Transcript of SCHOOL OF NATURAL AND APPLIED SCIENCES …
PROGRAMME RULES AND INFORMATION
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SCHOOL OF NATURAL AND APPLIED SCIENCES
PROGRAMME RULES AND INFORMATION
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TABLE OF CONTENTS 1. Contact Details
.....................................................................................................1
2. Staff Information
...................................................................................................2
3. General Rules
......................................................................................................5
4. Programmes offered
..........................................................................................
17 4.1 Module codes
..............................................................................................
17 4.2 Programmes
................................................................................................19
4.2.1 Bachelor of Science (Data Science) (NDSC701)
.............................. 19 4.2.1.1 Programme admission rules
.................................................20 4.2.1.2
Programme curriculum
.........................................................20 4.2.1.3
Module prerequisites
............................................................25
4.2.1.4 Module information
...............................................................27
4.2.2 Bachelor of Science
..........................................................................76
4.2.2.1 Programme admission rules
................................................76 4.2.2.2
Programme curriculum
........................................................77 (a)
Mathematical and Computer Sciences (NBSC705) ...................77
(b) Physical Sciences: Chemistry Stream
(NBSC761)...................138 (c) Physical Sciences: Physics
Stream (NBSC762) ....................... 170 (d) Biological
Sciences (NBSC707)
...............................................195
4.2.3 Diploma in Information and Communication Technology
(Applications Development)
............................................................ 216
4.2.3.1 Programme admission rules
.............................................. 218 4.2.3.2
Programme curriculum
...................................................... 218 4.2.3.3
Module Information
............................................................224
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1. CONTACT DETAILS Contact Details for School Enquiries
All correspondence with regard to academic programme matters should
be addressed to:
Head of School
Postal Address Sol Plaatje University Private Bag X5008 KIMBERLEY
8300
Street Address Luka Jantjie House Chapel Street KIMBERLEY
8300
Office Telephone Number & E-mail Address
(+27) 053 491 0216 aifheli.gelebe@spu.ac.za Head of School (+27)
053 491 0154 Kehilwe.Lesiba@spu.ac.za School Administrator
Website www.spu.ac.za
This Calendar is valid for the year 2018. The University reserves
the right to amend any rule or provision in this Calendar at any
time without prior
notice.
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2. STAFF INFORMATION Members of Staff
Administrative Staff Head of School Gelebe AC, Prof PhD, MSc (RU),
BSc Hons,
BSc (UNIVEN) School Administrator Lesiba K, Mrs
Academic Staff (*Head of Department)
Department of Biological and Agricultural Sciences Senior
Lecturers
Harebottle DM, Dr (Zoology) PhD (UCT), MSc, BSc Hons, BSc
(UKZN)
Adebowale A, Dr (Botany/ Biology)
PhD (UKZN), MSc, BSc Hons (OAU)
Lecturers Bopape MA, Ms (Agriculture) MTech, BTech (TUT) Nenzhelele
E, Ms (Botany) MSc (UCT), BSc Hons, BSc
(UNIVEN) Musvuugwa T, Dr (Botany) PhD (US), MSc (UCT), BSc
Hons (NUST, Zimbabwe) Modiba RV, Mr (Zoology) MSc, BSc Hons,
BSc
(UNIVEN) Laboratory Technician
Department of Computer Science and Information Technology Senior
Lecturers
Tuyikeze T, Dr (Information Technology)
PhD (UFH), MIT (NMMU), BTech (NMMU), N. Dipl. (WSU)
Mwansa G, Dr (Information Systems)
PhD (UNISA), MSc (UNAM), BSc (UNZA)
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Department of Computer Science and Information Technology Lecturers
Baitshenyetsi T, Mr
(Computer Science) MSc, BSc Hons, BSc (NWU)
Rudolph G, Mr (Information Technology)
MTech IT, BTech, N. Dipl. (CPUT)
Randle O, Mr (Information Networks)
MTech (TUT), BSc Hons (CU, Nigeria)
Mwansa M, Mrs (Management)
MPhil (UJ), BA Hons (UNISA), Bed (Univ, Namibia), Adv. Cert.
Project Management (UNISA)
Serutla LS, Mr (Computer Science)
MSc (Sherfield, UK), BSc (NUL)
Matsebula FT, Ms (Information Systems)
MTech (TUT)
Nkomo M, Mr (Communication and Information Engineering)
MSc (Shadong Univ. of Science & Tech., China), BEng Hons
(National University of Tech., Zimbabwe)
Mwanza AJ, Dr (Computer Science)
DEd (WSU), MSc (NUST), BSc (UNZA), PGDE (UCT), CCAI (CISCO)
Kunjuzwa DT, Mr (Computer Science)
MSc (UFH)
MSc (Zimbabwe)
Gundu T, Dr (Information Systems)
PhD, MCom, BSc Hons, BSc (UFH), Dip. PC Maintenance & Network,
Dip. Web Designing & E-Commerce (BCE, London)
Junior Lecturers
BTech (TUT), BSc (Vista)
BTech (TUT)
MSc, BSc Hons (NWU), BSc (Amity)
Marais IJ, Mr (Communications)
BCom Hons, BCom (NWU)
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Department of Mathematical Sciences Senior Lecturers
Sikwila ST, Dr (Applied Mathematics)
PhD (Univ. Limerick, Ireland), BSc Hons (UZ)
Mothibi D, Dr (Applied Mathematics)
PhD (NWU), MSc (SU), BSc Hons, BSc (NWU)
Lecturers Bappoo R, Mr (Operations Research)
MPhil (NUST, Zimbabwe), BA Hons (Delhi University, India), PG Dip
(Higher Education) (RU), Ed Planning & Admin (New Delhi), ACE
(UKZN)
Mabokgole MI, Mr (Mathematical Statistics)
MSc (UFS)
Pienaar M, Mr (Mathematics)
Sebogodi K, Mr (Mathematics)
Junior Lecturers
Sefadi JS, Dr (Chemistry) PhD (UFS)
Lecturers Tshabalala TE, Dr (Chemistry) PhD, MSc, BSc Hons, BSc
(WITS)
Mashile TR, Mr (Chemistry) MSc (UP), BSc Hons, BSc (UL)
Komati FS, Mr (Physics) MSc, BSc (Ed) (NWU) Sekonya KG, Dr
(Physics) PhD, MSc (WITS), BSc Hons,
BSc (UL) Zungu A, Mr (Physics) MSc, BSc Hons, BSc (UKZN) Kabanda
TH, Dr (Geography) PhD (NWU) Hlatywayo J, Mr (Geography) MSc, MCom
(UKZN), BSc
Hons, Dipl. Education (UZ, Zimbabwe)
Mokoena PP, Ms MSc, BSc Hons, BSc (UFS) Laboratory
Technicians
Mabuza MPJ, Ms (Chemistry) BSc Hons, BSc (UFS) Nepfumbada D, Ms
(Geology) BSc Hons, BSc (UFS) Buthelezi MD, Mr (Physics) BSc Hons,
BSc (UKZN)
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3. GENERAL RULES The University’s general rules are set out in the
General Rules and Information Book 2018. Please refer to it in
dealing with the following issues:
• University’s admission requirements;
• registration as a student, changing courses, course composition,
study duration, prerequisites for certain courses/modules, credit
for courses/modules passed at other tertiary institutions, etc.;
and
• requirements for a pass including passing with a distinction,
re-admission and exclusion of students, special examinations, rules
relating to examination halls, a misreading of examination
timetable, results and mark lists, etc.
3.1 RULES OF THE SCHOOL OF NATURAL AND APPLIED SCIENCES
The rules in this booklet relate specifically to the programmes
offered by the School of Natural and Applied Sciences.
