Bwriad y gweithgaredd hwn yw cyflwyno dysgwyr i'r syniad o wyddor data a sut mae'n dilyn yn naturiol o'r sgiliau dadansoddi maen nhw eisoes yn eu datblygu mewn gwersi gwyddoniaeth.

Cliciwch ar y botwm 'Nesaf' isod i gychwyn.

Yr Arbrawf

Mae grŵp o fyfyrwyr yn ymchwilio i faint o law sy'n disgyn yn eu hysgol nhw bob mis drwy gydol blwyddyn.

I wneud hyn, maen nhw wedi gosod tri mesurydd mewn gwahanol leoliadau ar dir yr ysgol. Maen nhw wedi dewis tri lleoliad i fesur glawiad cyfartalog ar draws yr ardal, ac ar gyfer ailadrodd er mwyn gwneud yr arbrawf yn fwy teg a chywir.

Mae'r canlyniadau yn cael eu casglu ar ddiwedd pob mis a'u cofnodi yn y tabl canlyniadau isod.

Glawiad misol (mewn milimetrau) o dri mesurydd

Mis	Mesurydd A	Mesurydd B	Mesurydd C	Cyfartaledd
Ionawr	142	138	145
Chwefror	108	112	110
Mawrth	95	90	100
Ebrill	78	82	80
Mai	74	70	76
Mehefin	88	84	86
Gorfennaf	92	68	95
Awst	115	108	112
Medi	128	135	130
Hydref	162	158	165
Tachwedd	170	175	168
Rhagfyr	155	150	160

Tasg 1:

Bydd angen i chi gopïo'r tabl canlyniadau a chwblhau'r golofn olaf drwy gyfrifo'r cyfartaledd cymedrig ar gyfer pob mis.

Tasg 2:

Gan ddefnyddio'r gwerthoedd cyfartalog ar gyfer pob mis, lluniwch graff priodol.

Ar ôl i chi orffen eich graff, cliciwch ar y botwm 'Nesaf'isod i ddechrau'r dadansoddiad.

Dadansoddiad

Wrth ysgrifennu arbrofion gwyddoniaeth, mae angen i chi ddilyn eich canlyniadau gyda dadansoddiad. Dyma lle rydyn ni'n ysgrifennu am y canlyniadau a'r hyn maen nhw'n ei ddangos i ni.

Tasg 3:

Ysgrifennwch baragraff sy'n esbonio'r canlyniadau. Gweler y rhestr isod am syniadau ynglŷn â'r hyn y gallech ei gynnwys.

• Pa fis oedd y gwlypaf?
• Pa fis oedd y sychaf?
• A oedd unrhyw ganlyniadau anarferol?
• A oedd y canlyniadau i'w disgwyl?
• Gawsoch chi eich synnu gan unrhyw un o'r canlyniadau?

Hyd yn hyn, dylai popeth fod yn gyfarwydd i chi - rydyn ni wedi bod yn dilyn fformat safonol adroddiad arbrawf gwyddonol. Bydd yr adran nesaf yn dechrau dangos i chi sut i roi egwyddorion gwyddor data ar waith gyda'r un wybodaeth.

Dechrau Cwestiynu

Gadewch i ni ddechrau ein taith tuag at feddwl fel gwyddonydd data drwy gwestiynu'r data.

Cliciwch ar unrhyw un o'r cwestiynau isod i ddysgu mwy am bwysigrwydd gofyn hyn.

1. O ble ddaeth y data?

   You've been provided with a basic description of the experiment. However, the method of collection/measuring is not specified. Consider how this may have affected the results.



2. Pam mae canlyniadau'r mesuryddion yn wahanol ar gyfer pob mis?

   The reason for repetition within an experiment is to take multiple measurements of the same thing to improve accuracy. It can also highlight anomalous results and highlight issues within the methodology.

   The data provided may seem similar - we can examine it more deeply by producing a comparison graph instead of only using the average.

   

   This graph shows that gauge 2's line does not match the others. Interestingly, this is also the gauge to produce both the lowest and highest reading.

