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MIS771 Descriptive Analytics And Visualisation, Deakin University, Australia


Question 1: Describe the Audience for your Data Visualisation.

Answer: Audience: This visualization targets all daily users of music data, the most common users of such data includes singers, Music companies, fans, advertisers , music promoters media houses among others groups. The projects targets at availing important statistics such groups needs daily for the purpose of decision making. Below are more details about these users.

Singers: Artistes are the main audience of this visualization; it will help them know to what audience one ought to direct his music. Some frequent questions artistes might want to get answers about includes, how much they should invest in advertisement, what genre of the music should they choose, which streaming sites are less or more prone to piracy, how attractive they should be in order to be accepted, which language they should use in order to improve sales.

By discovering the most selling genres, the genres which require intensive advertisements and marketing, relationship between numbers of pirated copies across the streaming sites will help singers make a decision on which streaming sites to trust with their content as well as how to control these variables in order to maximize their returns. The visualization therefore will be instrumental to singer's decision making

Music companies: Music companies are out to make money; artistes are like assets to them. This visualization will be useful to them in answering questions related to; which singers they should sign. Common questions would be similar to those of singers, I.e from which genre should they recruit artistes, what language do they need to sign, how able are the singers in lobbying and campaigning for supporters,what type of gender should be signed (in some countries female artistes are not much accepted due to culture). This decision of most companies on these variables is advised by the expected return and the kind of artistes already signed. For instance a company might be looking to sign female artistes if they have so many male artistes.

Artistes therefore can be assigned market value using these visual representations, from the time plot the singers can appreciate as depreciate based on the command of fans he/she has and overall changes of the measured attributes.

Advertisers: Advertisers here refer to promoters of music albums and advertisers of other products. Promoters can get useful information from the visualizations of this data. Some common questions they would like to answer include. Which music should they choose to promote? Which music is more acceptable in each country? Does choosing album based on these variables really matter to their sales.

Another group of advertisers include companies looking for brand ambassadors, the choice they make really matter , their brands might turn more acceptable based on the choice of brand ambassadors they make , perception can really turn losses in to profits, common questions includes. Which artiste is treading (has maintained consistence in Top20), for the type of brand they are handling do they choose male or female. Do they source local artistes or do they go for foreigners?, Does the artist content qualify for commercials( some music genres might not be applicable in advertisements)

Media: Media will also be a target audience in this visualization. Media houses like radio stations and tv stations give airplay based on whether a singer is likely to attract men listeners, from this visualizations they will be able to know what variables to choose artistes based on sample question a person would ask when choosing a play list or when making programs is, which artistes are trending? What genre of music is trending, who are target listeners of their program, these question are answered by checking which music is being downloaded. Which music is much selling, does the music match their broadcast language? The moral a media house propagates also determines which genres it will give knowing the genres is important(religious media houses will rarely play secular music, secular programs/segment as well will rarely play religious music.


Question 2: Identify Data Elements.

Answer: Data elements: Album no- This is a unique identification number assigned to each album, being a label variable, Album number is categorical, the variable enables us identify each album during data cleaning or either data analysis. The variable also helps to conserve confidentiality by not displaying the names, or personal information. There is no possibility that a labeled data will mix up when being handled.

Advertise: this is a continuous variable measuring the advertising expense for each album. The measurement type is ratio scale. Variable is useful in measuring financial input on each album and can be used to determine if advertisement input is important in determining how the album is received. And more importantly how it affects sales.

Sales: A continuous variable measuring the output (returns) of the album. The variable is of measure type ratio scale. It is useful in measuring the output corresponding to the inputs. All the other variables can be considered to be inputs which affect sales.

Airplay: this is a discrete variable which measures the Airplay of each album (the number of times the album is played). It is of measure type ratio scale. It can be used to indicate the reach of a certain album. It also measures how a certain album is loved (demanded)

Attract: this is a rating scale categorical variable of measure type ordinal, it measures how attractive a band or a singer is on a rating scale ranging from 1-10,it is expected that albums from attractive singers attract more audience than albums from unattractive singers, attractiveness may include physical attributes as well as well as vocals

Price: This is a continuous variable of measure type ratio scale, it can be used to determine whether the set price of an album affects the amount of sale, some concerns are setting high prices might discourage people from buying, again it might look classy to have expensive music.

Gender: This is the gender of the albums either male or female, the variable is categorical and of type nominal. It is instrumental determining if ones gender determines sales, also if female/ male singer's albums are more accepted by the society, also which type of gender has to put more importance on advertisement for their music to sell.

Streaming services: This variable indicates whether the album is available for streaming in streaming site Spotify, Pandora or whether the album is completely unavailable in these sites, it is a categorical variable of measurement type nominal. With this variable one can determine the impact of availing their music to this music on sales or any other variable on the data, parading content in such sites might contribute significantly in piracy if the involved sites don't take security measure. The variable is important

Piracy: A discrete variable of measurement scale ratio scale, it measures the total number of illegal downloads. from this variable we are able to visualize how piracy affect sales, as well what type of music is prone to piracy also which streaming sites attracts more piracy copies. i.e some sites might have bad security measures to avoid piracy. This variable is instrumental for people who want to understand the patterns of piracy and possible measures one can take to avoid

Top 20: A categorical variable of measure type nominal. It indicates whether any of the tracks on the album appeared on top 20 billboard for the last 2 weeks. Probably these increases reach to people who have not heard about the music. The variable can be compared to sales and any other variable in this data set.