Take note:
It is the students’ responsibility to acquaint themselves with both
the General Rules and the Programme Rules relevant to their
degree/diploma programme.
3.1.1 Registration and Progression Rules for all Programmes
3.1.1.1 Registration requirements
First-time entering students must enroll for all the required
modules at that level.
If a student fails courses spanning multiple levels, then the
student must first enroll for the courses at the lower level.
Consideration for enrolment of courses at the higher level will
only be considered if the pre-requisite criteria for these courses
are met AND if there are no timetable clashes.
A student will not be allowed to jump levels or enroll for modules
at more than two levels (e.g. a student with Year 1 module
outstanding, cannot enroll for Year Level 3 modules but will be
required to first complete all Year 1 modules).
The Head of School may limit the number of modules that a student
may enroll for when poor academic progress is evident.
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3.1.1.2 Exit Rules
Completion Rules as per University’s General Rules. (Refer to
General Rules and Information Book 2018)
3.1.1.3 Re-Admission of existing students
Refer to the University’s General Rules and Information Book
2018.
3.1.1.4 Credits and Exemptions
3.1.2 Assessment Rules
These assessment rules and procedure must be read in conjunction
with the Sol Plaatje University Policy on Assessment (DVC/003)
which was approved by Senate on 19 November 2014.
Assessment is the process of determining and developing students’
applied competencies, giving feedback on their progress, and final
result grades are awarded. Accordingly, this rules and procedure
manual provides a guide for the assessment of students learning in
the School of Natural and Applied Sciences of Sol Plaatje
University and supports quality assessment practices. It applies to
all coursework modules offered by the School, both at undergraduate
and postgraduate levels.
3.1.2.1 Rationale for Assessment
(a) The three key objectives for quality in student assessment in
higher education are to:
(i) Guide and encourage effective approaches to learning;
(ii) Validly and reliably measure expected learning outcomes, in
particular the higher-order learning that characterises higher
education; and
(iii) Define and protect academic standards.
(b) The following general principles underpin these Assessment
Rules and Procedures:
(i) Assessment should be valid and reliable. Moderation (including
external moderation where appropriate) of both the setting of
assessment tasks and of marking will be established to improve the
validity and reliability of assessment processes.
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(ii) Assessment requirements should be based on pre- determined and
clearly articulated criteria that describe standards of knowledge,
skills, competencies and/or capabilities.
(iii) Both formative and summative assessment should be used. In
its formative role it provides feedback to students and staff to
reinforce their successful learning and highlight areas where
improvements are needed. In its summative form it provides
information to judge the extent to which the student has achieved
the course objectives.
iv) Assessment should be inclusive and equitable for all students.
All students should be treated fairly, without prejudice, and with
reasonable assistance given to overcome disability and
disadvantage.
(v) Students should receive feedback on their work in a timely
manner that assists them to monitor their progress towards the
achievement of specified learning outcomes and to improve the
quality of their work.
(vi) Assessment should be regularly monitored and evaluated, so
that assessment items may be improved continuously.
(vii) Assessment processes and procedures should conform to the
highest ethical and moral standards.
3.1.2.2 Roles and Responsibilities
The key responsibilities of students include the following:
(i) To be aware that all forms of academic dishonesty or misconduct
are unacceptable.
(ii) To participate actively in the teaching and learning
environment. It is expected that students will: • Attend classes as
required; • Maintain steady progress within the module framework; •
Comply with workload expectations; and • Submit required work on
time.
(iii) To participate in the functioning of the Department and to
provide constructive feedback on the teaching and learning
environment.
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(iv) To be aware of their individual rights and responsibilities
regarding the proper use of copyright material.
(v) To be aware of all module information made available to them
and raise any questions concerns with the appropriate academic
staff member in a timely manner.
(vi) To raise any concern they may have regarding the marks for
each assessment task promptly, rather than wait until the final
mark is awarded in the module.
(vii) To check that their name is on the module list after classes
commence and if not, to contact the relevant department.
(viii) In the case of late enrolment, it is the responsibility of
the student to obtain all module materials already handed to
students from the lecturer in charge as early as possible.
(ix) Access and abide by all rules, procedures and regulations
relating to assessment and seek clarification where
necessary.
(b) Lecturers
It is the responsibility of Lecturer in charge (in consultation
with Head of Department or other relevant staff as appropriate)
to:
(i) Design and specify the number and type of assessment tasks and
their weightings.
(ii) Prepare the course outline in accordance with the procedures
and provide both an electronic/hard copy to the Department Office
prior to the start of the study period.
(iii) Make the module outline available to students enrolled in the
module on the first day of the study period.
(iv) Be available for student consultation on a regular
basis.
(v) Prepare and submit copies of the assessment tasks together with
their model answers to the Department Office at least three days
before the actual sitting of the assessment.
(vi) Prepare and submit the examination paper(s) to the Department
Office for central examinations by the due date.
(vii) Prepare and arrange the conduct of all Department-based
assessment tasks for the module and alternative/additional
assessment tasks, as required.
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(viii) Maintain and collate records of each student’s marks for all
assessment components in accordance with the assessment schedule in
the module outline. A secure record of each student’s results, both
electronically and in hard copy, must be kept by both the lecturer
and the Department Office.
(c) Markers
If markers, other than the lecturer in charge are appointed; it is
the responsibility of markers to mark assessment tasks accurately,
consistently and fairly, as guided by the Lecturer in charge.
(d) Head of Department
It is the responsibility of the Head of Department to:
(i) Oversee all the modules offered by the Department.
(ii) Allocate a Lecturer in charge for each module administered by
the Department.
(iii) Ensure that module outlines are reviewed and accurate prior
to publication.
(iv) Ensure that examination papers are reviewed and accurate prior
to submission, and are submitted by the relevant due date.
(v) Review the performance of students undertaking modules offered
by the Department, paying particular attention to results that are
border-line between grades.
(vi) Ensure that all ratified marks are submitted by the due
date.
(vii) Ensure that University quality assurance processes for
assessment are followed.
(viii) Ensure that the School Assessment Rules and Procedures and
academic regulations are implemented.
(e) Assessment Review Committee
An Assessment Review Committee shall be established by the Head of
School to review assessment outcomes for the School. The role and
responsibilities of the Assessment Review Committee shall be
specified by the Head of School at the time of its establishment
and should be reviewed annually. The Head of School shall undertake
the role of Chair of the Assessment
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Review Committee. Responsibilities so delegated by the Head of
School may include the following:
(i) Review the performance of students undertaking modules offered
by the Department.
(ii) Advise the Head of Department, who ratifies the final results
prior to submission.
(iii) Monitor the effectiveness of assessment practices in modules
offered by the Department.
(iv) Make recommendations to the Head of department regarding
assessment rules, procedures and outcomes.
3.1.2.3 Planning Assessment
(a) Assessment Tasks
Assessment tasks are the single components of an assessment
schedule and should be of different types to address students’
differing learning styles. Within any one assessment task, there
may be several aspects of assessment.
Assessment should be both formative and summative. The assessment
tasks should be appropriate to the discipline and explicitly
reflect the learning outcomes for the module and related generic
skills.
(b) Assessment Schedules
The learning outcomes in a module should be assessed through a
variety of tasks for example, an essay, seminar, class tests and
formal examination so students have several opportunities to
demonstrate their learning.
(c) Module Outlines (Study Guides)
The purpose of a module outline is to provide students with
essential administrative information about a module under study and
to give guidelines in achieving the learning outcomes for the
module.
(i) The module outline should include but not limited to, the
following information: • The module code and title. • The credit
value of the module. • The module learning outcomes.
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• Assessment criteria • The type of learning activities utilised
and delivery
mechanism (i.e. lectures, tutorials, seminars and/or online
learning activities).
• Recommended textbooks and other related books, including
information on whether or not these books are available in the
library/reserve section.