   This is information we lose when we only analyse the average value - you may have picked up on the anomalous result for July on gauge 2 previously but it is clearer now.

   What sort of factors could answer why these gauge readings do not match more closely?



3. Pa newidynnau eraill allai chwarae rhan yn yr arbrawf?

   Think about how temperature and other weather conditions may affect the results. Also, think about how positioning of gauges related to buildings or trees may affect such readings too.



4. A allwn ni ehangu'r data sydd ar gael drwy fesur y ffactorau ychwanegol hyn?

   This is looking at data collection to confirm or eliminate the additional variables considered above.

   What would you measure alongside the rainfall gauges to include other factors?



5. Beth oedd y ddamcaniaeth?

   We do not know what the students who carried out this experiment were looking to prove. This may of introduced a form of bias to their techniques, location selection, and/or data collection.

   This is why an hypothesis is important when discussing data. Who and why they were doing the experiment may influence approach, expectations, and how the results are selected/represented.

   For example, if we wanted to know if bacon is safe to eat, should we use data provided by a butcher, the NHS, or a charity? How would each of these groups may influence (intentionally or not) those results and why?



6. A oes unrhyw duedd arall ar waith?

   Bias in science experiments refers to the incorrect, inaccurate and/or influenced methods used to collect data. With the information available for this experiment, we can consider these possible forms:

   • Location bias - where the gauges were placed
   • Measurement bias - how the measurements were taken
   • Environmental bias - the impact of surrounding structures on gauges
   • Temporal bias - the decision to take readings monthly
   • Human error/Recording bias - mis-reading of gauge measurements, errors in recording, misinterpretation if handwritten initially (ie. bad handwriting).
   • Selection bias - the decision to only measure rainfall.

Tasg 4:

Ar ôl ystyried yr holl gwestiynau uchod, ewch ati i greu dull newydd er mwyn rhoi cynnig ar eich fersiwn eich hun o'r arbrawf.

Mae gwyddor data yn fwy na dim ond cwestiynu data; cam cyntaf y broses yw hynny. Mae'r adran nesaf yn edrych ar greu rheolau a/neu fodelau ar sail data.

Creu Rheolau

Un o nodau gwyddor data yw defnyddio data i greu rheolau a modelau i gynorthwyo gyda rhagweld a dirnad.

Mae'r canlyniadau a gasglwyd yn yr arbrawf hwn yn gyfyngedig iawn - dim ond blwyddyn o bwyntiau data misol sydd ar gael, heb fesur unrhyw ffactorau/newidynnau eraill. Fodd bynnag, mae digon o ddata ar gael i greu rhai rheolau cyffredinol, megis:

• Mae llai o law yn y gwanwyn a'r haf nag yn yr hydref a'r gaeaf.
• Os yw'r glawiad cyfartalog ar gyfer mis yn llai na 100mm, mae'n fis rhwng dechrau Mawrth a diwedd Gorffennaf.

Nid yw'r cyfyngiadau data yn caniatáu rheolau manylach - ac mae'n rhaid peidio ag ystyried bod y rhai rydyn ni'n gallu eu dylunio yn gywir nes bod sawl blwyddyn arall o ddata wedi'i gasglu a'i wirio.

Tasg 5:

Allwch chi ysgrifennu rheol gan ddefnyddio'r data hwn? Drwy wrthdroi geiriad un o'r enghreifftiau uchod efallai, neu mae'n bosib eich bod chi wedi sylwi ar batrwm gwahanol.

Darogan y Dyfodol

Drwy greu rheolau a/neu fodelau, mae gwyddonwyr data yn gallu darogan.

Ond mae cywirdeb y darogan hwnnw yn dibynnu'n llwyr ar y data sydd ar gael.

Rydyn ni eisoes wedi edrych ar ba mor gyfyngedig yw'r set ddata sydd ar gael yma, a bod hynny'n gwneud unrhyw reolau, modelau a darogan yn annibynadwy.

Fodd bynnag, fel rhan o'r gweithgaredd hwn, byddwn yn defnyddio'r data sydd ar gael i ddarogan ychydig ynghylch glawiad yn ystod y flwyddyn nesaf yn yr ysgol.

