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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.

Assessment

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.

Certification

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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

Course Curriculum

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
Assessment
Submit Your Assignment 00:00:00
Certification 00:00:00

Course Reviews

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