• Any learning resources (e.g. study guides) available for the
module and details of how to access them.
• Details of assessment including the criteria for successful
completion of the module: ~ The number, types and purpose of
assessment tasks
and the distribution of marks between them; ~ Information on which
learning outcomes are assessed
within each assessment task; ~ The dates of tests and other
scheduled assessment
tasks; ~ Due dates for each assessment task; ~ The duration of any
examination(s) for the module.
• Details of any penalties for late submission of work. • Contact
details for all staff teaching in the module. • Specific marking
criteria and weightings for each
assessment task, including referencing requirements. • Clear
details of any minimum essential requirements,
such as compulsory attendance or compulsory completion of some or
all of the assessment tasks.
• Reference to rules/policies, procedures and regulations
concerning late submission; plagiarism, collusion and processes for
allocating final marks.
(ii) Responsibility for Preparing Module Outlines
The Lecturer in charge of the module is responsible for preparing
the module outline and ensuring that the content is accurate
according to the approved programme. The module outline must be
finalised no later than two weeks prior to the commencement of the
first teaching week of the study period of the module.
(iii) Provision of Module Outlines to Students
A hard copy must be provided to every enrolled student no later
than the first scheduled class contact of the study period in which
the module will be delivered. If the module
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outline is available on line, a hard copy does not have to be
provided to every student, however the Department and the Lecturer
in charge may choose to provide any or all students with hard
copies irrespective.
The Head of the Department is responsible for ensuring that module
outlines are made available to students in accordance with this
procedure manual.
(iv) Altering a Module Outline after Issue
After a module outline has been issued to students, the assessment
details, criteria for successful completion of the module, and due
dates for assessment tasks, may be altered only with the consent of
the majority of the students enrolled in the module.
(v) Compliance
The Head of Department is responsible for ensuring module outlines
are prepared and made available to students in accordance with this
rules and procedure manual.
3.1.2.4 Examinations/Tests
An Examination means a formal, supervised assessment activity used
to assess student learning outcomes which comprises 40 – 60% of the
overall mark and which normally takes place at the end of a study
period.
Examinations are normally held during the University standard
examination periods and are centrally scheduled and managed by the
University Registrar’s Office.
(a) Security of Assessment Documents
Maintaining the integrity of examinations and assessment processes
is critical to the School’s operations. It is essential that the
security and confidentiality of examination/test papers be
maintained at all times and that unauthorised access does not
occur.
It is the responsibility of the Head of Department together with
the lecturer in charge of each module to ensure that appropriate
procedures and mechanisms are in place to guarantee proper handling
and storage of examination/test papers so that unauthorised access
to either electronic or hard copies does not occur.
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(i) Examination/test data files in computers must be password
protected and must not be stored on shared drives accessible to
unauthorised persons.
(ii) Examination/test papers must be printed in a secured room.
Papers must be kept in a strong room or locked cabinet or cupboard.
Only authorised personnel should have access to the storage
unit.
(iii) Copies of examination/test papers must not be emailed unless
they are password protected.
3.1.2.5 Invigilation
Invigilation of examinations shall be as per Examination Office
rules. The invigilation of other assessment types shall be
organised by the lecturer in charge of the module with the help of
the Head of Department. Under no circumstances are students
permitted as invigilators in these assessment types.
3.1.2.6 Marking, Checking, Recording and Submission of Marks
Lecturers in charge must ensure that:
(a) Marking is fair and consistent across the student cohort,
particularly in modules where more than one marker is used.
(b) Marking is not to be delegated to any other member of staff or
student, except to academic staff contracted to mark assessment,
without the approval of the Head of Department.
(c) Where other markers are employed, specific information is
provided by the lecturer in charge as to what is to be marked, the
marking scheme and the date by which assessed work must be returned
to the lecturer in charge.
(d) Comments on the assessment tasks submitted by students are made
on the exercise/assignment or on a marking sheet that is returned
to students with the assessment task.
3.1.2.7 Feedback on Assessment
(a) It is the responsibility of the lecturer in charge to ensure
that students receive feedback on their performance in assessment
tasks in a timely and effective manner. This occurs when students
are provided with feedback within a timeframe that will enable the
students the opportunity to take corrective measures to any
deficiencies prior to completion of the next related
assessment
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task. Feedback should be aimed at supporting the student’s learning
process and achievement of learning outcomes.
(b) For timeframes on feedback see the University Policy on
Assessment, section 8.2.
(c) Feedback on a student’s progress should be both in a quantified
form, such as scores, and a qualitative form such as comments,
model answers or suggested readings.
(d) Marks for assessment tasks may be posted on a noticeboard.
Student numbers only must be used in any such posting to preserve
confidentiality.
(e) Students should be given the opportunity to discuss their
performance and the feedback received with an appropriate academic
staff member.
(f) The lecturer in charge should make sure that the assessment
answer books are returned to the relevant student. The student
marks are confidential and they should not be displayed for anyone
to see them other than the actual student.
3.1.2.8 Remarking/Reviewing of Assessment Scripts
(a) An application from a student for remarking/reviewing of any
assessment script should be lodged with the Head of Department in
writing on the prescribed Departmental Application for
Remarking/Reviewing Form within two (02) working days of the
assessment feedback having been issued to the class.
(b) Requests for remarking/reviewing of assessment scripts will not
be entertained /allowed after two (02) days have elapsed.
(c) The Head of Department shall make the necessary arrangements
for the remarking/reviewing of the script and inform the student in
writing of the outcome thereof within two (02) days.
3.1.2.9 Exemption from Practicals
Exempting students from performing practicals in a given module is
a mechanism that is available to the Department to solve the
problem of shortage of space in the laboratory. A student repeating
a module which he/she failed in the previous academic year may be
granted exemption from practicals for that module provided he/she
obtained a practical mark of no less than 60% in the failed module.
The granting of this exemption is the Department’s prerogative, and
may not be granted to the same student in successive academic
years.
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The practical marks obtained in the failed module shall be used in
the calculation of the CASS mark in the module being
repeated.
3.1.2.10 Academic Integrity, Plagiarism and Academic
Misconduct
Academic Integrity is adherence to the principles underpinning the
work of an academic community. It involves pursuit of knowledge
through a commitment to such fundamental values as honesty, trust,
fairness, respect and responsibility, and requires acknowledgement
of the contribution of others.
Plagiarism means presenting the work or property of another person
as one’s own, without appropriate acknowledgement or
referencing.
Academic Misconduct means acting dishonestly or unfairly in
connection with any examination or other assessment task, or other
academic work.
It is the intention of this School to install good academic
practices by means of teaching, learning and research methodologies
that will ensure that all role players participating in these
academic practices do not plagiarise or transgress academic
integrity/ honesty. Concerns regarding possible plagiarism and/or
academic writing misconduct will be addressed by means of the SPU
Policy on Plagiarism.
3.1.3 Class Attendance
Regular attendance of the lectures is of primary importance. It is
a student’s responsibility to sign the register or the list of
attendance every day in class when applicable. All programmes in
the School, in line with section 6.2 of the General Rules of the
University, has set a minimum class attendance (mandatory) of 80%
for all courses/modules. The lecturer may give a 0 (zero) class
mark in cases where an absenteeism of more than 20% (without
legitimate reasons) is recorded. Absence from a class where any
mark is given e.g. presentation, class test, etc. will result in a
student obtaining 0% for that assessment.
Students are required to arrive on time for lectures. It is
disruptive to the lecturer as well as fellow students if persons
continue to enter the lecture room after start of the
lecture.
3.1.4 Laboratory Rules
3.1.4.1 Students must comply with the instructions of laboratory
supervisors.
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3.1.4.2 No eating, drinking or smoking is allowed in any
laboratory.
