Learn Python, SageMath, Matlab, Maple, Stata and R for mathematics and statistical analysis by taking advantage of our online self study program for only KES 7,250 ($72.5). Read more
Numerical analysis and modelling for scientists and engineers
Numerical analysis is a multidisciplinary subject which is recognized as an integral part in many areas including: mathematics, computer science, physics, commerce, engineering, biology, e.t.c.
Participants will get a comprehensive introduction to either Python or SageMath after which they will use either of these programming languages to find numerical solutions to applied scientific problems. While analytic/symbolic methods will not be our main focus, we will nonetheless use the Python Sympy library or SageMath Symbolic Toolbox to find analytic solutions to symbolic expressions.
This numerical analysis course targets students and professionals in mathematics, engineering, finance, economics, biology amnong others. Those who have an interest in mathematical modelling and simulation are highly encouraged to apply.
Dates 
Monthly: Starts every Third Monday Duration: 2 week (10 days) 

Time  0830Hrs  1630Hrs EAT (Mon  Fri)  
Delivery  Classroom (online also available)  
Charges  KES 125,500 ($1,250) 
The course can be customized to fit your specific needs in terms of course content, date, time and venue (including online sessions). Please talk to us for the customization.
 Getting started with Python
 About Python
 Python Editors
 Python Modules
 Structure of Python commands
 Creating variables and arrays
 Working with strings
 Input and output functions
 Accessing Python help
 Arithmetic operations and builtin functions
 Real Numbers
 Complex Numbers
 Lists
 Tuples
 Round functions
 Mathematical functions
 Tuples
 Python functions and program control flow
 Introduction
 Scripts
 User defined functions
 Program control flow
 Vectors and matrices with NumPy library
 Introduction
 Creating scalars and arrays
 Sequences
 Subscripting arrays
 Special matrices
 Restructuring matrices
 Operations on matrices
 Symbolic mathematics with SymPy library
 Introduction
 Polynomials and function simplification
 Solutions of equations
 Limits
 Series expansion
 Series summation
 Symbolic operations on matrices
 Differentiation and Integration
 Ordinary differential equations
 Transforms
 Vector differential calculus
 Vector integral calculus
 Vectors and matrices with Numpy library
 Introduction to plotting with Matplotlib library
 Introduction
 The plot and fplot functions
 Titles and axes labels (x and y)
 Creating multiple graphs
 Adding annotations / text to graphs
 X and Y axes properties
 Additional options
 Direct solutions of linear systems of equations
 Introduction
 Elementary row operations
 Elementary row operation applications
 LU factorization
 Solutions of linear systems with Python builtin functions
 Iterative and conjugate gradient methods
 Introduction
 Vector norms
 Matrix norms
 Iterative techniques
 Conjugate gradient methods
 Equations with multiple roots
 Introduction
 Closed domain methods
 Open domain methods
 Other methods
 Equations with multiple roots
 Solutions of systems of nonlinear equations
 Introduction
 Fixed point iteration
 Newtonâ€™s method
 QuasiNewton methods: Broyden method
 Steepest gradient techniques: Steepest descent
 Homotopy and continuation methods: Continuation algorithm
 Numerical differentiation
 Introduction
 Direct polynomial fit
 Newton difference methods
 Three point formulas
 Five point formulas
 Richardson extrapolation
 Second derivative midpoint formula
 Numerical integration
 Introduction
 Direct polynomial fit
 NewtonCotes formulas
 Composite rules
 Romberg integration
 Gaussian quadrature
 Double integration
 Curve fitting and interpolation
 Introduction
 Least square regression
 Linearizing nonlinear data
 Polynomial interpolation
 Interpolation using Splines
 Initial value problems
 Introduction
 Single step methods
 Adaptive RungeKutta methods
 Multistep methods
 Predictorcorrector methods
 Extrapolation method
 Systems of ordinary differential equations
 Higher order ordinary differential equations
 Boundary value problems
 Introduction
 Shooting method
 Finite difference method
 RayleighRitz method