PAU Applied Mathematics for Social Sciences II - Canary Islands / ULPGC & ULL study plan
PAU Applied Mathematics for Social Sciences II - Canary Islands / ULPGC & ULL · Give each week a clear focus.
8 weeks · About 6 hours per week
Matrices and systems of linear equations: matrix operations, rank, determinants, Gaussian elimination, Rouché–Capelli discussion, Cramer's rule, matrix equations
Compute sums, products, transposes and powers of 2×2 and 3×3 matrices by hand and state when a product is defined
Calculate determinants of order 2 and 3 using cofactor expansion and apply the properties of determinants to simplify before computing
Determine the rank of a matrix by row reduction and identify inconsistent or dependent rows at a glance
Solve square systems with Cramer's rule and check the answer by substitution
Your checkpoint
A one-page handwritten summary sheet with 12 solved exercises (3 per goal) covering matrix products, 3×3 determinants, rank and Cramer's rule, each with a final verified answer.
Matrices and systems of linear equations: matrix operations, rank, determinants, Gaussian elimination, Rouché–Capelli discussion, Cramer's rule, matrix equations
Apply Gaussian elimination to classify a system as compatible determined, compatible indeterminate or incompatible
State and use the Rouché–Capelli theorem comparing rank of the coefficient matrix with rank of the augmented matrix
Solve matrix equations of the form AX = B and XA = B by inverting A and justify when the inverse exists
Discuss one-parameter systems and express the solution set in terms of the free parameter
Your checkpoint
A written solutions set of 8 systems, at least 3 of them depending on a parameter, each with an explicit Rouché–Capelli discussion and the full solution set written out.
Linear programming: modelling with inequalities, feasible region, vertices, graphical and algebraic optimisation, duality and sensitivity in two-variable problems
Translate a word problem in two variables into a system of linear inequalities and identify the objective function
Draw the feasible region accurately, label every vertex and detect unbounded or empty regions
Evaluate the objective function at the vertices and apply the fundamental theorem of linear programming to locate the optimum
Recognise integer or excluded-boundary constraints and adjust the vertex search accordingly
Your checkpoint
A graphical portfolio of 6 complete linear programming problems on squared paper, each with the feasible region shaded, all vertices labelled with coordinates and the optimal value stated with justification.
Linear programming: modelling with inequalities, feasible region, vertices, graphical and algebraic optimisation, duality and sensitivity in two-variable problems & Limits, continuity and differentiation: limits and indeterminacies, asymptotic behaviour, continuity, derivative rules, tangent line, rates of change
Solve two-variable linear programming problems algebraically by intersecting constraint lines instead of relying on the drawing
Interpret the dual problem and the shadow price of a binding constraint in a small two-variable model
Analyse how the optimal vertex changes when one coefficient of the objective function varies, and state the range of optimality
Compute limits of rational and piecewise functions at a point, resolving 0/0 and ∞/∞ indeterminacies
Your checkpoint
A timed 60-minute mock section with 2 full linear programming problems (including one sensitivity question) and 4 limit computations, self-marked against a written rubric with errors logged.
Limits, continuity and differentiation: limits and indeterminacies, asymptotic behaviour, continuity, derivative rules, tangent line, rates of change
Resolve the indeterminacies 0/0, ∞/∞, ∞−∞ and 0·∞ using factorisation, conjugates, common denominators and L'Hôpital's rule
Determine asymptotic behaviour, horizontal and vertical asymptotes and one-sided limits at the boundary of the domain
Decide continuity of a function at a point and find the value of a parameter that makes a piecewise function continuous
Differentiate using the product, quotient, chain and implicit rules, and write the tangent line at a given point
Your checkpoint
A formula-and-technique card listing each indeterminacy with one worked example, plus 10 differentiation and tangent-line exercises solved end to end.
Applications of the derivative: monotonicity, extrema, concavity and convexity, inflection points, curve sketching, optimisation of one-variable economic functions
Find intervals of increase and decrease from the sign of the first derivative and classify extrema
Determine concavity, convexity and inflection points from the sign of the second derivative
Sketch a complete curve from its domain, asymptotes, monotonicity and concavity without plotting software
Optimise one-variable economic functions such as cost, revenue and profit, and interpret the marginal values at the optimum
Your checkpoint
Two full curve sketches with a written analysis table (domain, asymptotes, first-derivative sign chart, second-derivative sign chart) plus one solved profit-maximisation word problem.
Integration: antiderivatives, definite integrals, Barrow's rule, area between curves, accumulation and applied interpretation & Probability: sample space, combinatorics, conditional probability, independence, total probability, Bayes' theorem
Compute immediate integrals of elementary functions and apply integration by substitution and by parts where required
Apply Barrow's rule to evaluate definite integrals and compute the area between two curves, including finding the intersection points
Solve counting problems using permutations, combinations and the multiplication principle
Apply conditional probability, independence, the total probability theorem and Bayes' theorem to a stated problem
Your checkpoint
A mixed problem set of 10 exercises (6 on integration and areas, 4 on probability including one full Bayes tree diagram) solved with all intermediate steps shown.
Random variables and distributions: discrete and continuous variables, expectation and variance, binomial and normal distributions, approximation and standardisation & Probability: sample space, combinatorics, conditional probability, independence, total probability, Bayes' theorem
Distinguish discrete from continuous random variables and compute expectation and variance from a table or a density function
Compute binomial probabilities and identify when the binomial model applies
Standardise a normal variable, use the standard normal table and compute probabilities for intervals and inverse problems
Decide when the normal approximation to the binomial is valid and apply the continuity correction
Your checkpoint
A full timed mock exam of 6 problems spanning all seven domains, marked against a score breakdown per domain, with a written list of the three weakest areas and the corrections made.
Milestones
Week 2: Solve a parameter-dependent 3×3 system unaided, correctly stating the Rouché–Capelli discussion and writing the solution set in under 15 minutes.
Week 4: Score at least 70% on the timed mock section covering linear programming and limits, with no arithmetic errors in the vertex coordinates.
Week 6: Produce a complete curve sketch with correct monotonicity and concavity intervals and solve one economic optimisation problem without consulting notes.
Week 8: Score at least 60% overall on the full mock exam with no domain below 40%, and have every logged error rewritten correctly.