3.1.4.3 Students are not allowed to fiddle with laboratory
equipment in any way.
3.1.4.4 The use of the World Wide Web and the Internet is limited
to academic work only.
3.1.4.5 Be considerate of other laboratory users – this is a study
area. In consideration of others, do not talk on cell phones in the
laboratory. Please step outside the laboratory to conduct your
phone call.
3.1.4.6 Do not install or download any software or modify or delete
any system files on any laboratory computers.
3.1.4.7 Respect the equipment. Don’t damage, remove, or disconnect
any labels, parts, cables, or equipment.
3.1.4.8 Do not read or modify other users’ files.
3.1.4.9 Keep the noise level down. Use headphones for listening to
audio.
3.1.5 Grievance and Disciplinary Procedures
Students should STRICTLY adhere to the following grievance
procedure as formulated by the School:
Should you not be content with the offering in class, outcome of
results for work completed or any situation, you may complain or
appeal in the following order:
Step 1: Consult directly with the module lecturer.
Step 2: If problem persists, communicate with the class
representative and meet with the lecturer to discuss the problem
(class representative to minute the meeting);
Step 3: If the problem persists, put the problem in writing and
forward it to the Head of Department for his/her attention.
Step 4: If the outcome is not satisfactory, consult (with the class
representative’s assistance) with the next level of management, and
so on.
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4. PROGRAMMES OFFERED The following three programmes are offered in
the School of Natural and Applied Sciences:
Name of Qualification Minimum duration of study
Bachelor of Science (Data Science) 3 years Bachelor of Science 3
years Diploma Information and Communication Technology
(Applications Development)
3 years
4.1 Module Codes
The nine alpha-numeric coding system consists of two parts, namely
the Subject Field consisting of four characters and the Catalogue
Number consisting of five characters each.
4.1.1 Subject field (4 alpha characters)
Four alpha characters are available to identify the discipline. The
first character indicates the School; (or SPU) for example M refers
to Economic and Management; E refers to Education; N refers to
Natural and Applied Sciences, H refers to Humanities, S refers to
SPU. The next three alpha characters indicate the module name, for
example, MAT refers to Mathematics; PHY refers to Physics,
etc.
4.1.2 Catalogue number (5 numerical characters)
The second set represents the catalogue number, which consists of
four numerical characters.
4.1.2.1 The first character is assigned to the level at which the
module is offered (HEQF level).
4.1.2.2 The second character indicates the year of the
qualification; 1 = 1st year; 2 = 2nd year, 3 = 3rd year and 4 = 4th
year.
4.1.2.3 The third character indicates the tuition period, i.e.
semester 1 (uneven number), semester 2 (even number) or 0 for a
year module.
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4.1.2.4 The fourth and fifth characters correspond to the value of
the module.
All module codes in the School are based on the following
format:
Letter Letter Letter Letter Number Number Number Number
Number
S ch
oo l
Su bj
e
Example: The module code of Computer Science in semester 2 of year
1 will be – NCOS51216 (Basic Computer Organisation)
N C O S 5 1 2 1 6
N –
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4.2 Programmes
4.2.1 Bachelor of Science (Data Science)
The Bachelor of Science degree addresses a critical skills shortage
in the country and provides access to students in the Northern Cape
Province to an advanced area of study in a critical contemporary
discipline.
The Bachelor of Science degree in Data Science has a strong
mathematics core and focuses on data science and applications
thereof. The degree is designed to develop highly skilled graduates
in areas in which there are considerable shortages across the
country. Graduates in possession of this degree will be both
employable and eligible for further study, whether in Honors or
postgraduate diploma studies in the same or a cognate
discipline.
Data Science focuses on finding solutions to solving the ‘big data’
problems. This qualification addresses the need for predictive
models in diverse disciplines such as clinical research,
intelligence, consumer behavior and risk management. It also
addresses the critical skills shortage in the country and will
provide access to students to an advanced area of study in a
critical contemporary discipline.
In addition, this qualification forms an important part of the
evolving Academic Plan of the new Sol Plaatje University (SPU) in
Kimberley in the Northern Cape. The academic posture adopted by the
University has been to focus on the unique characteristics and
needs of the general Northern Cape region in a manner that raises
intellectual matters of local and global interest. SPU is keen to
develop the capacity for academic engagement in data science that
is wide in its reach. By providing access to students in the
Northern Cape to an advanced area of study in a critical
contemporary discipline, SPU will continue to focus on areas in
which it aims to make a high-quality intervention, driven by
academic excellence.
Career opportunities include Data Scientist, Software Engineer,
Business Analyst, Solutions Architect, Research, Statistics,
Computer Network Professional, Network Administrator, Network
Analyst, Software Applications Programmer, Software Applications
Developer, Web Administrator, Web Designer, Web Developer, Business
and Systems Analyst, Intelligence Analyst, Systems
Administrator.
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4.2.1.1 Programme Admission Rules
Minimum requirements for the BSc (Data Science) degree are as
follows:
(a) NSC pass with Bachelor’s Degree requirement
(b) English Home Language: NSC Level 4; or English 1st Language:
NSC Level 5.
(c) Mathematics: NSC Level 5 (Mathematical Literacy is not
acceptable).
(d) Admission Points Score: (APS): Minimum 30 points.
4.2.1.2 Programme Curriculum
CURRICULUM INFORMATION
BSC DATA SCIENCE (QUALIFICATION CODE DSC701) School School of
Natural and Applied Sciences
Qualification Name
DSC701
396 Is this a fixed Curriculum?
Yes
Once off Implementation Year No Migration Implementation
Years
Year Level 1 Year Level 2 Year Level 3 2018 2019 2020
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O LD
P R
O G
R A
M M
E N
EW P
R O
G R
A M
M E
PE R
IO D
O F
ST U
D Y
/ Y E
A R
L EV
EL 1
PE R
IO D
O F
ST U
D Y
/ Y E
A R
L EV
EL 1
1st S
em es
te r
1st S
em es
te r
M od
ul e
C od
e M
od ul
e N
am e
SA Q
en sd
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2nd S
em es
te r
2nd S
em es
te r
N D
SA 61
21 2
In tro
du ct
io n
to D
at a
St ru
ct ur
en sd
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PE R
IO D
O F
ST U
D Y
/ Y E
A R
L EV
EL 2
PE R
IO D
O F
ST U
D Y
/ Y E
A R
L EV
EL 2
1st S
em es
te r
1st S
em es
te r
M od
ul e
C od
e M
od ul
e N
am e
SA Q
en sd
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PE R
IO D
O F
ST U
D Y
/ Y E
A R
L EV
EL 3
PE R
IO D
O F
ST U
D Y
/ Y E
A R
L EV
EL 3
1st S
em es
te r
1st S
em es
te r
M od
ul e
C od
e M
od ul
e N
am e
SA Q
en sd
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4.2.1.3 Module Prerequisites
Module Code Module Name Prerequisite Module
Module Name
Admission Criteria
NMAT51516 Calculus Admission Criteria
Admission Criteria
Algorithms Admission
Criteria NAPM51216 Introduction to Numerical
methods and mathematical modelling
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YEAR LEVEL 2 1st Semester
Module Code
None
NDAS51210 Data Structures and Algorithms
NMAT62320 Advanced calculus
NDAS62212 Data Science 2B: Large scale Data analysis and
visualization
NDAS51210 Data Science I
NDIM62112 Discrete Mathematics
NDBS62212 Database Systems
NDIM62112 Discrete Mathematics
NAPM62410 Linear Programming
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YEAR LEVEL 3 1st Semester
Module Code
processing NMAT62410 Linear Algebra
automata NDIM62112 Discrete Mathematics
analysis NAAA62212 Applications and
Simulation and Modelling
NMAT62320 Advanced calculus
Module Code NBCA51110 Module Name Basic Computer Organization and
Architecture Module Description This course will introduce students
to the fundamental
concepts underlying modern computer organization and architecture.
