Data Analytics with AI

Data Analytics with AI

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New lesson editorMathematicsFurther Education (Key Stage 5)

This lesson contains 19 slides, with interactive quiz and text slides.

Items in this lesson

Data Analytics with AI

Session ILO's

As Individuals you will be able to review data to comment on at least 5 properties



Use one new feature to analysis date or present results


Use Co Pilot to Review data and compare the results



What is data analytics

Data analytics is a systematic approach to transforming raw data into valuable insights. It involves the collection, transformation, and organization of data to draw conclusions, make predictions, and drive informed decision-making.”


This may involve

Finding Averages, Ranges, Standard deviation

Algorithms can be used alongside standard statistical terms to find patterns and trends

Predictions and Conclusions

  • Need to be evidence based and staff have the context to know where to look and find supporting evidence.


  • Statistics might point towards things but you must verify and critically review conclusions to ensure they are robust.


  • Users are accountable of decision not AI so don’t assume conclusions and calculations are correct.

What can we do in the college

  • Cleaning data (Co Pilot License required)

  • Sorting data (Co Pilot License required)

  • Assisting analytics- Pivot charts and Tables (in Excel)


  • Uploaded data must be anonymised, never use personal data ie Date of birth address etc


  • Don’t upload college data outside Co Pilot on college devices.

Statistics Recap

Which term to use

Mean

  • Can be average or xbar

  • Add all terms together

  • Divide by the number of terms


Range

  • Maximum- Minimum


Mode

  • Most Common value


Median

  • Middle Value

Standard Deviation

  • Also known as Variance


  • Measures spread of data by measuring distance from the average


  • Big Variance = Big Spread



Skew



  • If mean > median → positive skew

  • If mean < median → negative skew

  • If mean ≈ median → symmetric distribution

Correlation

Correlation in statistics is a measure that describes the strength and direction of a relationship between two numerical variables


  • Positive or Negative

  • Strong or weak


  • Measured as between 0 and 1

Regression

  • Regression (or regression analysis) is a statistical method used to model and predict the relationship between variables

Regression will draw a liner of best fit and may allow you to make predictions

  • Linear ie y=mx+c

  • Quadratic y=x2+5x+6

  • Polynomial y=4x4+5x2+6

  • Exponential y=e4-10

  • Logarithmic y= logn 10

What is you experience with pivot tables

1

I am happy creating them

2

i found them to difficult

3

No idea or I don't need them

4

Know what they are but I don't use them

AI for training in the Data analytics field

  • Data analysis add in for excel

  • Produce Pivot Tables and Pivot Charts





  • Show regression and can create lines of data sets to explain or highlight relationships

Statistics Glossary

  • Average (Mean): sum of values divided by number of values.

  • Range: max value minus min value.

  • Standard Deviation (σ): typical distance from the mean.

  • Correlation (r): strength & direction of linear association (−1 to 1).

  • Linear Regression: best‑fit line predicting y from x.


Once you have reviewed the tables and charts. Add a comment on any unusual data. Try and reference the Terms discussed

1

minute

00

second

AI in Excel



Select the %min missed and Attendance%

  • Insert a Chart

  • Select XY scatter

  • Select Add chart element

  • Add a trendline


Instructions

Click on the Student Page and select Analyse Data Button


Review performance by Campus to compare Outcomes Between Campus


You can Review existing Pivot Tables

Co Pilot premium features

  • Available for premium users on the web version

Activity

AI reviewing Data


Upload the data into Co Pilot and then ask it question.

  1. What will you include in your prompt to find points of interest

  2. What does Co Pilot say about Age vs Attendance for the data