Learn to Program
Crafting Quality Code

An excellent Python programming course on Coursera is open (deadline-this week)

Coursera is offering Learn to Program: The Fundamentals. It is a Python 3.4.3 based course. I took it in 2013 and found it very beautifully and carefully designed course – sequence of topics, pace of lectures and succinct course materials were great. At that time, it was fifth most popular course on Coursera. Despite my previous basic background in Python, I did learn few exciting new things that I didn’t know before. And it was a great refresher or brush-up for what I already knew. Hope some of you might like to enroll (enrollment deadline is this week)

There is also a 2nd part of this course Learn to Program: Crafting Quality Code that should open soon. It looks like this 2nd part course is not for beginners and requires some basic background in Python. I missed to take this course earlier but based on my very positive experience with part-I course, I am eagerly looking forward to enrolling.

don't document your code using a notebook. document your code using docstring comments.

coursera has a class on "Crafting quality code" https://www.coursera.org/learn/program-code/home/welcome

From coursera and U of T, Learn to Program: The Fundamentals, and
Learn to Program: Crafting Quality Code; and
https://www.coursera.org/learn/learn-to-program https://www.coursera.org/learn/program-code

Planning a course list for undergraduate self study 'degree', and would like your input.

Hello, yet another one planning on Bachelors level studies online with heavy emphasis on machine learning and data science, i've been trying to put together a list of courses for my self to complete (and get a fancy certificate for completed courses) using coursera. So far I've come up with following list:

Learn to Program: The Fundamentals and Learn to Program: Crafting Quality Code (University of Toronto - https://www.coursera.org/learn/learn-to-program / https://www.coursera.org/learn/program-code )

Introduction to Discrete Mathematics of Computer Science (University of California, Sand Diego High School of Economics - https://www.coursera.org/specializations/discrete-mathematics )

Data Science Math Skills (Duke University - https://www.coursera.org/learn/datasciencemathskills ) Introduction to Logic (Standford University - https://www.coursera.org/learn/logic-introduction )

Data Structures and Algorithms (University of California, San Diego, High School of Economics - https://www.coursera.org/specializations/data-structures-algorithms )

Fundamentals of Computing (Rice University - https://www.coursera.org/specializations/computer-fundamentals )

Machine Learning (Stanford University - https://www.coursera.org/learn/machine-learning )

Deep Learning (deeplearning.ai - https://www.coursera.org/specializations/deep-learning )

Software Design and Architecture Specialization (University of Alberta - https://www.coursera.org/specializations/software-design-architecture )

Natural Language Processing (High School of Economics - https://www.coursera.org/learn/language-processing )

Data Science Specialization - (John Hopkins University - https://www.coursera.org/specializations/jhu-data-science)

When it comes to math, physics and possibly electrical engineering I've considered relying purely on khanacademy to fill in the gaps I have at moment.

So here's the main question, is there something you guys/gals can see that is "wrong", is there something that's missing or just would be nice to add on top of that?

Any comments/critique/your opinions are most welcome!

No, but the videos are still up on Coursera.

https://www.coursera.org/learn/learn-to-program

https://www.coursera.org/learn/program-code

There's a two part series by the folks who run the introduction to compsci courses at UofT.

You can also use Lynda for free thanks to UofT whenever you get access to your proper login.

Beyond that, you should check out /r/learnprogramming (and probably the language-specific learning subreddit community too) and see what resources they have in their wiki's and top posts!

I did a lot of python through the MOOC platforms. I even ended up as a volunteer teaching assistant for a while.

