# Course Identification

Introduction to Matlab and data analysis
20203031

## Lecturers and Teaching Assistants

Dr. Natalie Kronik
Dr. Ayelet Sarel, Dr. Yitzhak Norman, Dr. Tamir Eliav, Dr. Aharon Ravia

## Course Schedule and Location

2020
First Semester
Thursday, 09:15 - 11:00, FGS, Rm C

Tutorials
Monday, 09:15 - 11:00, FGS, Rm B
Wednesday, 09:15 - 11:00, FGS, Rm B
07/11/2019

## Field of Study, Course Type and Credit Points

Life Sciences: Lecture; Elective; Regular; 3.00 points
Chemical Sciences: Lecture; Elective; Regular; 2.00 points
Life Sciences (Molecular and Cellular Neuroscience Track): Lecture; Elective; Regular; 3.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Lecture; Elective; Regular; 3.00 points
Life Sciences (Computational and Systems Biology Track): Lecture; Elective; Core; 3.00 points

Office Hours on Monday 11:00-12:00 at FGS, Room B

*No tutorial on December 11th,2019.
*No tutorial on January 29th, 2020.

Make up tutorials:
November 28th, 2019 11:15-13:00, FGS Room B.
December 8th, 2019 09:15-11:00, FGS Room B.
December 26th, 2019 11:15-13:00, FGS Room B.
January 30th, 2020 11:15-13:00, FGS Room B.

No

100

English

## Attendance and participation

Expected and Recommended

Pass / Fail

60%
40%
Final project

Final assignment

N/A
N/A
-
N/A

3

## Syllabus

The course will teach how MATLAB can be used to solve practical problems in data analysis that come from a wide range of disciplines such as biology, chemistry and physics. The first part of the course focuses on the foundations of Matlab programming:

1. The Matlab working environment
2. Variables, constants and reserved words
3. Arrays and matrices
4. Scripts
5. The debugger
6. Generating 2D and 3D Graphics
7. Simple statistical analysis
8. String manipulation
9. Boolean logic and if statements
10. Loops (while, for)
11. Functions & Files
12. Program design
13. Matlab structures
14. Complexity
15. Producing publication quality graphs

The second part of the course focuses on applying Matlab to practical problems in
data analysis:

1. Mathematical modeling of cancer therapy using the Matlab ODE solvers
2. Systems biology: protein production gene expression
3. Analyzing images using the Matlab image processing toolbox

========================

SYLLABUS WEEK BY WEEK:

[1] Introduction

a. Why learn Matlab?
b. The Matlab working environment
c. The "help" command
d. Our first Matlab program.

[2] Basics

a. Variables, constants and reserved words
b. Arrays and matrices
c. Scripts
d. The debugger

[3] Graphics and simple analysis

a. 2D Graphics
b. simple statistical analysis (mean, std etc)
c. String manipulation

[4] Control

a. Boolean logic
b. If statements

[5] Loops

a. while
b. for

[6] Functions & Files

[7] Program design

a. Matlab structures
b. Top down, bottom up, etc
c. Complexity

[8] Making simple GUIs

[9] Matlab image processing toolbox

[10] Matlab Bioinformatics toolbox

[11] Solving differential equations numerically using Matlab

[12] & [13] Introduction of some advanced topics in how to prepare Graphs using Matlab:

a. Preparing publication-quality figures in Matlab
b. How to make Movies of data in Matlab
c. Tricks for extracting raw data from old published graphs
d. Advanced topics in 2-D and 3-D graphs: efficient ways of making graphs; more about Handle Graphics; Latex formatting of text embedded in graphs

[14] Mathematical modeling of ordinary differential equations in Matlab.

[15] Basic image processing with Matlab

## Learning Outcomes

Upon successful completion of this course students should be be able to:

1. Demonstrate basic skills in Matlab programming, using 'if' and 'while' loops, using Boolean logic and flow control, creating functions and designing programs in Matlab.
2. Use Matlab cell arrays.
3. Produce publication quality graphs in Matlab.
4. Perform numerical analysis using Matlab.