Main objective of the course is to familiarize students about
hardware design including logic design, basic structure and
behavior of the various functional modules of the computer and how
they interact to provide the processing needs of the user. It will
cover machine level representation of data, instruction sets,
computer arithmetic, CPU structure and functions, memory system
organization and architecture, system input/output,
multiprocessors, and digital logic. The emphasis is on studying and
analyzing fundamental issues in architecture design and their
impact on performance.
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Module Code NBCA51110 Module Name Basic Computer Organization and
Architecture Module Content • Computer Evolution and
Performance
• Performance Issues • A Top-Level View of Computer Function
and
Interconnection • Cache Memory • External / Internal Memory •
Input/ Output • Operating System Support • Number Systems •
Computer Arithmetic • Digital Logic • Machine Instruction
Characteristics • Instruction Sets: Addressing Modes and Formats •
Processor Structure and Function
Learning Outcomes At the end of the module the learner is expected
to be able to: • Understand history of computers • understand the
basics of computer hardware and
how software interacts with computer hardware • analyze and
evaluate computer performance • understand how computers represent
and
manipulate data • understand computer arithmetic and convert
between different number systems • understand basics of Instruction
Set Architecture
(ISA) – MIPS • assemble a simple computer with hardware
design
including data format, instruction format, instruction set,
addressing modes, bus structure, input/output, memory,
Arithmetic/Logic unit, control unit, and data, instruction and
address flow
• use Boolean algebra as related to designing computer logic,
through simple combinational and sequential logic circuits
• Explore computer memory system • Understand input-Output concepts
in Computer
Organisation • Understand computer communication systems work •
Learn about Processor Structure and Functions
Module Information SAQA Credits CESM Code 10
Delivery Information Full/Part Time Semester Full Time 1
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Module Code NBCA51110 Module Name Basic Computer Organization and
Architecture Periods per Week Classes Practical Tutorials
Independent
Learning 4 2
Assessment Weighting Min. Formative Assessment mark for exam
admission (%)
40
Min. Final Assessment mark to pass (%) 50 Summative Assessment
Paper
Paper Paper Theory/ Practical
40%
Module Code NSTA51516 Module Name Introduction to Statistics Module
Description This course provides an introduction to the
contemporary application of statistics to a wide range of real
world situations. It has a strong practical focus using the
statistical package SPSS to analyse real data. The course will also
introduces a wide range of statistical techniques required for the
analysis of quantitative data.
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Module Code NSTA51516 Module Name Introduction to Statistics Module
Content • Descriptive statistical methods.
• Measures of central tendency and dispersion. • Permutations and
combinations. • Basic probability concepts. w • Discrete random
variables and their properties:
Bernoulli, Binomial, Poisson, Hypergeometric. • Normal
distributions. • Sampling distributions. • Point and interval
estimation. • One-sample and two-sample hypothesis tests for
proportions, means and variances. • Correlation and simple linear
regression.
Learning Outcomes At the end of the module the learner is expected
to be able to: • Understand, construct, visualize and present
a
coherent, mathematical argument. • Apply methods for scientific
problem-solving. • Demonstrate an ability to plan simple
experiments
and surveys. • Recognise the appropriate techniques for
the analysis of a variety of experimental and observational
studies.
• Appreciate statistics as a coherent discipline in its own
right.
• Demonstrate a sound preparation for a more theoretical and
mathematical study of statistics at Levels II and III.
• Use a modern statistical computing package Module Information
SAQA Credits CESM Code
16 Delivery Information Full/Part Time Semester
Full Time 1 Periods per Week Classes Practicals Tutorials
Independent
Learning 4 2
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Module Code NSTA51516 Module Name Introduction to Statistics
Assessment Weighting Min. Formative Assessment mark for
exam admission (%) 40
50
40%
Module Code NIAP51310 Module Name Introduction to Algorithms and
Programming Module Description This module serves as the first
course in computer
programming. It introduces student to concepts of algorithms and
their development, how to turn algorithms into programs, writing
programs, techniques that control flow and processing. The module
also aims to introduce concepts of object oriented
programming.
Module Content • pseudocode generation, algorithms, flowcharts, •
program development, compilation and running. • data types •
control structures in programming • basic data structures: one and
2 dimensional arrays,
records • file and file manipulation • recursion • Functions •
object oriented programming: concepts of objects,
methods, classes, inheritance.
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Module Code NIAP51310 Module Name Introduction to Algorithms and
Programming Learning Outcomes At the end of the module the learner
is expected to be
able to: • Decompose a problem into a pseudocode or
flowchart. • Turn pseudocode/flowchart into a program. • Implement
a program and control structures
appropriately and effectively. • Make good choice of data types
when implementing
solutions. • Aware of other programming techniques such as
recursion. • Appreciate importance of basic data structures
and
their application in programming. • Introduce concepts of object
oriented programming:
objects, classes, methods, classes, inheritance. Module Information
SAQA Credits CESM Code
10 Delivery Information Full/Part Time Semester
Full Time 1 Periods per Week Classes Practicals Tutorials
Independent
Learning 4 2
Assessment Weighting Min. Formative Assessment mark for exam
admission (%)
40
50
40%
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Module Code NSTA51416 Module Name Probability Theory Module
Description This module is an introduction to the theory
and concepts of Mathematical Statistics. It is a continuation of
the Introduction to Statistics course. This course investigates
probability distributions and their basic properties. Students are
introduced to Non-parametric tests and ANOVA.
Module Content • Set Theory • Probability Theory • Random
Variables, Expected Values • Distributions and their properties
(Discrete and
Continuous) • Hypothesis Testing and Confidence Intervals
(two
samples) • Non-parametric Tests, Chi-squared and
Correlation • Regression and Introduction to ANOVA
Learning Outcomes At the end of the module the learner is expected
to be able to: • Understand both Discrete and Continuous
Random variables and their applications • Calculate Expected
values, Variance and
Covariance • Perform hypothesis testing and calculate
confidence intervals for two samples • Understand non-parametric
tests and their related
applications • Solve Regression problems • Use Chi-squared tests to
assess the goodness of
fit • Understand and familiarise yourself with the
basics of ANOVA Module Information SAQA Credits CESM Code
16 Delivery Information Full/Part Time Semester
Full Time 2 Periods per Week Classes Practicals Tutorials
Independent
Learning 4 2
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Module Code NSTA51416 Module Name Probability Theory Assessment
Weighting Min. Formative Assessment mark
for exam admission (%) 40
50
40%
Module Code NMAT51516 Module Name Calculus Module Description This
module is designed to develop the topics of
differential and integral calculus. Emphasis is placed on limits,
continuity, derivatives and integrals of elementary and
transcendental functions of one variable.
Module Content Functions (Review): Domains and ranges of functions.
Inverse functions. Trigonometric and logarithmic functions. Limits
and Continuity. Derivatives and applications. Integration and
techniques of evaluating indefinite integrals. Integration by
substitutions, integration by parts and integration by partial
fractions. Change of variable in indefinite integrals. Integrals
involving roots and quadratics. Area and estimating with finite
sums. Sigma notation and limit of finite sums. Definite integrals,
Fundamental theorem of Calculus. Applications of the integral: Area
between curves. Volumes by cross sections and cylindrical shells.
Arc-length. Surface areas of revolution. Separable and linear first
order differential equations.
