Introduction to Data Science

Real Life Problem Solving using Data Science

Twelve real world problems to introduce you to the world of data science. Every problem takes you through six stages, from an untidy spreadsheet to a final report you can present.

  • 1 Understanding
  • 2 Import
  • 3 Clean
  • 4 Program
  • 5 Interpret
  • 6 Report

Open your assigned problem below. Each problem stands on its own — work through the six stages in order, and record every decision you make along the way.

Problem 1

Effort and Output Correlation

Are efforts and output correlated? Weekly study hours against unit test marks.

CorrelationScatter plotLinear regression
Start with problem understanding Problem 2

Demand Prediction

How many samosas should the canteen prepare tomorrow?

Weekday averagesLinear regressionComparing methods
Start with problem understanding Problem 3

Churn Prediction

Which mobile customers are about to leave their operator?

Comparing two meansBar chartKNN
Start with problem understanding Problem 4

Species Classification

Can a species be identified from four measurements alone?

Separation scoreKNNChoosing k
Start with problem understanding Problem 5

Pollution Analysis

How bad is the air across four cities, and what makes it bad?

GroupingLine chartKNN
Start with problem understanding Problem 6

Price Prediction

What is a used two-wheeler actually worth?

Box plotMultiple regressionEncoding text
Start with problem understanding Problem 7

Feedback Classification

Can positive and negative feedback be sorted automatically?

Word frequenciesKNNReading the errors
Start with problem understanding Problem 8

Student Profiling

Which stream suits a student, and what can a model really say?

Correlation heatmapKNNLimits of a model
Start with problem understanding Problem 9

Population Prediction

What will a district's population be in 2036?

Growth ratesLinear regressionLog model
Start with problem understanding Problem 10

Population Estimation by Sampling

Can you estimate a ward's population without counting everyone?

Random samplingHistogramSample size
Start with problem understanding Problem 11

Rain Prediction

How do two cities compare on rainfall reliability in July?

ProbabilityBinomial distributionTesting an assumption
Start with problem understanding Problem 12

Yield Prediction

Can crop yield be predicted from rainfall and fertiliser?

CorrelationMultiple regressionTrain and test split
Start with problem understanding