Introduction to computational thinking and data science

Bachelor's degree

In Maynard (USA)

Price on request

Description

  • Type

    Bachelor's degree

  • Location

    Maynard (USA)

  • Start date

    Different dates available

6.0002 is the continuation of 6.0001 Introduction to Computer Science and Programming in Python and is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language.

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Location

Start date

Maynard (USA)
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02139

Start date

Different dates availableEnrolment now open

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Subjects

  • Computational
  • Play
  • Programming
  • Materials

Course programme

Lectures: 2 sessions / week, 1 hour / session


Recitations: 1 sessions / week, 1 hour / session


6.0001 Introduction to Computer Science and Programming in Python or permission of instructor.


This subject is aimed at students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems. It also aims to help students, regardless of their major, to feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class will use the Python programming language.


6.00 satisfies all degree / minor requirements that can be satisfied by taking both 6.0001 and 6.0002.


Students taking 6.00 will attend the 6.0001 and 6.0002 lectures and do the problem set for 6.0001 and 6.0002. The 6.0001 final will serve as a 6.00 midterm. The 6.0002 final will serve as the 6.00 final.


OCW has additional versions of 6.00 that include useful materials; this course will closely parallell the material covered in these versions:


The textbook is Guttag, John. Introduction to Computation and Programming Using Python: With Application to Understanding Data. 2nd ed. MIT Press, 2016. ISBN: 9780262529624. [Preview with Google Books] The book and the course lectures parallel each other, though there is more detail in the book about some topics. It is available both in hard copy and as an e-book.


A significant portion of the material for this course will presented only in lecture, so students are expected to regularly attend lectures.


Recitations give students a chance to ask questions about the lecture material or the problem set for the given week. Sometimes, new material may be covered in recitation. Recitation attendance is encouraged but not required.


Each problem set will involve programming in Python. There will be 5 problem sets in the course.


There will be one final exam. The exam is open book / notes but Not open Internet and Not open computer. Please print whatever you may want to use during the quiz.


Grades will be roughly computed as follows:


Problem sets will be graded out of 10 points. Submissions that do not run will receive at most 20% of the points.


Note: Finger exercises are not available on OCW.


We do not grant any extensions. Instead, we offer late days and the option of rolling at most 2 problem set grades into the final exam score.


At the beginning of the term, students are given two late days that they can use on problem sets. Starting with Problem Set 1, additional late days can be accumulated for each assignment, one late day for each day the assignment is turned in ahead of the deadline. Up to three late days may be accumulated in this fashion in this course, i.e., you can only have a maximum of 3 late days at any point in time. Late days are discrete (a student cannot use half a late day). The staff will keep track of late days and feedback for each problem set will include the number of late days the student has remaining. Any additional late work beyond these late days will not be accepted. To avoid surprises, we suggest that after you submit your problem set, you double check to make sure the submission was uploaded correctly.


As we assign final grades, we will maximize your score based on the choice to roll the weight of at most two problem sets into your final exam score. If rolled, the percent that the problem sets are worth will be rolled into the final exam score. We strongly urge you to see the late days and dropping the problem sets as backup in case of an emergency. Your best strategy is to do the problem sets early before work starts to pile up.


Problem set 1 due


Problem set 2 out


Problem set 2 due


Problem set 3 out


Problem set 3 due


Problem set 4 out


Problem set 4 due


Problem set 5 out



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Introduction to computational thinking and data science

Price on request