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Courseware
This course is intended for students enrolling for BSc with Education and BEd degrees. Solid state physics forms the backborn of physics. The module has four units: Introduction to solid state physics; Crystal defects and mechanical properties ; Thermal and electrical properties; and Band theory & Optical properties.In the first unit/activity i.e. introduction to solid state physics. The student is expected to explain the atomic structure, describe the various atomic bonds such as ionic bonds and covalent bonds. The learning will also require students to distinguish between crystalline and amorphous solids; polycrystalline and amorphous solids and to explain the production and use of X-ray diffraction. In the second unit i.e. crystal defects and mechanical properties, the learning includes, differentiating between the different types of crystal defects: the point defects (vacancy, interstitials, and substitutional) and dislocations (screw and edge). Here, the student learns that point defects are very localised and are of atomic size, while dislocation is a disorder which extend beyond the volume of one or two atoms. The effects of the defects on mechanical, and electrical properties of these defects are also part of the learning that will take place. In unit three the learning outcomes include definitions of heat capacity, and explanations of variation of heat capacity with temperature based on the classical, Einstein and Debye models. The students will be required to use the free electron theory to explain high thermal and electrical conductivities of metals and also be able to derive and apply the Wiedermann-Frantz law. Finally, in activity four, the expected learning should enable the students to use the band theory to explain the differences between conductors, semiconductors and insulators; explain the differences between intrinsic and extrinsic semiconductors in relation to the role of doping. At the end of it all, the students use the concepts of the interaction of electromagnetic waves (light) with materials to explain optical absorption, reflectivity and transmissivity.
- Subjects:
- Physics
- Keywords:
- Solid state physics
- Resource Type:
- Courseware
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Courseware
The BIIG problem-solving method is unique in that it forces us to concentrate on decoding a real-world word problem completely into meaningful parts and aids us in finding and applying the right formula to easily arrive at the correct solution. As desired, it places less emphasis on the memorization of factual detail and more emphasis on the understanding of concepts. Evidently, this method is beneficial in many ways as it aids students in honing skills in critical thinking, logical approach and attention to detail. As a method for organizing information it helps students avoid errors and sets them on a path to succeed. As long as the numbers are “buddied up” with their units, “identified” by the appropriate variables, “isolated” within the context, and the answer is presented “gourmet”, or explained in terms of the original question, finding a solution to any complex problem will become seamless, understandable and enjoyable. This innovation in science education fosters a passion for learning and serves as a foundation for a new paradigm for problem-solving in any discipline of science worldwide.
- Subjects:
- Physics
- Keywords:
- Problem solving Physics
- Resource Type:
- Courseware
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Others
Extract human-understandable insights from any machine learning model.
- Subjects:
- Computing
- Keywords:
- Python (Computer program language) Machine learning
- Resource Type:
- Others
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Others
Take your SQL skills to the next level.
- Subjects:
- Computing
- Keywords:
- Database management SQL (Computer program language)
- Resource Type:
- Others
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Others
Make great data visualizations. A great way to see the power of coding!
- Subjects:
- Computing
- Keywords:
- Information visualization Python (Computer program language)
- Resource Type:
- Others
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Others
Learn to handle missing values, non-numeric values, data leakage and more. Your models will be more accurate and useful.
- Subjects:
- Computing
- Keywords:
- Python (Computer program language) Machine learning
- Resource Type:
- Others
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Others
Learn SQL for working with databases, using Google BigQuery to scale to massive datasets.
- Subjects:
- Computing
- Keywords:
- Database management SQL (Computer program language)
- Resource Type:
- Others
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Others
Distinguish yourself by learning to work with text data.
- Subjects:
- Computing
- Keywords:
- Python (Computer program language) Natural language processing (Computer science)
- Resource Type:
- Others
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Others
Use TensorFlow to take machine learning to the next level. Your new skills will amaze you.
- Subjects:
- Computing
- Keywords:
- Python (Computer program language) Machine learning
- Resource Type:
- Others
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MOOC
This course teaches the R programming language in the context of statistical data and statistical analysis in the life sciences. We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R code. We provide R programming examples in a way that will help make the connection between concepts and implementation. Problem sets requiring R programming will be used to test understanding and ability to implement basic data analyses. We will use visualization techniques to explore new data sets and determine the most appropriate approach. We will describe robust statistical techniques as alternatives when data do not fit assumptions required by the standard approaches. By using R scripts to analyze data, you will learn the basics of conducting reproducible research. Given the diversity in educational background of our students we have divided the course materials into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. We start with simple calculations and descriptive statistics. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.
- Subjects:
- Statistics and Research Methods and Mathematics and Statistics
- Keywords:
- Life sciences -- Statistical methods Mathematical statistics -- Data processing R (Computer program language)
- Resource Type:
- MOOC