For details of standard term assessment timelines and exam structures, visit our Academics page.
WEEK 1
Introduction and type of data, Types of data, Descriptive and Inferential
statistics, Scales of measurement
WEEK 2
Describing categorical data
Frequency distribution of
categorical data, Best practices for
graphing categorical data, Mode and median for
categorical variable
WEEK 3
Describing numerical data
Frequency tables for numerical data, Measures of central tendency - Mean, median and mode, Quartiles and percentiles, Measures of dispersion - Range, variance, standard deviation and IQR, Five number summary
WEEK 4
Association between two variables -
Association between two categorical variables - Using relative frequencies in contingency tables, Association between two numerical variables - Scatterplot, covariance, Pearson correlation coefficient, Point bi-serial correlation coefficient
WEEK 5
Basic principles of counting and factorial concepts -
Addition rule of counting, Multiplication rule of
counting, Factorials
WEEK 6
Permutations and combinations
WEEK 7
Probability
Basic definitions of
probability, Events, Properties of probability
WEEK 8
Conditional probability -
Multiplication rule, Independence, Law of total probability, Bayes’ theorem
WEEK 9
Random Variables -
Random experiment, sample space and random variable, Discrete and continuous random variable, Probability mass function, Cumulative density function
WEEK 10
Expectation and Variance -
Expectation of a discrete random variable, Variance and standard deviation of a discrete random variable
WEEK 11
Binomial and poisson random variables -
Bernoulli trials, Independent and identically distributed random variable, Binomial random variable, Expectation and variance of abinomial random variable, Poisson distribution
WEEK 12
Introduction to continous random variables -
Area under the curve, Properties of pdf, Uniform distribution, Exponential distribution
