Useful links for code newbies who want to study more than coding
Tags: learning, mooc, beginners, codenewbie
Published: 2019-05-25 00:16:00

---

title: Useful links for code newbies who want to study more than coding
published: true
description: A list of links for study other than coding what will help you become a better programmer.
tags: learning, mooc, beginner, codenewbie
cover_image: https://images.unsplash.com/photo-1483736762161-1d107f3c78e1

---

*I didn't plan to write a post like this. I wrote an e-mail to my friend to share links in my bookmark, then when I hit send button, realized I put a bit excessive effort on it to share with only one person.*

---

## 1. Algorithms
  * [Khan Academy](https://www.khanacademy.org/)
I think you might know this one. I heavily relied on this one since I was studying 12-grade math. They cover have pretty much all topics I have learned at my college's Discrete mathematics course. There Algorithms course is also amazingly easy and good to get a taste of the rough concepts.

  * [Intro to Data Structures & Algorithms in Python | Udacity](https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513)
Another astonishing tasting course in case you haven't studied JavaScript that is used in Khan Academy's Algorithms course. This one is recommended [vie DEV Community](https://dev.to/ljuneaul/review-of-algorithmic-toolbox-24go) by [Doaa](https://dev.to/dmahely).

  * [Algorithms, Part I | Coursera](https://www.coursera.org/learn/algorithms-part1)
This one also has been recommended by [Andrew Healey](https://dev.to/healeycodes) [vie DEV Community](https://dev.to/ljuneaul/review-of-algorithmic-toolbox-24go).
+) I just found that they have [booksite](https://algs4.cs.princeton.edu/) which is including synopses of their famous textbook.

  * [Project Euler](https://projecteuler.net/)
You can solve mathematical problems with cording in the language of your choice. Your solution mustn't take more than 1 minute - which means that it should be a good algorithm since the range is really big most of times. Hackerrank has [ProjectEuler+ contests](https://www.hackerrank.com/contests/projecteuler/challenges).

## 2. Big Data
  * [Machine Learning | Coursera](https://www.coursera.org/learn/machine-learning)
This is another my to-do list for summer. I heard that Andrew Ng not only has a great reputation around the area but also an amazing teacher.

  * [Mathematics for Machine Learning Specialization | Coursera](https://www.coursera.org/specializations/mathematics-machine-learning)
The title is self-explaining.

  * [IBM Data Science Professional Certificate | Coursera](https://www.coursera.org/specializations/ibm-data-science-professional-certificate)
Seems like a really beefy practical course. I hope there will be enough time for me to finish this one during the summer. [Review](https://cognitiveclass.ai/blog/data-science-professional-certificate/)

  * [Data Science and Cognitive Computing Courses](https://cognitiveclass.ai/)
This site seems like having pretty much everything.

  * [DataCamp](https://www.datacamp.com/)
I haven't tried this one yet, but it seems like a great place to practice.

  * Free Statistics E-books
*I won't leave any comment on them since I haven't read any of them yet.*
    * [Collaborative Statistics](http://cnx.org/content/col10522/latest/)
    * [OpenIntro Statistics](http://www.openintro.org/stat/)
    * [Probability and Statistics for Programmers](http://greenteapress.com/thinkstats/)
    * [Introduction to Statistical Thinking](http://pluto.huji.ac.il/~msby/StatThink/index.html)
    * [Introductory Statistics](http://www.saylor.org/site/textbooks/Introductory%20Statistics.pdf)

## 3. UX
  * [UX Design Techdegree | Treehouse](https://join.teamtreehouse.com/ux-design-techdegree/?utm_source=techdegree-A)
I started a free trial last week and seriously hooked up with them. All of their tech degrees seems really well-structured. I don't like the fact that it's not self-faced though (the minimum duration is 3 months due to the limitation of submitting assignment 1 project/w).

  * [Interaction Design Foundation courses](https://www.interaction-design.org/courses)
[Florian Prz](https://dev.to/flrnprz) have recommended it on the comment. It has the whole variety of courses, and also provide with the guideline for each specific job role where to start and where to go.

## 4. Computer Science
  * [Build a Modern Computer from First Principles: From Nand to Tetris](https://www.coursera.org/learn/build-a-computer)
I can't find a better way to address its category. It's pretty much about low-level 'stuff' such as assembly and compiler. I am really glad I've taken this course since my school doesn't touch anything low-level including C and C++. You can my review [here](https://dev.to/ljuneaul/review-of-build-a-modern-computer-from-first-principles-nand-to-tetris-mma).

  * [Godel, Escher, Bach - An Eternal Golden Braid](https://archive.org/details/GEBen_201404/)
I can't believe there is a free e-book of this. I even googled if it is a legal site and it was. I bought this book twice. I almost ten years ago bought an English paperback first since I heard that the translation of this book in my mother tongue is horrible. I even split this book into chapters to finish reading but couldn't finish it. it was too challenging for my level of English. While I was struggling for several years, the publisher retranslated this book last year to celebrate 20th of celebration, so I bought it again. This book is not just about computer science but the recursive mechanism through the whole universe. Actually, the first reason I started to read this book was that Escher was one of my favourite artists. This book is not super easy or light, but it is elegant, brilliant, joyful, and kind.

  * [Open Source Society University](https://github.com/ossu/computer-science)
[Florian Prz](https://dev.to/flrnprz) have recommended it on the comment. This is a serious curriculum that requires 2 years of full-time commitment to complete everything. Really hope I have known it early. I am surprised that the initial foundation is done by few people.

  * [Harvard university](https://online-learning.harvard.edu/catalog)
Also recommended by [Florian Prz](https://dev.to/flrnprz). Their CS50 course is really great. At the time I took it there was only one core one and it was a bit challenging from the halfway so I couldn't finish it through the end(I thought it's why it's Harvard they taught new language almost every 2 weeks), but it was really good and fun. Now they have a whole variety of courses, so I think it will be less hard and more interesting.

  * [MIT OpenCourseWare](https://ocw.mit.edu/index.htm)
Another recommendation by [Florian Prz](https://dev.to/flrnprz). I did a part of [Mathematics for Computer Science](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/) before. The lecture was really great and it helps me understand the whole concept and goal of Discrete Math.

---

> All of the materials in this post(maybe except GEB) are meant to be for introductory including statistics e-books.

> Please feel free to add anything you also want to recommend of opinion about any of them on the comment!