If you're open to suggestions I found these two courses particularly good:

https://www.coursera.org/specializations/computer-fundamentals#courses

https://www.coursera.org/learn/learn-to-program https://www.coursera.org/learn/program-code

Here are two free introductory Python courses on Coursera:

• Part 1: Learn to Program: The Fundamentals

• Part 2: Learn to Program: Crafting Quality Code

Break your objective in two parts:

1) Learn python:

https://www.coursera.org/learn/learn-to-program

https://www.coursera.org/learn/program-code

2) Apply python to pentest:

http://www.securitytube-training.com/online-courses/securitytube-python-scripting-expert/index.html

Hi, course difficulty is very subjective but on average the course is not too difficult. The bigger problem that CS students find is trying to balance the work with other courses like calculus and proofs. If you haven't coded before it might be a good idea to check out these courses on Coursera: https://www.coursera.org/learn/learn-to-program and https://www.coursera.org/learn/program-code by UofT. They cover a great deal of the material and it'll ensure you have everything setup and ready :)

Yeah, I was a strong math student, but not exceptional. Mid-high 90s grades in math and top quartile on COMC/Euclid but nothing more. As for CS, I had zero experience except working through these two courses on my own:

https://www.coursera.org/learn/learn-to-program

https://www.coursera.org/learn/program-code

Check this two courses, they are really good: https://www.coursera.org/learn/learn-to-program https://www.coursera.org/learn/program-code Notice that it is meant to take them in that exact order.

https://www.coursera.org/learn/learn-to-program

https://www.coursera.org/learn/program-code

I like this course : Learn to Program: The Fundamentals | Coursera . I've recommended it to several people and for those who stuck with it, they've all come back to say they found it to be a good foundation. It also has a follow up course Learn to Program: Crafting Quality Code | Coursera that extends the knowledge you learn in the beginner course and gently introduces you to data structures and algorithms. Both are taught in Python. You can supplement the above by learning by following along with this Microsoft Python Playlist : Python for Beginners - YouTube .

Why don't you start with learning programming . It will take approximately 1-2months to reach the intermediate level. Here are the courses you can do to learn python from Coursera.org (Go serial wise do course 1 and then 2 and so on)

Course 1 https://www.coursera.org/learn/python

Course 2 https://www.coursera.org/learn/python-data

Course 3 https://www.coursera.org/learn/python-network-data

Course 4 https://www.coursera.org/learn/python-databases

Course 5 https://www.coursera.org/learn/python-data-visualization

Course 6 https://www.coursera.org/learn/learn-to-program

Course 7 https://www.coursera.org/learn/program-code

My (long-term) learning plan

I’ve browsed the sidebar curriculum, David Venturi’s Data Science recommendations, OSSU’s bioinformatics curriculum, the Bioinformatics Career Guide and countless number of posts here, to come up with the below plan. I’m keen to read thoughts/critiques/opinions. The italics are maybes depending on how comfortable I'm feeling at moving forwards.

​

|Title|Platform|Provider| |:-|:-|:-| |CS50's Introduction to Computer Science |edX|Harvard| |Learn to Program: The Fundamentals|Coursera|University of Toronto| |Learn to Program: Crafting Quality Code|Coursera|University of Toronto| |Complete Python Bootcamp|Udemy|Jose Portilla| |Data Scientist with R Career Track|DataCamp |DataCamp | |Data Structures and Algorithms Specialization|Coursera |UCSD | |Statistics in Medicine |Stanford OpenEdx |Stanford| |Foundations of Data Analysis - Part 1: Statistics Using R |edX| UTAustin | |Foundations of Data Analysis - Part 2: Inferential Statistics|edX| UTAustin | |Statistical Learning|Stanford OpenEdx |Stanford| |Bioinformatics Specialization|Coursera|UCSD| |Machine Learning|Coursera|Stanford|

This is will accompanied by Rosalind.info towards the back end.

​

About me:

I’m a clinical haematology/haematopathology registrar (US equivalent of resident/fellow) in Australia looking for an employment and research ‘niche’. My programming experience consists of dabbling with BASIC when I was about 7 years old; I can do a wicked conditional colour formatting in Excel for what that’s worth. My stats knowledge is limited (clinical research and some laboratory applications), though really high school maths is probably a better baseline. In summary, I’m green.

My target is Cambridge MPhil in Computational Biology in 2021/22. Not having a computer science or mathematical undergrad background, I'm aiming to have a solid foundation so this is a 2 year project.