Tasg 6:

Allwch chi ragweld pa dymor fydd y sychaf/gwlypaf?

Pa fis fydd yn gweld y lleiaf/mwyaf o law?

Diolch!

Gobeithio eich bod chi wedi mwynhau'r gweithgaredd hwn ac wedi dechrau cydnabod sut mae gwyddor data yn esblygiad o'r gwaith dadansoddi rydych chi eisoes yn ei wneud yn rheolaidd yn yr ystafell ddosbarth.

Os hoffech roi cynnig ar weithgaredd estynol ychwanegol cyn symud ymlaen, cliciwch ar y botwm 'Nesaf' isod. Fel arall, mae croeso i chi glicio ar y botwm tudalen gwyddor data ar waelod y sgrin i archwilio'r pwnc ymhellach.

Estyniad

Gan ddefnyddio popeth rydych chi wedi'i ddysgu drwy gwestiynu pob cam o'r arbrawf a'r data a ddarparwyd, allwch chi lunio eich fersiwn eich hun o'r arbrawf ar gyfer eich ysgol neu gymdogaeth? Rydyn ni wedi darparu rhestr wirio gyflym isod.

Rhestr wirio dylunio arbrawf

Cyflwyniad - disgrifiad byr o'r arbrawf.

Damcaniaeth - beth ydych chi'n ceisio ei gyflawni?

Dull - yr offer, y lleoliadau a sut mae'r arbrawf yn cael ei gynnal.

Canlyniadau - pa fesuriadau fydd yn cael eu cymryd, pryd a sut? Dylid ystyried dulliau cofnodi a chynllun tablau ar gyfer yr adran hon.

Dadansoddiad - sut fyddwch chi'n prosesu'r data i brofi/gwrthbrofi eich damcaniaeth? Pa ddarogan a/neu reolau ydych chi'n gobeithio gallu eu gwneud?

I gloi...

Tasg olaf yr ymarfer yn ei gyfanrwydd yw ysgrifennu gwerthusiad ar gyfer yr arbrawf gwreiddiol - gan ddefnyddio eich dadansoddiad a'ch fersiwn eich hun o'r arbrawf i'ch cynorthwyo.

This activity aims to introduce learners to the idea of data science and how it is a natural progression from analysis skills they are already developing in science lessons.

Click on the 'next' button below to get started.

The Experiment

A group of students are investigating how much rainfall their school gets each month throughout a year.

To do this, they have set up three gauges in different locations on the school grounds. They've chosen three locations to provide an average rainfall value across the area, and for repetition to increase fairness and accuracy.

The results are then collected at the end of each month and logged in the below results table.

Monthly rainfall (in millimetres) from three gauges

Month	Gauge A	Gauge B	Gauge C	Average
January	142	138	145
February	108	112	110
March	95	90	100
April	78	82	80
May	74	70	76
June	88	84	86
July	92	68	95
August	115	108	112
September	128	135	130
October	162	158	165
November	170	175	168
December	155	150	160

Task 1:

You will need to copy the results table and complete the last column by calculating the mean average for each month.

Task 2:

Using the average values for each month draw an appropriate graph.

Once you've finished your graph, click on the next button below to start the analysis.

Analysis

When writing up science experiments, you need to follow your results with an analysis. This is where we write about the results and what they show us.

Task 3:

Write a paragraph explaining the results. See the below list for ideas as to what you could include.

• Which month was the wettest?
• Which month was the driest?
• Were there any anomalous results
• Are the results expected?
• Have you been surprised by any of the results?

So far, everything should be familiar to you - we have been following the standard format of a science experiment report. The next section will start to show you how to apply data science principles to the same information.

Start Questioning

Let's begin our journey towards thinking like a data scientist by questioning the data.

Click on any of the questions below to find out more about why asking this is important.

1. Where has it come from?

   You've been provided with a basic description of the experiment. However, the method of collection/measuring is not specified. Consider how this may have affected the results.