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Module Code NMAT51516 Module Name Calculus Learning Outcomes At the
end of the module the learner is expected to
be able to: • Provide interpretations of function, graphs of
functions and function values. • Compute limits of functions. •
Determine the existence of limits and the continuity
of functions. • Compute and apply equations of tangent lines
at
points on the graph of a function. • Interpret the derivative of a
function graphically,
numerically and analytically. • Compute derivatives using the rules
for
differentiation. • Perform implicit differentiation and
logarithmic
differentiation. • Recognize indeterminate form of a limit and
use
L’Hospital’s Rule to find the limit. • Determine intervals on which
a function is increasing
or decreasing, concave up or down and perform the first and second
derivative tests.
• Compute definite integrals and apply the Fundamental Theorem of
Calculus
Module Information SAQA Credits CESM Code 16
Delivery Information Full/Part Time Semester Full Time 1
Periods per Week Classes Practicals Tutorials Independent
Learning
4 2 Pre-requisite Admission criteria Assessment Methods Formative
(50%): Tests, Tutorials, Quizzes and/or
Assignments. Summative (50%): 1 × 3 h written examination.
Assessment Weighting Min. Formative Assessment mark for exam
admission (%)
40
50
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Module Code NMAT51516 Module Name Calculus Summative Assessment
Paper
Paper Paper Theory/ Practical
40%
Module Code NDSA51210 Module Name Data Structures and Algorithms
Module Description This module introduces to the study of data
structures
and algorithms. The purpose of this course is to provide the
students with solid foundations in the basic concepts of
programming: data structures and algorithms. The main objective of
the course is to teach the students how to select and design data
structures and algorithms that are appropriate for problems that
they might encounter. It introduces students to new types of data
structures such as trees, stacks and queues. Students will also
learn how to design new algorithms for each new data structure
studied, create and perform simple operations on graph data
structures, describe and implement common algorithms for working
with advanced data structures and recognize which data structure is
the best to use to solve a particular problem.
Module Content • Data Structure and Object-Oriented Programming •
Linked List. • Stacks and Data structures. • Queues. • Binary Trees
• Sort Algorithms • Search Algorithms • Graph Representations
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Module Code NDSA51210 Module Name Data Structures and Algorithms
Learning Outcomes At the end of the module the learner is expected
to
be able to: • Understand concepts of encapsulation,
inheritance,
polymorphism • Develop knowledge of basic data structures for
storage and retrieval of ordered or unordered data. Data structures
include: arrays, linked lists, binary trees.
• Develop knowledge of applications of data structures including
the ability to implement algorithms for the creation, insertion,
deletion, searching, and sorting of each data structure.
• Students implement projects requiring the implementation of the
any type data structures
• Be familiar with basic data structure of algorithms. • Master the
standard data structure library of a
major programming language (e.g. in C++) Module Information SAQA
Credits CESM Code
10 Delivery Information Full/Part Time Semester
Full Time 2 Periods per Week Classes Practicals Tutorials
Independent
Learning 4 2
Assessment Weighting Min. Formative Assessment mark for exam
admission (%)
40
50
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Module Code NDSA51210 Module Name Data Structures and Algorithms
Summative Assessment Paper
Paper Paper Theory/ Practical
40%
Module Code NMAT51416 Module Name Algebra Module Description This
course is designed to introduce students to the
concepts of linear systems and parametric equations. The course
also gives an introduction to complex numbers and gives the
relationship between vectors and complex numbers.
Module Content The Binomial Theorem. Principle of Mathematical
Induction. Parametric Equations and Polar Coordinates: Curves
defined by parametric equations. Calculus with parametric
equations. Polar coordinates. Conic sections.
Systems of Linear Equations and Matrices. Gaussian Elimination.
Matrices and Matrix Operations. Inverses. Determinants. Cramer’s
rule. Vectors in R^2 and R^3. Complex Numbers: The vector
interpretation of a complex number. Complex numbers
arithmetic.
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Module Code NMAT51416 Module Name Algebra Learning Outcomes At the
end of the module the learner is expected to
be able to: • Expand expressions using the Binomial theorem. •
Prove statements using the principle of
mathematical induction. • Sketch curves defined by parametric
equations. • Find derivatives and tangent lines of curves
defined
by parametric curves. • Perform matrix operations. • Find inverses
and determinants of matrices. • Solve systems of linear equations
and
homogeneous systems of linear equations by Gaussian
elimination
• Interpret vectors in two and three-dimensional space both
algebraically and geometrically.
• Perform basic algebraic manipulation with complex numbers
• Understand the geometric interpretation of complex numbers
• Know methods of finding the nth roots of complex numbers and the
solutions of simple polynomial equations.
Module Information SAQA Credits CESM Code 16
Delivery Information Full/Part Time Semester Full Time 2
Periods per Week Classes Practicals Tutorials Independent
Learning
4 2 Pre-requisite Admission criteria Assessment Methods Formative
(50%): Tests, Tutorials, Quizzes and/or
Assignments. Summative (50%): 1 × 3 h written examination.
Assessment Weighting Min. Formative Assessment mark for exam
admission (%)
40
50
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Module Code NMAT51416 Module Name Algebra Summative Assessment
Paper
Paper Paper Theory/ Practical
40%
Module Code NAPM51216 Module Name Introduction to Numerical Methods
and Mathematical
Modelling Module Description The course will develop numerical
methods to solve
algebraic, transcendental, and nonlinear equations, and to
calculate derivatives and integrals. The course will also develop
an understanding of the elements of error analysis for numerical
methods and certain proofs. The course will further develop problem
solving skills and modelling skills.
Module Content Error Analysis: absolute and relative error, round
off error and truncation error.
Roots of Non-Linear Equations: Bisection method, False position
method, Newton’s method, Secant method. Lagrange Interpolation.
Numerical Differentiation and Integration: Taylor series expansion,
numerical differentiation, numerical integration, left and right
Riemann sums, the Trapezoidal rule, Simpson’s rule, accuracy of the
Trapezoidal and Simpson’s approximation. Introduction to
Mathematical Modelling: exponential growth and decay, radioactive
decay, newton’s law of cooling, first order linear equations,
applications of linear equations in mixture problems and electric
circuits.
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Module Code NAPM51216 Module Name Introduction to Numerical Methods
and Mathematical
Modelling Learning Outcomes At the end of the module the learner is
expected to
be able to: • Demonstrate understanding of common numerical
methods and how they are used to obtain approximate solutions to
otherwise intractable mathematical problems.
• Apply numerical methods to obtain approximate solutions to
mathematical problems.
• Derive numerical methods for various mathematical operations and
tasks, such as interpolation, differentiation, integration, the
solution of linear and nonlinear equations.
• Analyse and evaluate the accuracy of common numerical
methods.
• Implement numerical methods in Matlab. • Write efficient,
well-documented Matlab code and
present numerical results in an informative way. • Model real life
situations using differential equations.
Module Information SAQA Credits CESM Code 16
Delivery Information Full/Part Time Semester Full Time 2
Periods per Week Classes Practicals Tutorials Independent
Learning
4 2 Pre-requisite Admission criteria Assessment Methods Formative
(50%): Tests, Tutorials, Quizzes and/or
Assignments.
Summative (50%): 1 × 3 h written examination. Assessment Weighting
Min. Formative Assessment mark
for exam admission (%) 40
50
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Module Code NAPM51216 Module Name Introduction to Numerical Methods
and Mathematical
Modelling Summative Assessment Paper
Paper Paper Theory/ Practical
40%
Module Code NDAS51210 Module Name Data Science I Module Description
This module provides students with the basics
concepts in big data such as the understanding of the various forms
or types of data and how they are processed and managed within a
short space of time. Finally the module provides the basics of
python programming.
