Intro to Computational Thinking and Data Science

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Programming is a crucial a part of knowledge science, as are the underlying ideas of computer systems science. If we plan to implement computational options to knowledge science issues, it’s clear that programming is an absolute necessity. To facilitate these wanting to set up or solidify these expertise, we not too long ago shared an excellent free course from MIT’s Open Courseware to begin with.

After one learns the essential of programming, pivoting to pondering computationally is an effective transition step towards fixing advanced actual world issues, together with from an information science perspective. Today we share Computational Thinking and Data Science, one other prime notch MIT Open Courseware providing freely-available to anybody all for studying.

Computational thinking

 

The course web site describes itself as:

[T]he continuation of 6.0001 Introduction to Computer Science and Programming in Python and is meant for college kids with little or no programming expertise. It goals to present college students with an understanding of the function computation can play in fixing issues and to assist college students, no matter their main, really feel justifiably assured of their capacity to write small packages that enable them to accomplish helpful objectives. The class makes use of the Python 3.5 programming language.

 

The Fall 2016 iteration of this course is taught by Eric Grimson, John Guttag, and Ana Bell. The course is taught utilizing solely Python because the implementation programming language.

The lecture subjects are proven under, taken from the syllabus:

  1. Introduction and Optimization Problems
  2. Optimization Problems
  3. Graph-theoretic Models
  4. Stochastic Thinking
  5. Random Walks
  6. Monte Carlo Simulation
  7. Confidence Intervals
  8. Sampling and Standard Error
  9. Understanding Experimental Data
  10. Understanding Experimental Data (cont.)
  11. Introduction to Machine Learning
  12. Clustering
  13. Classification
  14. Classification and Statistical Sins
  15. Statistical Sins and Wrap Up

I notably like how this course is seemingly cut up into just a few distinct sections. The first part (up to lecture 6) focuses on computational ideas; the following part (lectures 7-10) are statistical in nature; and the remaining lectures make up a closing part on machine studying, although it by no means strays removed from statistics, and appropriately circles again round on the very finish.

This construction provides college students the chance to study these distinct ideas with out complicated them. Thinking computationally has nothing to do with machine studying; it facilitates the separation of an issue into smaller issues and permits one to take into consideration essentially the most environment friendly methods to remedy these smaller issues. It’s an excellent talent to develop in any side of your life or work. However — although not intrinsically linked to machine studying — it does present practitioners with the requisite insights to perceive the internal workings of machine studying algorithms, the options to issues utilizing these algorithms, and how to iterate and enhance on these options to make them extra environment friendly, correct, and helpful.

Statistics is rarely removed from the middle of an information science drawback, or its answer. The dialogue of sampling errors, confidence intervals, and the give attention to understanding each experimental knowledge and the potential misuse of statistical studying outcomes, should not typically given the eye they deserve in an introductory knowledge science course, which units Intro to Computational Thinking and Data Science aside from many others.

Computational thinking

 

The Open Courseware model of this course consists of lectures slides and required information, drawback units, readings (sadly, the course textual content just isn’t free), and — of specific observe — lecture movies. In this sense, the course being freely-offered can actually be regarded as full.

This materials additionally varieties the idea of the edX course of the identical title. If you have an interest in a extra structured studying atmosphere or a verified certificates if you find yourself completed with the course materials, you may enroll there and pursue this feature.

When paired with MIT’s Intro to Computer Science and Programming in Python, these free programs supply a robust begin to somebody studying the basics of programming, laptop science, Python, computation, statistics, and machine studying — lots of the elements to a profitable knowledge science profession.

 
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