2. Why are the gauge measurements different for each month?

   The reason for repetition within an experiment is to take multiple measurements of the same thing to improve accuracy. It can also highlight anomalous results and highlight issues within the methodology.

   The data provided may seem similar - we can examine it more deeply by producing a comparison graph instead of only using the average.

   

   This graph shows that gauge 2's line does not match the others. Interestingly, this is also the gauge to produce both the lowest and highest reading.

   This is information we lose when we only analyse the average value - you may have picked up on the anomalous result for July on gauge 2 previously but it is clearer now.

   What sort of factors could answer why these gauge readings do not match more closely?



3. What other variables may play a part in the experiment?

   Think about how temperature and other weather conditions may affect the results. Also, think about how positioning of gauges related to buildings or trees may affect such readings too.



4. Could we expand the data available by measuring these additional factors?

   This is looking at data collection to confirm or eliminate the additional variables considered above.

   What would you measure alongside the rainfall gauges to include other factors?



5. What was the hypothesis?

   We do not know what the students who carried out this experiment were looking to prove. This may of introduced a form of bias to their techniques, location selection, and/or data collection.

   This is why an hypothesis is important when discussing data. Who and why they were doing the experiment may influence approach, expectations, and how the results are selected/represented.

   For example, if we wanted to know if bacon is safe to eat, should we use data provided by a butcher, the NHS, or a charity? How would each of these groups may influence (intentionally or not) those results and why?



6. Any other bias present?

   Bias in science experiments refers to the incorrect, inaccurate and/or influenced methods used to collect data. With the information available for this experiment, we can consider these possible forms:

   • Location bias - where the gauges were placed
   • Measurement bias - how the measurements were taken
   • Environmental bias - the impact of surrounding structures on gauges
   • Temporal bias - the decision to take readings monthly
   • Human error/Recording bias - mis-reading of gauge measurements, errors in recording, misinterpretation if handwritten initially (ie. bad handwriting).
   • Selection bias - the decision to only measure rainfall.

Task 4:

Produce a new method for attempting your own version of this experiment after considering all the above questions.

Data science is not just about questioning data; it is just the first step in the progress. The next section looks at creating rules and/or models from data.

Creating Rules

One of the aims of data science is to take data to create rules and models to aid with predictions and understanding.

The results gathered in this experiment are very limited - we only have one year of monthly data points with no other factors/variables measured. However, there is still enough to make some general rules, such as:

• There is less rainfall in spring and summer than autumn and winter.
• If average rainfall is less than 100mm, it is a month between and including March and July.

The data limitations do not allow more detailed rules - and the ones we can design should not be taken as accurate until several more years' worth of data has been collected to check.

Task 5:

Can you write a rule using this data? Perhaps by reversing the wording of one of the above examples or, perhaps, you've spotted a different pattern.

Predicting the Future

By creating rules and/or models, data scientists can make predictions.

These predictions are only as accurate as the data available to them.

We've already looked at how limited the provided dataset here is, which makes any rules, models, and predictions un-reliable.

However, as part of this activity, we shall use the data we have to make some predictions for the next year's rainfall at the school.

Task 6:

Can you predict which season will be driest/wettest?

Which month will see the least/most rain?

Thank you!

We hope that you've enjoyed this activity and have started to recognise how data science is an evolution on the analysis you already do in the classroom regularly.

If you would like an additional extension activity before moving on, please click on the next button below. Otherwise, feel free to click on the data science page button at the very bottom of the screen to explore the subject further.

Extension

Using everything you've learned through questioning each stage of the experiment and data provided, can you write your own version of the experiment for your school or neighbourhood? We have provided a quick checklist below.

Experiment design checklist

Introduction - A brief description of the experiment.

Hypothesis - What are you trying to prove?

Method - The equipment, the locations, and how the experiment will be carried out.

Results - What measurements will be taken, when, and how? Recording methods and table design should be considered for this section.

Analysis - How will you process this data to prove/disprove your hypothesis? What predictions and/or rules do you hope to be able to make?

Finally...

The ultimate task for this entire exercise, is to write an evaluation for the original experiment - using your analysis and experiment re-design to assist.