Month: November 2017

Becoming a better programmer

You have just learnt the basics of a programming language at school or college or through an online course. You now know the basic components of a program. You are also able to solve some basic problems using small amounts of code.

But somehow when you are writing pieces of codes in professional capacity, you are always making multiple changes in your code and are constantly discussing things over long meetings. Maybe you are not as good a programmer as you thought you were?

I was faced with an exactly the same kind of problem a couple of years back, when i started my career as a software developer working on large code bases and developing pieces of software that would run in production and impact thousands of systems. Fortunately for me, I had the support of extremely patient peers and colleagues who were kind enough to spend some of their valuable time guiding me. These were people who had almost 15 to 20 years of experience writing programs that were efficient, easy to debug and easy to modify with frequent changes in requirements.

In this post I will list a few resources that were recommended by these seasoned programmers and why every programmer should have a look at them as well. Going through these resources surely changed the way I approached the problems and made me realize the immense knowledge that is still to be gained.

Here are some of the recommended readings for anyone who wants to program for a living:

  1. The Pragmatic Programmer by Andrew Hunt and David Thomas
  2. Head First Design Patterns by Eric Freeman and Elisabeth Robson
  3. Structure and Interpretation of Computer Programs by Gerald Jay Sussman and Hal Abelson
  4. Introduction to Algorithms by Cormen, Leiserson, Rivest and Stein
  5. Modern C++ Programming with Test-Driven Development by Jeff Langr

If you are also working as a person working with developing analytics solutions and using machine learning in your work and are interested in a better understanding of the various algorithms that you are working with, then you should also have a look at these books:

  1. Machine Learning by Tom Mitchell
  2. An Introduction to Statistical Learning by Gareth JamesDaniela WittenTrevor Hastie and Robert Tibshirani
  3. The Elements of Statistical Learning by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie
  4. Deep Learning by Ian Goodfellow,‎ Yoshua Bengio and Aaron Courville
  5. Principles of Data Mining by by David J. Hand, Heikki Mannila, and Padhraic Smyth
  6. Modeling the Internet and the Web bPierre BaldiPaolo Frasconi and Padhraic Smyth

The second list of books are not for light reading and require a sufficient amount of devotion. I am still reading some of them even after 2 years and going back to them multiple times for better understanding. However, they are all worth your time and will definitely reward you over the next couple of years as we work on more complex problems and design more sophisticated systems.

Good luck on your journey to becoming a better programmer 🙂

 

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