Module Content • Introduction to Big Data • Distributed Computing •
Big data technology Concepts • Virtualization and Distributed
Computing • Cloud and Bigdata • Operational Databases • Map
reduction fundamentals • Exploring Hadoop • Understanding text
analytics and big data • Understanding how Big Data leverages
distributed
and parallel processing • Introduction to python basics, variable
types • Creating lists, slicing and dicing, list manipulation •
Introduction to python functions and packages
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Module Code NDAS51210 Module Name Data Science I Learning Outcomes
At the end of the module the learner is expected to
be able to: • Understand what Big Data is • Understand the benefits
that Big Data can offer to
businesses and organisations • Apply the methods and procedures of
Big Data
application areas and approaches in order to generate meaningful
report.
• be able to perform basic operations using python
programming
• use tools such as R and and Hadoop • Understand conceptually how
Big Data is stored • Understand how Big Data can be analysed
to
extract knowledge Module Information SAQA Credits CESM Code
10 Delivery Information Full/Part Time Semester
Full Time 2 Periods per Week Classes Practicals Tutorials
Independent
Learning 4 2
or Assignments. Summative (50%): 1 × 3 h written examination.
Assessment Weighting Min Formative Assessment mark for exam
admission (%)
40
50
40%
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Year Level 2
Module Code NOCN62112 Module Name Operating Systems and Computer
Networks Module Description This module is intended as an
introduction to both
fundamental concepts of computer networking and operating systems
for Data Scientist. Students will get a comprehensive overview
operating system concepts, maintenance, resources, network design,
and acquire hands-on experience in programming different aspects of
a computer network. The course provides a full introduction to
modern operating system design, including memory management,
scheduling, I/O, protection.
Module Content • Introduction to operating systems, • Operating
systems principles, • Memory management, Security protection, •
Virtual machines, • Device and file Management, • Introduction to
Computer Networks and Data
Communications, • Fundamentals of Data and Signals, • Conducted and
Wireless Media, • Local Area Networks, • The Internet, • Network
Security,
Learning Outcomes At the end of the module the learner is expected
to be able to: • Understand the generic requirements,
structure,
operation, and administration of a modern operating system.
• Understand the principles of operating and network
security.
• Be able to analyze, design and write programs at the operating
systems level.
• Understand the requirements and design of modern network
protocols and systems, their operation and use by
applications.
Module Information SAQA Credits CESM Code 12
Delivery Information Full/Part Time Semester Full Time 1
Periods per Week Classes Practicals Tutorials Independent
Learning
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Module Code NOCN62112 Module Name Operating Systems and Computer
Networks
4 2 Pre-requisite None Assessment Methods Formative (50%): Tests,
Practicals, Tutorials and/or
Assignments. Summative (50%): 1 × 3 h written examination.
Assessment Weighting Min. Formative Assessment mark for exam
admission (%)
40
50
40%
Module Code NDIM62112 Module Name Discrete Mathematics Module
Description This is an introductory module in discrete
mathematics. The goal of this course is to introduce students to
ideas and techniques from discrete mathematics that are widely used
in science. This course teaches the students techniques in how to
think logically and mathematically and apply these techniques in
solving problems.
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Module Code NDIM62112 Module Name Discrete Mathematics Module
Content Principles of Counting: The rules of sum and
product, permutations, combinations, the Binomial theorem,
combinations with repetition. Introduction to Probability Theory.
Logic: Basic connectives and truth tables, logical equivalence,
logical implication, the use of quantifiers. Set Theory: Sets and
subsets, set operations and the laws of set theory, counting and
venn diagrams. Properties of Integers: The well ordering principle,
mathematical induction, recursive definitions, the division
algorithm: prime numbers, the greatest common divisor: The
Euclidean algorithm, the fundamental theorem of arithmetic. Graph
Theory: Definitions and examples, subgraphs, complements, graph
isomorphism, vertex degree: Euler trails and circuits, planar
graphs, Hamilton paths and cycles, graph colouring. Trees:
Definitions, properties and examples, rooted trees, trees and
sorting, weighted trees and prefix codes, biconnected components
and articulation points. Coding Theory Principles.
Learning Outcomes At the end of the module the learner is expected
to be able to: • Understand and construct mathematical arguments •
Prove simple arguments • Develop recursive algorithms based
on
mathematical induction • State basic properties of relations •
State and apply essential concepts in graph theory
and related algorithms • State basic concepts in formal languages
and
computability • Apply knowledge about discrete mathematics in
problem solving Module Information SAQA Credits CESM Code
12 Delivery Information Full/Part Time Semester
Full Time 1 Periods per Week Classes Practicals Tutorials
Independent
Learning 4 2
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Module Code NDIM62112 Module Name Discrete Mathematics Assessment
Methods Formative (50%): Tests, Tutorials, Quizzes and/or
Assignments. Summative (50%): 1 × 3 h written examination.
Assessment Weighting Min. Formative Assessment mark for exam
admission (%)
40
50
40%
Module Code NDAS62112 Module Name Data Science 2A: Data Analysis
and Visualization Module Description This module focuses on the
technical aspects to
creating a data scientist, providing the students with the
necessary skills to manage data such as cleaning, scraping and
visualization techniques.
Module Content • Cleaning and preparing Big Data for analysis •
Classification of data • Working with missing values • Numpy
manipulation processes • Managing large volumes of data through
Pandas • Basics of visualization • Introduction to Matplotlip
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Module Code NDAS62112 Module Name Data Science 2A: Data Analysis
and Visualization Learning Outcomes At the end of the module the
learner is expected to
be able to: • Define and explain key data science concepts
and
models, including data cleaning and integration, data-intensive
distributed computing.
• Design, implement, and evaluate the core algorithms underlying an
end-to-end data science workflow, including the experimental
design, data collection, mining, analysis, and presentation of
information derived from large datasets.
• Use Python tools to scrape, clean, and process data.
• Demonstrate knowledge of the use of data management techniques to
store data locally and in cloud infrastructures.
• Explore data using statistical methods and visualization
techniques.
• Be able to make predictions based on visualization
techniques
• Use descriptive statistics and visualizations to effectively
communicate the outcome of data analysis
Module Information SAQA Credits CESM Code 12
Delivery Information Full/Part Time Semester Full Time 1
Periods per Week Classes Practicals Tutorials Independent
Learning
4 2 Pre-requisite NDAS51210 - Data Science I Assessment Methods
Formative (50%): Tests, Practicals, Tutorials and/or
Assignments. Summative (50%): 1 × 3 h written examination.
Assessment Weighting Min Formative Assessment mark for exam
admission (%)
40
50
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Module Code NDAS62112 Module Name Data Science 2A: Data Analysis
and Visualization Summative Assessment Paper
Paper Paper Theory/ Practical
40%
Module Code NMAT62320 Module Name Advanced Calculus Module
Description This module takes calculus from the two
dimensional
world of single variable functions into the three dimensional
world, and beyond, of multivariable functions.
Module Content Sequences and Series: Basic terminology and
convergence of Sequences. Basic terminology of Series. Partial
Derivatives. Limits and continuity.
Multiple Integrals. Vector Calculus: Integration in vector fields
Line integrals. Vector fields. Work, circulation, and flux. Path
independence, potential functions, and conservative fields. Green’s
theorem. Surface area and surface integrals. Stokes’ theorem.
Divergence theorem. High Order linear ordinary differential
equations, homogeneous and nonhomogeneous equations.