My biggest concern is stats- the OSSU curriculum has college level intro units like algebra and calculus- would these be advisable?

​

Thanks for all input.

I used this in high school, 100% recommend:

• https://cscircles.cemc.uwaterloo.ca/

These might be helpful:

• https://automatetheboringstuff.com/

• https://www.coursera.org/learn/learn-to-program

• https://www.coursera.org/learn/program-code

https://www.coursera.org/learn/learn-to-program

https://www.coursera.org/learn/program-code

Highly recommend these 2 courses from UofT (Toronto), which is top 10 for CS in the world.

Please note that these requirements are for in stream CS kids. It's a mixture of the old CS post courses: CSC148 and CSC165. The goal for these new courses is to give a student a better understanding of how theory and programming should go hand in hand. A lot of it is recycled material from the past so you can find the course notes of 165 here and 148 here. I strongly recommend you take a look during the summer and learn some beginner python here and here. As for difficulty level, personally CSC148 was smooth sailing until recursion, CSC165 until big OH. I think I'm an average student, and I had to study my butt off to get high 80s. So as long as you keep on top of things, you'll be okay :)) 70s are doable! This new system is designed to be less cut throat. Also I believe that CSC110 is gonna be kinda crazy cause it's a year course crammed into a semester. Meaning 6 hours of lecture per week... So don't slack off on that one haha good luck :)

Start here: https://www.coursera.org/learn/learn-to-program then this: https://www.coursera.org/learn/program-code read this: https://automatetheboringstuff.com/

free machine learning course https://developers.google.com/machine-learning/crash-course/?utm_source=google-ai&utm_medium=card-image&utm_campaign=training-hub&utm_content=ml-crash-course

this is meant to be a great course: https://www.coursera.org/specializations/deep-learning

Can't comment on its quality, but it's from 2014 and it shows. Many of the links are outdated. Gonna link to updated ones though there might be better alternatives at this point.

Intro to Computer Science

• MIT 6.00.1x Introduction to Computer Science and Programming Using Python edX or 6.0001 Introduction to Computer Science and Programming in Python (both are updated to python 3. edX is paced, ocw is not)
• Harvard CS50's Introduction to Computer Science (links to newest version of cs50)
• Stanford CS106A - Programming Methodology (their link is broken)
• Stanford CS106B - Programming Abstractions (their link is broken)

Mathematics

• MIT 6.042J Mathematics for Computer Science (newer course)
• ArsDigita Discrete Mathematics (includes more than just the videos)

Programming

• University of Toronto Learn to Program: The Fundamentals (what their link redirects to)
• University of Toronto Learn to Program: Crafting Quality Code (what their link redirects to)

Theory of Computation

• ArsDigita Theory of Computation (includes more than just the videos)
• Rice Principles of Computing (Part 1) and Part 2 (their link was broken)

I'm gonna stop there because this is getting tedious and the next two links are fine. My point is that although these courses are probably still great courses, I know CS50 is the general recommendation if you don't have any specific request and 6.0001 and 6.042J are recommended a lot, there may or may not be better courses for many of them and many of the links don't work anymore or are not the best link to the content. I wouldn't say don't use it, but maybe google every course before you take it.

Python

Learn to Program: The Fundamentals - https://www.coursera.org/learn/learn-to-program

Learn to Program: Creating Quality Code - https://www.coursera.org/learn/program-code

Complete Python Bootcamp: Go from zero to hero in Python 3 - https://www.udemy.com/course/complete-python-bootcamp/

The Python Mega Course: Build 10 Real World Applications - https://www.udemy.com/course/the-python-mega-course/

Machine Learning

Machine Learning - https://www.coursera.org/learn/machine-learning

Applied Data Science with Python Specialization - https://www.coursera.org/specializations/data-science-python

Machine Learning A-Z™: Hands-On Python & R In Data Science - https://www.udemy.com/course/machinelearning/

Deep Learning A-Z™: Hands-On Artificial Neural Networks - https://www.udemy.com/course/deeplearning/