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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 [course_title]. 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.

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Course Credit: MIT

Course Curriculum

Module: 01
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
Module: 02
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
Module: 03
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
Module: 04
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
Module: 05
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
Certification 00:00:00

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