Computational thinking is an actual thought process that can be applied to data science. Learning how you can apply computation thinking strategies will help in improving your data computation and analysis skill through learning a specific programming language here.
This [course_title] will help you understand the role of computational thinking in solving related data science problems. After taking this course, your ability in programming and problem-solving will be improved.
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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- Add the certificate to your CV or resume and brighten up your career
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Course Credit: MIT
|Lecture 1: Introduction and Optimization Problems||00:41:00|
|Lecture 2: Optimization Problems||00:48:00|
|Lecture 3: Graph-theoretic Models||00:50:00|
|Lecture 4: Stochastic Thinking||00:50:00|
|Lecture 5: Random Walks||00:49:00|
|Lecture 6: Monte Carlo Simulation||00:50:00|
|Lecture 7: Confidence Intervals||00:50:00|
|Lecture 8: Sampling and Standard Error||00:47:00|
|Lecture 9: Understanding Experimental Data||00:47:00|
|Lecture 10: Understanding Experimental Data (cont.)||00:51:00|
|Lecture 11: Introduction to Machine Learning||00:52:00|
|Lecture 12: Clustering||00:51:00|
|Lecture 13: Classification||00:50:00|
|Lecture 14: Classification and Statistical Sins||00:49:00|
|Lecture 15: Statistical Sins and Wrap Up||00:45:00|
|Submit Your Assignment||00:00:00|
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