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1. INTRODUCTION
Welcome to the Programme in Basic-, Intermediate- and Advanced Project Management
at UNISA. I trust that you will not only enjoy the programme, but also that you will find it
useful with regard to your work.
2. SYLLABUS
The syllabi for this programme and the module are specified hereunder.
2.1 Programme in Basic-, Intermediate- and Advanced Project Management:
The Programme consists of the following twelve (12) modules:
Semester 1 registration period 1: (Part 1)
• The Project Management Framework ( Module code: DAPM01M)
• Strategic Project Management (Module code: DAPM02N)
• Project Management for Development and Upliftment (Module code: DAPM03P)
• Project Management Processes (Module code: DAPM04Q)
• Project Time Management (Module code: DAPM05R)
• Project Human Resources Management (Module code: DAPM07T)
Semester 2 registration period 2: (Part 2)
• Project Cost Management (Module code: DAPM08U)
• Project Procurement Management (Module code: DAPM09V)
• Project Risk Management (Module code: DAPM10N)
• Project Quality Management (Module code: DAPM11P)
• Project Communications Management (Module code: DAPM12Q)
• Information Technology for Project Management (Module code: DAPM06S)
2.2 Information Technology for Project Management
This module consists of 2 study units:
• Introduction to MS Project 2007
• Building your plan, track and manage your project
Attachments:
4111883_1_69641-1-101-201107-3-b-1-.pdf

Operations Research-I
Practice Problem 3
(1) Consider the following problem.
You are given the information that the nonzero variables in the optimal solution are x2 and x3. Describe how
you can use this information to adapt the simplex method to solve this problem in the minimum possible
number of iterations.
(2) Consider the following problem.
Work through the simplex method to demonstrate that Z is unbounded.

Chapter 11
Pages 300-306 of the text cover Teams and Team Development. Research articles related to this topic online and select one. Provide a synopsis of the article including any recommendations the author shares on the following.
1. Making the process smoother.
2. Overcoming obstacles in the process.
3. Alternative methods of team development.
4. Include the link to the article.
Chapter 12
Performance appraisals allow supervisors to evaluate and help develop the skills of their employees. They review past performance to identify accomplishments and deficiencies and can offer a detailed plan to improve future performance. Answer the following questions about performance appraisals. Write at least 250 words.
1. Why, in your opinion, do many supervisors dislike or even avoid giving employees performance feedback?
2. What can a supervisor do to minimize distortions in the appraisal process?
3. What is the relationship between goal setting and performance appraisal?
4. What is a 360-degree appraisal? What are the advantages and disadvantages of using this method of appraising?
Attachments:
4118280_1_Chapter-1112.docx

Data Mining Helps in Cancer Research
According to the American Cancer Society, half of all men and one-third of all women in the
United States will develop cancer during their lifetimes; approximately 1.5 million new cancer
cases were expected to be diagnosed in 2013. Cancer is the second-most-common cause of death
in the United States and in the world, exceeded only by cardiovascular disease. This year, over
500,000 Americans are expected to die of cancer—more than 1,300 people a day—accounting
for nearly one of every four deaths. Cancer is a group of diseases generally characterized by
uncontrolled growth and spread of abnormal cells. If the growth and/or spread are not
controlled, it can result in death. Even though the exact reasons are not known, cancer is believed
to be caused by both external factors (e.g., tobacco, infectious organisms, chemicals, and
radiation) and internal factors (e.g., inherited mutations, hormones, immune conditions, and
mutations that occur from metabolism). These causal factors may act together or in sequence to
initiate or promote carcinogenesis. Cancer is treated with surgery, radiation, chemotherapy,
hormone therapy, biological therapy, and targeted therapy. Survival statistics vary greatly by
cancer type and stage at diagnosis. The 5-year relative survival rate for all cancers is improving,
and decline in cancer mortality had reached 20% in 2013, translating into the avoidance of about
1.2 million deaths from cancer since 1991. That’s more than 400 lives saved per day! The
improvement in survival reflects progress in diagnosing certain cancers at an earlier stage and
improvements in treatment. Further improvements are needed to prevent and treat cancer. Even
though cancer research has traditionally been clinical and biological in nature, in recent years
data-driven analytic studies have become a common complement. In medical domains where
data- and analytics-driven research have been applied successfully, novel research directions
have been identified to further advance the clinical and biological studies. Using various types of
data, including molecular, clinical, literature-based, and clinical trial data, along with suitable data
mining tools and techniques, researchers have been able to identify novel patterns, paving the
road toward a cancer-free society. In one study, Delen (2009) used three popular data mining
techniques (decision trees, artificial neural networks, and SVMs) in conjunction with logistic
regression to develop prediction models for prostate cancer survivability. The data set contained
around 120,000 records and 77 variables. A k-fold cross-validation methodology was used in
Attachments:
4126656_1_Data-Mining-Helps-in-Cancer-Research.pdf