Candopi Programming 101 : How to Learn Algorithms

 Candopi Programming101 

HOW TO LEARN ALGORITHMS

PART 1: Selection

1. You select by reputation, looks and feels.

2.You select by running a given code. These days you get about 3000-plus new math research papers every week.
  In biology/medicine field, according Yancopolous(of Rengenron), 60 to 90 % research reports are either irrelevant or wrong! 

PART 2: Learning
These great scientists disagree:

1. I work on/with the paper : Feynman.
2. Pencil-paper simulation is the most painless method to learn algorithms: Donald Knuth

3. No scientist thinks in equations: Einstein.
4. After paralysis, Stephen Hawking worked even better without pencil and paper.: An American contemporary physicist with Hawking
5. With certain types of problems, once a student picks up paper to solve, he loses the ability to solve it : Edgar Dijkstra.

Both groups are right. 

(1) You must run the code on paper. 
(2) But at certain spots, that will get you into trouble (just as Dijkstra warned.) 
Then, you run it in your mind, especially the conditions, corner cases, edge cases. 


REMEMBER the whole field is small. Technology moves super-slowly, as Steve Jobs kept saying.
So if you cannot learn an algorithm today, it means nothing.

Learning it today or learning it 3 months later  or 3 years later, makes ZERO difference.
These algorithms are invented or discovered 1 in every decade. You have time. 

Candopi Programming 101 : What's in it for you?

Candopi Programming101 

BENEFITS:

You can become a software billionaire, a great data scientist or anything in between.

You become ready to explore more, master more.

1. As you can see in my own examples (Java, C++, Python, Ruby in the folders : /7_data_structures_and_algorithms/sorts_python and /7_data_structures_and_algorithms/quick_sort_python), JavaScript you learn here is very close to any other language: Java, C++, Python, PHP, Ruby or any other.
2. Moving to Web development, Mobile development, app development, blockchain development etc, too is smooth and easy.
3. Moving to AI, Machine Learning, Data Science etc, too is smooth and easy. (But you need a bit of math, at least A-level, or US high school level.) e.g. Neural networks, Convolutional networks (CNN), RNN, Capsule networks, One-Shot, Few-Shots etc are also made up of "while" and "for" loops you learn here.

COSTS:

1. The course is very easy, even for primary school students.
2. You don't need any experience.
3. You do zero installation.
4. The course takes just 20 to 40 hours. And zero fees.

IF YOU GET INTO TROUBLE:

1. Say this mantra 3 times: "I'm alright. This person cannot teach. I will find a better teacher, a better book."
   As the great George Polya says, emotions is a big part of math teaching. Or any STEM teaching in general. Blame us. Don't blame yourself. Do you think we are Gods who can never err?
2. Better still, tell us. We can, we will change this a million times for our students' sake. Try us.