Growing your developer career: four core skill areas

August 6, 2018

Introduction

It is such a joy to receive an email and then suddenly I found that I am reading a good article. Here is the link from the code pointers.

I like the blog and it is so good to read so many topics.

My notes

Developer skills is the multiplication of four areas, fundamentals, technology and shipping experience, soft skills.

Foundation vs technology

Related to myself, I just stopped subscription of pluralsight.com and fronendmaster.com last month since I like to focus on fundamentals of computer science, algorithm and data structure. I choose to work on more on fundamentals instead of technology for the time being.

Shipping (experience)

Let me copy the list of examples here from the blog:
Importance of good logging
Risk/ value tradeoffs when adding dependencies
Long term maintainability of a code base
Value of good API design and documentation
Not over or under designing
The value of simplicity

Related to my experience, I learn how to have a good API design and documentation as a solo programmer since Nov. 2016. I started to ask questions on codereview.stackexchange.com, and got feedback on my coding style, C# language skills, and other problem solving area. Small things like English writing and make the question as simple as possible.

The value of simplicity is my top rating one. I understand how important it is for me to keep thing simple. I have to cover so many areas as a full stack web programmer, I know that my time is limited and I have to make tradeoffs.

Soft skills

For some reason, it is easy to neglect developing your soft skills at some stage. Don't fall into that trap.

I like to copy the advice from the blog, areas you should pay attention to:

Communication
  Listening
  Presentation
  Writing

Leadership
  Empathy
  Influence
  Integrity

Team work
  Conflict resolution
  Collaboration
  Motivation

Time management

Let me think about three areas related to soft skills.

I like to add a personal story here.

Learn to appreciate what you have

I like to explain to appreciate what I have, a small career holding on my hand called a programmer lasting eight years working for a Canadian manufacturer company. It takes so much effort for me to be independent, and also learn to contribute the economy through my current job, I went through so many years education and research in United States.

So many people helped me in the past, specially in United States, Florida from 2000 to 2010, I do not want to let those people down. I also like to try to do my best to contribute something in Mathematics, Science and computer science. There are so many things I like to work for and make the society much better to live and enjoyable.

“I have learned the secret of being content in any and every situation, whether well fed or hungry, whether living in plenty or in want” (Philippians 4:12, NIV). A spirit of thankfulness makes all the difference.

Algorithm study: 95 algorithm videos

April 5, 2018

Introduction

It is very surprising that those videos are very well prepared by a facebook engineer in Chinese language. I really like to learn something from the teaching.

Here is the video feed. Here is the author's profile on leetcode.com.

10 most favorite algorithms

I like to choose my 10 most favorite algorithms and write down some notes here. I like to study some algorithms so that I can prepare some good questions to be a mock interviewer for a peer who prepares for Google phone interview second round.

I just could not believe that I had last 2 mock interviews as interviewer, and two peers are preparing for Google onsite in a week. I can tell how good they are to prepare for the interview.

Lintcode 79: longest common substring

July 26 2016

Work on lintcode: longest common substring.

Problem statement:

Given two strings, find the longest common substring.

Return the length of it.

Note

The characters in substring should occur continiously in original string. This is different with subsequnce.

Algorithm Study

Study the blog - longest common substring (60 minutes reading first time/ 20 minutes review every 6 months) written by a facebook engineer, Ider Zheng. Julia likes the article written in Chinese, it is a well-written and very good thinking process about the problem solving.

Julia likes to repeat the process here in her own words.

1. Brute force solution -> from O(n⁴) to O(n³), great analysis in the above blog:

Good thinking addes value to your coding practice:

Warmup with a brute force solution:

Time complexity - O(n⁴)

For example, a string s1 = "abcdefg",

One way to think about brute force:

The length of string is 7.

How many substring in s1? Guess?

Substring - start position and end position, two variables. Each one has O( n ) choices. Total is O(n²) choices.

More detail, start position can be any i from 0 to 6, and then end position starts from i to n-1. The variation formula, Sum = (n - 1) + ( n - 2) + ... + 1 = (n-1) n / 2, so the total is O(n²);

Try to reduce brute force variation from O(n²) to O(n), instead of letting start position and end position
both varies, just work on start position only.

Small improvement based on brute force solution

Time complexity - O(n³)

Second way to think about using brute force:
The start position of substring is from 0 to n-1, so considering the start position, start a new search.

So, the total of search is O(n).

 Longest common substring - start from start-position, and then  compare both of chars are equal, if yes, continue, record length and compare to maximum length, else then break the search.

1. C++ code:

Code is from the blog written by a facebook engineer.

The time complexity is O( n³ ). There is duplicated calculation.

Will write C# practice very soon.

Dynamic programming - optimal time complexity O(n²)

2. Use Dynamic programming, in the above blog, the analysis is very helpful.

Work on dynamic programming, improve time complexity from O(n⁴) or O(n³) to O(n²), using memorization, space O(n²), bottom up approach.

The idea is to find the formula of DP - dynamic programming.

table T(i, j) - common substrings, using end position as a variable.


one is in s1, ending at position s1[i];
one is in s2, ending at position s2[j].

We know that if T(i, j) >0, then, s1[i] = s2[j];
if(s1[i+1] == s2[j+1]), then, T[i+1, j+1] = T[i,j]+1,
otherwise, T[i+1,j+1] = 0.

Will think about to put together a graph here to explain the idea as well.

2. C++ code:


From blog: C++ code
DP solution, time complexity O(nm), space O(nm)


3. further improvement: C++ code
DP solution, time complexity O(nm), space O(nm) -> O(n+m) -> O(1)
because the recurrence formula tells us that the current position only relies on diagonal position - left-up corner ( i - 1, j - 1)
https://gist.github.com/jianminchen/0061dcf562bd0bdb091301241c38730f

From blog

Julia's practice:
1. brute force solution C#
A. first writing, static analysis catching 1 bug, left 2 bugs for debugging. (not so good!)

B. fix all bugs - final presentation: C#

2. Dynamic programming solution using C#:


Highlights of code writing and execution:

1. static analysis - find bugs
change made: line 56 - 61, if the longest common substring is with length 1 and also start from row 0 or col 0.

line 67 - 72

2. Test case failed on line longestCommonSubstring("abc1def","1ghijkl") - should be "1",

change made: move end1 variable to line 49, and set variable from line 56 - 61, line 67 - line 72

2nd version: add comments


3. DP solution with space reduction: O( nm ) -> O( n+m )  -> O( 1 )

practice later.

* Design issues:
         *
         * 4 variables - memo, longest, end1, searchFirstRowCol
         * 1. memorization using two dimension array - memo
         * 2. variable int longest - get maximum length
         * 3. variable int end1    - string s1 - end position - s1's substring end position
         * 4. variable bool searchFirstRowCol - check first row and first col to update maximum length

DP problems:

Follow up after 8 months

March 17, 2017

1. Read code review on longest common substring algorithm.
2. Read wiki article about "Algorithm Implementation/Strings/Longest common substring"
3. Blog formatting to make it more readable. 
4. Code review:

Ashton and String Hackerrank

Coding practice is like sports - I don't feel fear when I am on court. That's where I feel at home.

I don't feel fear when I am on court. That's where I feel at home #CreateYourMark #RG16 @adidastennis @adidasFR pic.twitter.com/ZVNcZvWZE0

— Kristina Mladenovic (@KikiMladenovic) May 20, 2016