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Module Code NMAT62320 Module Name Advanced Calculus Learning
Outcomes At the end of the module the learner is expected to
be able to: • Perform convergence or divergence tests for
sequences and series. • Find the limits and partial derivatives for
multiple
variable functions. • Apply derivative concepts to find tangent
lines to
level curves and to solve optimization problems. • Find the optimum
points for multivariable functions. • Evaluate double and triple
integrals for area and
volume. • Change the order of integration and evaluate
double and triple integrals over general regions. • Set up
integrals in terms of cylindrical and spherical
coordinates, • differentiate vector fields. • Determine gradient
vector fields and find potential
functions. • Evaluate line integrals directly and by the
fundamental theorem. Module Information SAQA Credits CESM
Code
20 Delivery Information Full/Part Time Semester
Full Time 1 Periods per Week Classes Practicals Tutorials
Independent
Learning 4 2
Assessment Weighting Min Formative Assessment mark for exam
admission (%)
40
50
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Module Code NMAT62320 Module Name Advanced Calculus Summative
Assessment Paper
Paper Paper Theory/ Practical
40%
Module Code NSTI62112 Module Name Statistical Inference Module
Description The purpose of this module is to enable students
to
test, deduce and infer the validity of different types of
hypotheses and models built on the basis of the raw data. In this
course, students will also be able to learn basic statistical
softwares like SPSS and R. Students will also develop a deeper
understanding of the basis underlying modern statistical inference
and will enable them to apply their knowledge and skills to real
world tasks.
Module Content • Linear functions • Introduction to P-values •
Hypothesis recap • Chi-squared goodness of fit test • Analysis of
variance • Non-parametric statistics • Multiple regression •
Introduction to Markov chains • Time series analysis
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Module Code NSTI62112 Module Name Statistical Inference Learning
Outcomes At the end of the module the learner is expected to
be
able to: • Understand, construct, visualize and present a
coherent, mathematical argument. • Apply methods for scientific
problem-solving. • Demonstrate an ability to plan simple
experiments
and surveys. • Use a modern statistical computing package • To have
the skills necessary to analyze &
summarize various types of data with an emphasis on experimental
design. Stress will be placed on statistical reasoning &
applications, rather than derivation of theoretical details.
• understand the notion of a Markov chain, and how simple ideas of
conditional probability and matrices can be used to give a thorough
and effective account of discrete-time Markov chains;
• understand notions of long-time behaviour including transience,
recurrence, and equilibrium;
• Be able to apply these ideas to answer basic questions in several
applied situations including genetics, branching processes and
random walks.
Module Information SAQA Credits CESM Code 12
Delivery Information Full/Part Time Semester Full Time 1
Periods per Week Classes Practicals Tutorials Independent
Learning
4 1x3 Hours 2 Pre-requisite NSTA51516 - Introduction to Statistics
Assessment Methods Formative (50%): Tests, Practicals, Tutorials
and/or
Assignments. Summative (50%): 1 × 3 h written examination.
Assessment Weighting Min. Formative Assessment mark for exam
admission (%)
40
50
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Module Code NSTI62112 Module Name Statistical Inference Summative
Assessment Paper
Paper Paper Theory/ Practical
40%
Module Code NDAS62212 Module Name Data Science 2B: Large scale Data
analysis and
visualization Module Description This module offers a range of
statistical and graphical
techniques to uncover hidden structures in the data, including
machine learning and data mining techniques. The course has a
strong practical focus; the students will actively learn how to
apply these techniques on real data.
Module Content 1. Introduction to Large scale data analysis and
visualization • Handling Big Data in R • basic visualization,
graphics grammar • Feature extraction and Dimensional
reduction
2. Analyzing Big Data with Open Source R and Hadoop • Supervised
learning: regression • Train/validation/test paradigm,
metrics,
regularization, trees 3. Visualization and Storage
• Deriving reports and communicating results archiving data
Learning Outcomes At the end of the module the learner is expected
to be able to: • Hands on experience solving Data Science
problems working with big data storage and processing tools like
Hadoop or Cassandra.
• Use R and RStudio IDE as well as packages in R for large scale
big data exploration and analysis
• Demonstrate knowledge of the use of data management techniques to
store data locally and in cloud infrastructures.
• Use descriptive statistics and visualizations to effectively
communicate the outcome of data analysis.
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Module Code NDAS62212 Module Name Data Science 2B: Large scale Data
analysis and
visualization Module Information SAQA Credits CESM Code
12 Delivery Information Full/Part Time Semester
Full Time 2 Periods per Week Classes Practicals Tutorials
Independent
Learning 4 2
Pre-requisite NDAS51210 - Data Science I Assessment Methods
Formative (50%): Tests, Practicals, Tutorials and/or
Assignments. Summative (50%): 1 × 3 h written examination.
Assessment Weighting Min. Formative Assessment mark for exam
admission (%)
40
50
40%
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Module Code NAAA62212 Module Name Applications and Analysis of
Algorithms Module Description This module introduces the
fundamental techniques
for designing and analysing algorithms. The emphasis of the course
will be on the design and analysis of algorithms, with focus on
problems arising in computing applications. The course aims is to
provide students with techniques for designing such algorithms,
analysing them for their efficiency, and choosing appropriate data
structures to implement them. This course covers four major
algorithm design techniques (greedy algorithms, divide-and-conquer,
dynamic programming, and network flow), undesirability and
NP-completeness, and algorithmic techniques for intractable
problems (including identification of structured special cases,
approximation algorithms, local search heuristics, and online
algorithms).
Module Content • Introduction to algorithm analysis and design. •
Computer representation of graphs. • Algorithm design and analysis
techniques revisited. • Trees: spanning and search trees. • Paths:
Shortest path, searches and connectivity,
bicomponents, strongly connected components, PERT, Eulerian and
Hamiltonian circuits.
• Sorting and selection • Fundamental Techniques. •
NP-completeness.
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Module Code NAAA62212 Module Name Applications and Analysis of
Algorithms Learning Outcomes • At the end of the module the learner
is expected to
be able to: • Determine the time and space complexity of
simple
algorithms • Use big O notation formally to give asymptotic
upper
bounds on time and space complexity of algorithms • Explain the use
of big omega, big theta, and little o
notation to describe the amount of work done by an algorithm
• Solve problems using fundamental graph algorithms, including
depth-first and breadth-first search
• Demonstrate the ability to evaluate algorithms, to select from a
range of possible options, to provide justification for that
selection, and to implement the algorithm in a particular
context
• Solve problems using graph algorithms, including single-source
and all-pairs shortest paths, and at least one minimum spanning
tree algorithm
• Model a variety of real-world problems in computer science using
appropriate forms of graphs and trees, such as representing a
network topology or the organization of a hierarchical file
system
• Design, implement and analyse graph algorithms including search
trees, minimum weight spanning trees, connected components,
bicomponents, shortest paths, PERTs, and flows.
Module Information SAQA Credits CESM Code 12
Delivery Information Full/Part Time Semester Full Time 2
Periods per Week Classes Practicals Tutorials Independent
Learning
4 2 Co-Requisite NDIM62112 -Discrete Mathematics Assessment Methods
Formative (50%): Tests, Practicals, Tutorials and/or
Assignments. Summative (50%): 1 × 3 h written examination.
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Module Code NAAA62212 Module Name Applications and Analysis of
Algorithms Assessment Weighting Min. Formative Assessment mark
for
exam admission (%) 40
50
40%
Module Code NDBS62212 Module Name Database Systems Module
Description The module gives a broad overview of database
concepts. It covers data modelling issues and translating them into
database schemas. It explores issues relating to transaction
management in both centralized and distributed database
environments as well as security issues. It also covers issues
relating to project management and project appraisal.
Module Content • data modeling and database design • database
security • project management and project appraisal • normalization
• transaction management and processing • data warehouse • data
mining
Learning Outcomes At the end of the module the learner is expected
to be able to: • Model raw data into database schema • normalize
tables to create efficient database • Master concepts of
transaction management and
processing in centralised and distributed database systems
• Master concepts data warehousing and data mining implementation
and application.
• Master concepts of project management, appraisal, monitoring and
implementation
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Module Code NDBS62212 Module Name Database Systems Module
Information SAQA Credits CESM Code
12 Delivery Information Full/Part Time Semester
Full Time 2 Periods per Week Classes Practicals Tutorials
Independent
Learning 4 2
Assessment Weighting Min Formative Assess