Y9

HT2: Data Science

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2a: Define data science
Data Science
KeywordDefinition
databinary 0s and 1s used to represent files, text, images, sound or video
data scienceextracting meaning from large data sets in order to gain insights to support decision making
informationdata that has been processed to give it some meaning
insightinformation which comes from interpreting data and putting it in context
large data sethuge quantity of data such as Netflix viewing history or Amazon previous purchases
2b: Explain how visualising data can help identify patterns and trends in order to help us gain insights
Data Science
KeywordDefinition
datafacts and statistics collected together for reference or analysis
insightunderstanding something better by analysing data
trendspatterns in data that can help make predictions in the future
visualisationa way of displaying data that makes it easier to understand
2c: Use an appropriate software tool to visualise data sets and look for patterns or trends
Data Science
KeywordDefinition
chartgraphs which visualise data to make it easier to understand
databasesoftware used to store lots of data which has been structured in a way that makes it easy to search and use
softwareprograms that run on a computer
spreadsheetsoftware used to analyse data and create graphs
trenda pattern in data
visualiseto represent graphically (e.g. with a chart or infographic)
2d: Recognise examples of where large data sets are used in daily life
Data Science
KeywordDefinition
censusdata gathered by the government about each person in the country which is used to plan public services like schools and hospital
exam resultsyour estimated GCSE grades are calculated by analysing data gathered from students who are statistically similar to you
personalised advertisingdata from your web browsing history which is then used to show you adverts based on the sorts of things you might like to buy
social mediadata about your friendships, habits, likes and dislikes which is used to suggest content you might enjoy
2e: Select criteria and use data sets to investigate predictions
Data Science
KeywordDefinition
conclusionfacts that you can prove from your analysis of data
criteriaconditions used to filter a data set so you can focus on a smaller quantity of data
data seta large quantity of data which can be analysed
predictionestimating what an investigation will find before you have analysed the data
2f: Evaluate findings to support arguments for or against a prediction
Data Science
KeywordDefinition
argumentsopinions, suggestions or ideas that you are investigating
conclusionfacts that you can prove from your data analysis
evaluateexplain both sides of a debate in detail
findingsthe patterns and trends you identify from analysing data
predictionestimate made before you start analysing data
2g: Define the terms 'correlation' and 'outliers' in relation to data trends
Data Science
KeywordDefinition
causationprooving how changing one value causes another value to change
correlationThe relationship between two or more variables
data trenda pattern identified in a data set
negative correlationa trend identified in data where you see one value decrease wherever you see another value increase
outliera value in a data set which doesn't follow the same trend as most other values
positive correlationa trend identified in data where you see one value increase wherever you see another value increase
2h: Identify the steps of the investigative cycle
Data Science
KeywordDefinition
AnalysisStep 4 of the investigative cycle where you visualise the data to spot any patterns, trends, correlations or outliers
ConclusionsStep 5 of the investigative cycle where you are now able to use your data analysis to answer your research question
DataStep 3 of the investigative cycle where you gather the data and cleanse it to remove anything inaccurate
Investigative cycleFour step process that you can use in data science to go from predictions to conclusions
PlanStep 2 of the investigative cycle where you predict an answer to the question and make a plan to collect or access the data
ProblemStep 1 of the investigative cycle where you define the problem that needs to be solved and pose questions that can be investigated
2i: Solve a problem by implementing steps of the investigative cycle on a data set
Data Science
KeywordDefinition
AnalyseThird step of the investigative cycle where you create graphs or visualisations which show trends in the data
CollectSecond step of the investigative cycle where you gather data related to the question you're trying to answer
InterpretFinal step of the investigative cycle where you draw conclusions from data which help you ask better questions to continue your research
Investigative cycleFour step process that you can use in data science to go from predictions to conclusions
PoseFirst step of the investigative cycle were you describe the question that you're trying to answer
2j: Use findings to support a recommendation
Data Science
KeywordDefinition
datafacts and statistics collected together for reference or analysis
findingsconclusion that you have come up with after analysing data and interpreting it
predictionopinion or estimate which people have before analysing the data to come to a conclusion
recommendationsuggestion that you can make which is backed up with evidence from your analysis of the data
2k: Identify the data needed to answer a question defined by the learner
Data Science
KeywordDefinition
datafacts and statistics collected together for reference or analysis
identifyto spot, write down or define
learnerthe person doing the research or data analysis
questionsomething that can be investigated to go from predictions and opinions to conclusions and facts
2l: Create a data capture form
Data Science
KeywordDefinition
data capture formsomething that people can fill in so that you can collect data to be analysed
databasehow data might be stored on a computer
fielda single item that someone might fill in on a data capture form
validationchecking data that a user enters on a form to make sure it is valid
2m: Describe the need for data cleansing
Data Science
KeywordDefinition
data cleansingchecking data that has been collected to make sure that it is correct, complete and relevant
duplicated datadata which has been entered more than once by mistake
incomplete datadata which has been collected which may have parts missing
incorrect datadata which has been collected which contains mistakes
irrelevant datadata which isn't related to the question that was asked
2n: Apply data cleansing techniques to a data set
Data Science
KeywordDefinition
data cleansingchecking data that has been collected to make sure that it is correct, complete and relevant
duplicated datadata which has been entered more than once by mistake
incomplete datadata which has been collected which may have parts missing
incorrect datadata which has been collected which contains mistakes
irrelevant datadata which isn't related to the question that was asked
2o: Visualise a data set
Data Science
KeywordDefinition
data seta large collection of data
histograma type of visualisation that groups data points into specific ranges
pie charta type of visualisation where a circle is split up into sections showing the proportions of different groups of data rather
scatter grapha type of visualisation where two values in a data set are plotted against each other on X-Y axis to help spot a pattern or trend
visualisationa way of graphically presenting data to make it easier to spot patterns and trends
2p: Analyse visualisations to identify patterns, trends, and outliers
Data Science
KeywordDefinition
outliera data point that doesn't fit the general pattern or trend
patterna repeating sequence or shape that can be seen when you visualise a data set
trendthe direction that data seems to be heading in when you visualise a data set
visualisationa chart or graph which displays data graphically to make it easier to spot patterns and trends
2q: Draw conclusions and report findings
Data Science
KeywordDefinition
conclusionsummary of what you have discovered or proved as a result of your data analysis
plagiarismusing someone else's research or work without correctly referencing it so that people might think that it's your work instead
referencegiving credit to someone else's research or work when you use it in your report
reportdocument which contains a description of what you investigated, how you gathered your data, how it was analysed and a summary of your conclusions