length (minutes)
135511.4422
11377.6422
97014.6921
7677.2729
67220.6922
5569.2623
4196.5022
3835.8423
3708.2222
2505.6741

Appendix 3 A

Average of # Views (million)
20035.570544
20049.6906268
20058.8421612
200618.9087566
200713.926113
200810.0976812
200915.4114065
201011.007542
20118.666145417
201213.5643254
20139.168449476
20146.5789702
201510.72351311
20168.337124
20175.666038

Appendix 3B

Sum of # Comments
2003220
20042591
20054109
20066868
2007554
20084707
20099235
20106083
20116169
20127157
201310941
20141292
20153886
2016598
2017250

Appendix 3C

Average of Length (minutes)
200317.01666667
200419.68333333
200511.28
200619.57
20075.45
200815.63333333
200916.755
201014.45555556
201110.80972222
201215.46
201313.95634921
201413.22333333
201514.86666667
201612.68333333
201740.83333333

Appendix 3C

Total Average of # Views (million)10.77947544
Total Sum of # Comments64660
Total Average of Length (minutes)14.76466667.
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