A map is a good way to see visually where something is. It makes it very easy to be able to see how one place relates to another place and the distance between places.
A graph could be a line bar, scatter graph etc. It is good to use graphs to see trends and relationships. It is good to visualise numerical data.
Charts can be used an example of a chart is a pie chart. This is good to be able to visualise and quickly see things like percentages to see how something is divided.
A Table is a nice way to show structured information each row and column has a meaning and everything placed in the table has been placed in the correct categories. It looks more organised than scattered data.
Showing posts with label LO4. Show all posts
Showing posts with label LO4. Show all posts
Wednesday, June 5, 2019
Data Mining Methods
Data is extracted in order to find some sort of pattern in the data set. There are different levels of analysis we ca
Association rule mining: this is when the program tries to see if there is a relation between two or more variables from data. It helps find the probability of relations between variables in large sets of data by making associations from the relations found.
Correlation analysis: this is used to see how strong the relationship between two variables is. It can either be a positive or negative correlation. Positive correlation is when x goes up y goes up but negative correlation is when x goes up and y goes down (on a graph).
regression analysis: this allows you to study the relationship between two or more variables. In this type of analysis one or more variables is dependent on one variable. Points are plotted on a graph and a regressive line is drawn (or line of best fit) and this can help predict the future as we see which direction the line is heading.
Association rule mining: this is when the program tries to see if there is a relation between two or more variables from data. It helps find the probability of relations between variables in large sets of data by making associations from the relations found.
Correlation analysis: this is used to see how strong the relationship between two variables is. It can either be a positive or negative correlation. Positive correlation is when x goes up y goes up but negative correlation is when x goes up and y goes down (on a graph).
regression analysis: this allows you to study the relationship between two or more variables. In this type of analysis one or more variables is dependent on one variable. Points are plotted on a graph and a regressive line is drawn (or line of best fit) and this can help predict the future as we see which direction the line is heading.
Wednesday, May 29, 2019
Types of Problem suited to Big Data Analysis
predictive analytics
Predictive analytics is when we use big data to look for patterns which can help us predict what will happen in the future based on the pattern found. For example companies and investors can use big data analytics to predict what prices will be in the future from looking at past information of what sales have been in past years. This can help them in business decision making as they can make an estimate of what will likely happen.
diagnostic analytics
Diagnostic analytics is similar to predictive analytics in that it predicts a value, however it does not necessarily predict future values. It can have any 2 variables which can be compared. Using the existing information on the graph, the location of an unmapped value on the graph can be predicted by estimating where it would most likely be. For example the number of sales of hand held fans in a shop could be based on the temperature of that day. Using the information from previous sales and temperatures we can estimate how many fans are likely to be sold based on the temperature of the day. If the temperature is 5 degrees you may expect 1 sale. If the temperature is 30 degrees you might expect 400 sales.
descriptive analytics
Descriptive analytics is when we take data and map it onto a graph. Then see if there is any relationship between two variables. If a relationship is found we say what we think the relationship is. For example if we look at the previous example we can say that the warmer the weather is on a particular day, the more fans will be sold in the shop.
prescriptive analytics
Prescriptive analytics is when we look at the relationship and make decisions based on the relationship and what we can predict. It can be used to create the best outcome of something because we can make predictions based on the relationship. Businesses can use prescriptive analytics in order to maximise profits. Healthcare professionals can use it to give people the most effective treatment. Looking back to the shop example above we can make the decision that the business should buy less hand held fans for their stock during the cold winter and make sure there is a lot of fans stocked in the summer months to maximise profits.
Predictive analytics is when we use big data to look for patterns which can help us predict what will happen in the future based on the pattern found. For example companies and investors can use big data analytics to predict what prices will be in the future from looking at past information of what sales have been in past years. This can help them in business decision making as they can make an estimate of what will likely happen.
diagnostic analytics
Diagnostic analytics is similar to predictive analytics in that it predicts a value, however it does not necessarily predict future values. It can have any 2 variables which can be compared. Using the existing information on the graph, the location of an unmapped value on the graph can be predicted by estimating where it would most likely be. For example the number of sales of hand held fans in a shop could be based on the temperature of that day. Using the information from previous sales and temperatures we can estimate how many fans are likely to be sold based on the temperature of the day. If the temperature is 5 degrees you may expect 1 sale. If the temperature is 30 degrees you might expect 400 sales.
descriptive analytics
Descriptive analytics is when we take data and map it onto a graph. Then see if there is any relationship between two variables. If a relationship is found we say what we think the relationship is. For example if we look at the previous example we can say that the warmer the weather is on a particular day, the more fans will be sold in the shop.
prescriptive analytics
Prescriptive analytics is when we look at the relationship and make decisions based on the relationship and what we can predict. It can be used to create the best outcome of something because we can make predictions based on the relationship. Businesses can use prescriptive analytics in order to maximise profits. Healthcare professionals can use it to give people the most effective treatment. Looking back to the shop example above we can make the decision that the business should buy less hand held fans for their stock during the cold winter and make sure there is a lot of fans stocked in the summer months to maximise profits.
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