Age: A continuous variable which is measure of singers Age, it's of me of measure type ratio scale. Probably being elder is related to the number of albums one has and how known he/she is hence might affect sales, Top20 and other variables too.

No previous Album: A discrete variable of measure type ratio scale, it measures the number of previous records a singer has, having many albums means that one is known and is already a brand. This can be compared with top 20 , and any other variable to visualize how the two relates.

ITunes downloads: A discretea discrete variable of measure type ratio scale, it measures the number of downloads in iTunes for the past two weeks, the variable is informative as these are part of total downloads in (includes pirated downloads) it can also be used to measure the overall demand of the album.

Genre: A categorical variable of measure type nominal. It indicates the genre of the music. It can be used to visualize the most loved genre of music by comparing it with the number of iTunes downloads or sales.Adding a country to this tells what genre is more spread across the world.

Year released-a categorical variable of nominal scale it indicates the year an album was released, most recent albums are expected to have high airplay and demand other relationships are sales song songs hits but vanish very soon, this variable can be used if one is interested on identifying characteristics of such tracks.

Language: this is the language of the album, it's a categorical variable of type nominal it determines the target audience of the album; the variable is expected to be more related to geographical location (country) the variable is useful when in in deciding relationships between genre and language(if genre dictates the language)

Country: A categorical variable of type nominal indicates the country of origin of album the singer. It is useful to understand relations such as which genre of music is acceptable in certain country. Also which genre is more listened in certain countries The variable is expected to be related to language.


Question 3: Describe at a high level what types of charts you might use to display the data.

Answer: Chart elements: The following charts and elements will be considered in this analysis Bar graphs, scatter graphs, time plots, pie charts, geographical maps, Histograms and tables

Bar graphs: Bar charts are useful charts in comparing categorical variables; the charts are instrumental in comparing the frequencies of the categories on the variable of interest .Some categorical variables of interest include gender. The gender of the artistes will be graphed using a bar chart to show how many artistes in this sample come from each genderclass . Other variable to be overplayed includes country. This enables the visualization co communicate the distribution of gender (here gender refers to the gender of the artiste) or the respective countries, this enables the target audience to decide between male and female on their target countries. Overlaying language, language and genre enables audience to choose artiste based on these variables.

Scatter graph: A scatter graph is a simple chart use to compare pattern of relationship between continuous variables. The graph will be used to show how advertisement affects sales, and also how air play affects sales. Other variables to be overlayed include, genre gender, country language among others. These overlays communicate how sales are related to the genre of the music, how varied is the required investment on advertisement based on gender, how advertisements differs by genre and language. Another interesting relationship is how airplay is related to the previous number of records this makes it possible to answer the question whether the more the number of albums the more the airplay one is likely to give.

The chart is so crucial because it answers most of the asked questions listed on the previous section, an admiral feature of this chart is that it displays this information in different dimensions such as, gender, colour, text, size etc, which makes it easy to discover complex relationships and patterns between variables.

Histograms: Histograms are graphs used to show the distribution of a continuous data in similar manner to that of bar chart. The biggest difference is that the x axis for histogram has interval data the graph is useful in showing both the distribution and spread of data. The graph shows same information as a boxplot but has more information than a box plot. For this analysis an histogram will be used to visualize the distribution of sales, age, previous records and iTunes downloads.

Time plot: Time plots graphs continuous variables against time, the graphs represents the same data as histogram but with more details about time are available. Histogram assumes that items were sampled from same point of time. With time plot the target audience will be able to understand the trend of the continuous variables such as sales across time overlaying categorical variables like genres enables the audience to identify which music genre has declining trend over time in terms of sales, similarly the trend of piracy over time visualizing piracy overtime can be used to decide if the contribution of bodies mandated with curbing piracy has any impact over time

Pie charts: A pie chart is a representation of a categorical variable in sliced forms it represents same data as the bar graph, the most disadvantage is that it can only handle one dimension data. The chart will be used to visualize the distribution of albums in each streaming site also the distribution of music by language the genre will be overplayed on the chart size in order to understand the genre most hit by piracy as well as the relationship between genre and the

Maps: Geographical maps are used to represent data structured by geographical locations, for this chart the geographical location used is country. A visual representation of sales across country will give picture of much how each country has invested on music and returns, on the other hand presenting this across age gives. Overlays such as gender give more information on acceptability of music by counties, adding piracy too gives more information on which countries are more hit piracy.

Tables: Cross tabulations are used to understand the relationship between categorical variables they give a picture of the trend of one categorical variable within the other, from the analysis we will be able to analyze the impact how genre varies within the language, this enables singers to understand if the genre chosen dictates what language they are supposed to use. Also country tabulating genre across country helps know which music is preferred within a certain country. If music is less done in a given country a risk adverse investor (media houses or advertisers) will not go for it. Frequency tables can also be used to represent the distribution of categorical table. It gives the same information as the bar graphs and pie charts

Score cards: Score cards are used to observe values over time score cards of, scorecards will be used to track the average changes in income generally from music over time it helps to know if the industry is becoming better over time.


Question 4: Describe any data ethics considerations there with the data you intend to use.

Answer: Data ethics: To protect the confidentiality of the subjects, private information such as names, physical address will not be displayed will not be displayed; the lowest unit of physical location will be restrained to country too.

The second most crucial ethic consideration to keep in mind is that visually visible relationships might not be statistically significance. It is therefore important for users of these visualizations to exercise discretion before making conclusions;one can incorporate tests before reaching final conclusions.

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