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2a: Define data science
| Keyword | Definition |
|---|
| data | binary 0s and 1s used to represent files, text, images, sound or video |
| data science | extracting meaning from large data sets in order to gain insights to support decision making |
| information | data that has been processed to give it some meaning |
| insight | information which comes from interpreting data and putting it in context |
| large data set | huge 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
| Keyword | Definition |
|---|
| data | facts and statistics collected together for reference or analysis |
| insight | understanding something better by analysing data |
| trends | patterns in data that can help make predictions in the future |
| visualisation | a 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
| Keyword | Definition |
|---|
| chart | graphs which visualise data to make it easier to understand |
| database | software used to store lots of data which has been structured in a way that makes it easy to search and use |
| software | programs that run on a computer |
| spreadsheet | software used to analyse data and create graphs |
| trend | a pattern in data |
| visualise | to represent graphically (e.g. with a chart or infographic) |
2d: Recognise examples of where large data sets are used in daily life
| Keyword | Definition |
|---|
| census | data gathered by the government about each person in the country which is used to plan public services like schools and hospital |
| exam results | your estimated GCSE grades are calculated by analysing data gathered from students who are statistically similar to you |
| personalised advertising | data 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 media | data 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
| Keyword | Definition |
|---|
| conclusion | facts that you can prove from your analysis of data |
| criteria | conditions used to filter a data set so you can focus on a smaller quantity of data |
| data set | a large quantity of data which can be analysed |
| prediction | estimating what an investigation will find before you have analysed the data |
2f: Evaluate findings to support arguments for or against a prediction
| Keyword | Definition |
|---|
| arguments | opinions, suggestions or ideas that you are investigating |
| conclusion | facts that you can prove from your data analysis |
| evaluate | explain both sides of a debate in detail |
| findings | the patterns and trends you identify from analysing data |
| prediction | estimate made before you start analysing data |
2g: Define the terms 'correlation' and 'outliers' in relation to data trends
| Keyword | Definition |
|---|
| causation | prooving how changing one value causes another value to change |
| correlation | The relationship between two or more variables |
| data trend | a pattern identified in a data set |
| negative correlation | a trend identified in data where you see one value decrease wherever you see another value increase |
| outlier | a value in a data set which doesn't follow the same trend as most other values |
| positive correlation | a trend identified in data where you see one value increase wherever you see another value increase |
2h: Identify the steps of the investigative cycle
| Keyword | Definition |
|---|
| Analysis | Step 4 of the investigative cycle where you visualise the data to spot any patterns, trends, correlations or outliers |
| Conclusions | Step 5 of the investigative cycle where you are now able to use your data analysis to answer your research question |
| Data | Step 3 of the investigative cycle where you gather the data and cleanse it to remove anything inaccurate |
| Investigative cycle | Four step process that you can use in data science to go from predictions to conclusions |
| Plan | Step 2 of the investigative cycle where you predict an answer to the question and make a plan to collect or access the data |
| Problem | Step 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
| Keyword | Definition |
|---|
| Analyse | Third step of the investigative cycle where you create graphs or visualisations which show trends in the data |
| Collect | Second step of the investigative cycle where you gather data related to the question you're trying to answer |
| Interpret | Final step of the investigative cycle where you draw conclusions from data which help you ask better questions to continue your research |
| Investigative cycle | Four step process that you can use in data science to go from predictions to conclusions |
| Pose | First step of the investigative cycle were you describe the question that you're trying to answer |
2j: Use findings to support a recommendation
| Keyword | Definition |
|---|
| data | facts and statistics collected together for reference or analysis |
| findings | conclusion that you have come up with after analysing data and interpreting it |
| prediction | opinion or estimate which people have before analysing the data to come to a conclusion |
| recommendation | suggestion 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
| Keyword | Definition |
|---|
| data | facts and statistics collected together for reference or analysis |
| identify | to spot, write down or define |
| learner | the person doing the research or data analysis |
| question | something that can be investigated to go from predictions and opinions to conclusions and facts |
2l: Create a data capture form
| Keyword | Definition |
|---|
| data capture form | something that people can fill in so that you can collect data to be analysed |
| database | how data might be stored on a computer |
| field | a single item that someone might fill in on a data capture form |
| validation | checking data that a user enters on a form to make sure it is valid |
2m: Describe the need for data cleansing
| Keyword | Definition |
|---|
| data cleansing | checking data that has been collected to make sure that it is correct, complete and relevant |
| duplicated data | data which has been entered more than once by mistake |
| incomplete data | data which has been collected which may have parts missing |
| incorrect data | data which has been collected which contains mistakes |
| irrelevant data | data which isn't related to the question that was asked |
2n: Apply data cleansing techniques to a data set
| Keyword | Definition |
|---|
| data cleansing | checking data that has been collected to make sure that it is correct, complete and relevant |
| duplicated data | data which has been entered more than once by mistake |
| incomplete data | data which has been collected which may have parts missing |
| incorrect data | data which has been collected which contains mistakes |
| irrelevant data | data which isn't related to the question that was asked |
2o: Visualise a data set
| Keyword | Definition |
|---|
| data set | a large collection of data |
| histogram | a type of visualisation that groups data points into specific ranges |
| pie chart | a type of visualisation where a circle is split up into sections showing the proportions of different groups of data rather |
| scatter graph | a 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 |
| visualisation | a way of graphically presenting data to make it easier to spot patterns and trends |
2p: Analyse visualisations to identify patterns, trends, and outliers
| Keyword | Definition |
|---|
| outlier | a data point that doesn't fit the general pattern or trend |
| pattern | a repeating sequence or shape that can be seen when you visualise a data set |
| trend | the direction that data seems to be heading in when you visualise a data set |
| visualisation | a chart or graph which displays data graphically to make it easier to spot patterns and trends |
2q: Draw conclusions and report findings
| Keyword | Definition |
|---|
| conclusion | summary of what you have discovered or proved as a result of your data analysis |
| plagiarism | using someone else's research or work without correctly referencing it so that people might think that it's your work instead |
| reference | giving credit to someone else's research or work when you use it in your report |
| report | document which contains a description of what you investigated, how you gathered your data, how it was analysed and a summary of your conclusions |
