Become an Expert at the Technical Audition. In this article, I’m going to deep-dive into what deliberate practice is and suggest how you can apply deliberate practice to your technical audition preparation.
In Part I, I touched on the idea of deliberate practice¹, a term coined by Dr Anders Ericsson and identified as a method used by many to excel themselves to expertise. In this article, I’m going to deep-dive into what deliberate practice is and suggest how you can apply deliberate practice to your technical audition preparation.
For example sake, I’m going to structure this plan towards a software engineering interview(/audition) and structure the plan to address the main topics covered in Gayle McDowell’s Cracking the Code Interview book. I’ve identified 20 main topics in the book and categorised them into 3 main areas of study — you can adjust the list however you see fit. A list of these 20 topics will be shown later in this article.
I also want to preface and say that this plan is a ‘perfect world’ plan — keep in mind that we don’t live in a perfect world and you won’t always be able to follow a plan perfectly — be prepared to be flexible and adapt as necessary.
Deliberate practice is a combination of two things:
Deliberate Practice = Purposeful Practice + Expert Coaching
So, in order to know what deliberate practice is, we need to understand these two components. Let’s break these two areas down further:
Purposeful Practice is made up of 4 components:
To make the most out of the benefits of offshore software development, you should understand the crucial factors that affect offshore development.
In this article, we explore gradient descent - the grandfather of all optimization techniques and it’s variations. We implement them from scratch with Python.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.
A technical guide on machine learning and 20 challenging problems to look over. This post will provide a technical guide on machine learning theory within data science interviews. It is by no means comprehensive but aims to highlight key technical points within each topic. The problems discussed are from this data science interview newsletter which features questions from top tech companies and will be involved in an upcoming book.