SCHOOL OF NATURAL AND APPLIED SCIENCES …

PROGRAMME RULES AND INFORMATION
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page iSCHOOL OF NATURAL AND APPLIED SCIENCES PROGRAMME RULES AND INFORMATION 2018
SCHOOL OF NATURAL AND APPLIED SCIENCES
PROGRAMME RULES AND INFORMATION
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page ii SCHOOL OF NATURAL AND APPLIED SCIENCES PROGRAMME RULES AND INFORMATION 2018
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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page 1SCHOOL OF NATURAL AND APPLIED SCIENCES PROGRAMME RULES AND INFORMATION 2018
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 [email protected] Head of School (+27) 053 491 0154 [email protected] 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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page 2 SCHOOL OF NATURAL AND APPLIED SCIENCES PROGRAMME RULES AND INFORMATION 2018
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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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
Learning 4 2
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
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
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.
40
50
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Module Code NMAT51516 Module Name Calculus Summative Assessment Paper
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
Learning 4 2
40
50
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Module Code NDSA51210 Module Name Data Structures and Algorithms Summative Assessment Paper
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.
40
50
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Module Code NMAT51416 Module Name Algebra Summative Assessment Paper
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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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.
Assignments.
Summative (50%): 1 × 3 h written examination. Assessment Weighting Min. Formative Assessment mark
for exam admission (%) 40
50
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Modelling Summative Assessment Paper
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
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.
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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
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
Learning 4 2
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Module Code NDIM62112 Module Name Discrete Mathematics Assessment Methods Formative (50%): Tests, Tutorials, Quizzes and/or
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
4 2 Pre-requisite NDAS51210 - Data Science I Assessment Methods Formative (50%): Tests, Practicals, Tutorials and/or
40
50
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Module Code NDAS62112 Module Name Data Science 2A: Data Analysis and Visualization Summative Assessment Paper
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
Learning 4 2
40
50
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Module Code NMAT62320 Module Name Advanced Calculus Summative Assessment Paper
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.
4 1x3 Hours 2 Pre-requisite NSTA51516 - Introduction to Statistics Assessment Methods Formative (50%): Tests, Practicals, Tutorials and/or
40
50
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Module Code NSTI62112 Module Name Statistical Inference Summative Assessment Paper
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
Learning 4 2
Pre-requisite NDAS51210 - Data Science I Assessment Methods Formative (50%): Tests, Practicals, Tutorials and/or
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.
4 2 Co-Requisite NDIM62112 -Discrete Mathematics Assessment Methods Formative (50%): Tests, Practicals, Tutorials and/or
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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
Learning 4 2
Assessment Weighting Min Formative Assess