 BMA

19/08/2013 17:32

I. Numbers (6 lectures)

• Prime numbers
• Interesting properties of prime numbers without proofs
• Ramanujan's work on the Prime Number Theorem
• Euclid's division algorithm
• Mathematical illustration through intuitive examples
• Visualisation through tiling analogy
• Encryption and Prime numbers
• Gentle introduction of 2 x 2 matrices
Constructing the RSA Algorithm Project Themes

• Ramanujan's work
• Implementations of RSA algorithms
• Other methods of encryption II. Data and patterns (6 lectures)

• Historical perspective and importance of data
• Kepler's law for planetary orbits from Tycho Brahe's astronomical observation
• Ramanujan's work on Prime numbers through data
• Data collection techniques
• Formulation of problem – goals, targets
• Methods to collect data – questionnaire, observations, recording, etc.
• How much of data is enough for the given problem
• Population and sample Project Themes

• Collecting and organising data in various situations through practical methods, from the
internet and from other sources.
• Use of spreadsheets for practical work related to the above concepts. III. Statistics (8 lectures)

• Organisation of data
• Frequency table
• Grouping
• Visualisation of data
• Pictorially displaying data: dot plots, bar graphs, line graphs, pie charts
• Misinterpretation of data by distorting the figures: Scaling and axis manipulation,
Line graphs and cropping
• Analysis of data
• Mean, median, mode, variance, standard deviation.
• Histogram, skewed distribution
• Comparing two distributions Project Themes

• Statistical analysis of daily life data
• Statistical analysis of stock market data
• Statistical analysis of weather data
• Statistical analysis of data for better governance IV. Probability (4 lectures)

• Interpreting probability, Sample Space, Events
• Understanding the tossing of a coin and throwing of dice for large number of trials Probability in a situation where there are equally-likely outcomes
• Probability of two independent events V. Project Themes

• Compute probabilities from insufficient information
• Validity of computed probability
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