Welcome Message
Dear students,
Welcome to the Survey Sample Methods module! We hope that by the end of this module, you have a new appreciation for the subject matter and will continue your education in the subject. My name is Dr. Joseph Nkurunziza, and we will be your lecturer for the first Semester of the 2025-2026 academic year. In this welcome message, you will find basic information about the module as well as resources for ensuring your success in online education. The module will be conducted in UR E-learning, and everything for your success will be found there. Once the module starts, you will be able to log in at https://elearning.ur.ac.rw website. Success in an online class requires just as much work and effort as success in a traditional classroom. The amount of time you can expect to commit to the class in any given week will vary, but it will probably average around 20 hours per week.
NB: The reading materials, like books, scientific research papers, and reports are attached to the assignments or to the chapters.
Your success is important to us. Please do not hesitate to contact us if you are having difficulty with the module material. General questions about the module should be posted on the e-learning Forum. If you have questions that are more personal in nature, emailing j.nkurunziza@ur.ac.rw or nkurunzizaj@gmail.com is the best way to contact us for a quick response.
Our goal is to respond to messages within 6 hours between 7 am and 7 pm, Monday through Friday. However, it is a goal, and unforeseen circumstances can arise. If you do not receive a response back within the 6-hour timeframe, resend the message as it may have gotten lost in spam. We look forward to working with you!
The Lecturer of this module is:
Dr. Joseph Nkurunziza
Phone: (+250)788321816
Email: j.nkurunziza@ur.ac.rw/nkurunzizaj@gmail.com
Office: Principal Office: UR-Huye Campus
Welcome Message
Dear students,
Welcome to the Survey Sample Methods module! Our hope is that by the end of this module you have a new appreciation for the subject matter and will continue your education in the subject. My name is Dr. Joseph Nkurunziza and we will be your lecturer for the first Semester of 2025-2026 academic year. In this welcome message, you will find basic information about the module as well as resources for ensuring your success in online education. The module will be conducted in UR E-learning and everything for your success will be found there. Once the module starts you will be able to login at https://elearning3.ur.ac.rw/ website. Success in an online class requires just as much work and effort as success in a traditional classroom. The amount of time you can expect to commit to the class in any given week will vary but it will probably average around 20 hours per week.
NB: The reading materials like books, scientific research papers, and reports are attached to the assignments or to the chapters.
Your success is important to us. Please do not hesitate to contact us if you are having difficulty with the module material. General questions about the module should be posted on the e-learning Forum. If you have questions that are more personal in nature email to j.nkurunziza@ur.ac.rw or nkurunzizaj@gmail.com is the best way to contact us for a quick response.
Our goal is to respond to messages within 6 hours between 7 am and 7 pm, Monday through Friday. However, it is a goal and unforeseen circumstances can arise. If you do not receive a response back within the 6 hours timeframe, resend the message as it may have gotten lost in spam’s. We look forward to working with you!
The Lecturer of this module is:
Dr. Joseph Nkurunziza
Phone: (+250)788321816
Email: j.nkurunziza@ur.ac.rw/nkurunzizaj@gmail.com
Office: Principal Office: UR-Huye Campus
Welcome to the module of Numerical Linear algebra.
The main aim of this module is to equip the students with a common basis for linear algebra tools which are fundamental to the development of statistical models and implementation of machine learning algorithms. Moreover, the module treats main classes of large scale problems covering both dense and sparse matrices with numerical implementation on practical problems arising in areas such as data mining.