Computational Thinking (CT) is a problem-solving process that includes some characteristics and dispositions. It can be used in any industry. It helps you to formulate a problem and to express solutions for that problem in a way that you can use a computer to solve the problem.
The [course_title] course covers the concepts of Big Data MicroMasters program. The core computational thinking concepts including decomposition, pattern recognition, abstraction, and algorithmic thinking will be explained in the course.
Then the course focuses on data representation and analysis and the processes of cleaning, presenting, and visualising data.
Upon completion, you will be able to use computational thinking in data science.
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
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: University of Adelaide.
Course Curriculum
| Introduction | |||
| Welcome to CompX Introduction | 00:02:00 | ||
| Lesson 1 | |||
| What’s data in R all about? | 00:05:00 | ||
| Guided example of components in RStudio | 00:02:00 | ||
| The debrief | 00:01:00 | ||
| Summarising data: Mean, standard deviation etc. | 00:02:00 | ||
| From Jane to you | 00:01:00 | ||
| Lesson 2 | |||
| Visualising relationships | 00:01:00 | ||
| C vs C: Marginal and conditional frequencies | 00:03:00 | ||
| Q vs Q: Strength, linearity, outliers, direction | 00:03:00 | ||
| C vs Q: Shape, location, spread, outliers | 00:03:00 | ||
| Lesson 3 | |||
| Manipulating and joining data | 00:01:00 | ||
| Narrowing data down | 00:03:00 | ||
| Introduction to the dataset | 00:01:00 | ||
| Lesson 4 | |||
| Transforming data and dimension reduction | 00:01:00 | ||
| Why does it work? | 00:03:00 | ||
| Mathematics of PCA – Matrices | 00:03:00 | ||
| Mathematics of PCA – Covariance matrices | 00:05:00 | ||
| Term Frequency – Inverse Document Frequency (TF-IDF) | 00:01:00 | ||
| Lesson 5 | |||
| Population, parameters, samples, and statistics | 00:01:00 | ||
| Explanation of the concept | 00:03:00 | ||
| Estimating the population location and population spread | 00:05:00 | ||
| What is confidence? | 00:03:00 | ||
| The slow way | 00:01:00 | ||
| About k-mers in a genome assignment | 00:01:00 | ||
| Assessment | |||
| Submit Your Assignment | 00:00:00 | ||
| Certification | 00:00:00 | ||
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