Computer science and programming are two interrelated topics that will help you understand the role of programming and computation in solving computer-related problems. Learning the concepts of these topics will help you accomplish your goals in life.
You will be able to provide programming problem solutions after taking this Computer Science and Programming Diploma. After this course, you will notice that your confidence in writing small programs will be increased and improved drastically.
This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.
Edukite courses are free to study. To successfully complete a course you must submit all the assignment of the course as part of the assessment. Upon successful completion of a course, you can choose to make your achievement formal by obtaining your Certificate at a cost of £49.
Having an Official Edukite Certification is a great way to celebrate and share your success. You can:
- Add the certificate to your CV or resume and brighten up your career
- Show it to prove your success
Course Credit: MIT
|Lecture 1: Introduction to 6.00||00:41:00|
|Lecture 2: Core Elements of a Program||00:50:00|
|Lecture 3: Problem Solving||00:48:00|
|Lecture 4: Machine Interpretation of a Program||00:50:00|
|Lecture 5: Objects in Python||00:51:00|
|Lecture 6: Recursion||00:49:00|
|Lecture 7: Debugging||00:50:00|
|Lecture 8: Efficiency and Order of Growth||00:50:00|
|Lecture 9: Memory and Search Methods||00:48:00|
|Lecture 10: Hashing and Classes||00:45:00|
|Lecture 11: OOP and Inheritance||00:50:00|
|Lecture 12: Introduction to Simulation and Random Walks||00:50:00|
|Lecture 13: Some Basic Probability and Plotting Data||00:43:00|
|Lecture 14: Sampling and Monte Carlo Simulation||00:51:00|
|Lecture 15: Statistical Thinking||00:55:00|
|Lecture 16: Using Randomness to Solve Non-random Problems||00:50:00|
|Lecture 17: Curve Fitting||00:51:00|
|Lecture 18: Optimization Problems and Algorithms||00:50:00|
|Lecture 19: More Optimization and Clusterin||00:53:00|
|Lecture 20: More Clustering||00:49:00|
|Lecture 21: Using Graphs to Model Problems, Part 1||00:50:00|
|Lecture 22: Using Graphs to Model Problems, Part 2||00:49:00|
|Lecture 23: Dynamic Programming||00:54:00|
|Lecture 24: Avoiding Statistical Fallacie||00:50:00|
|Lecture 25: Queuing Network Models||00:53:00|
|Lecture 26: What Do Computer Scientists Do?||00:50:00|
|Submit Your Assignment||00:00:00|
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