David Asboth - Data Solutions & Consultancy

How long is a year?

I recently attended my first dedicated Python conference - PyCon UK in Manchester.

In her book The Gardener and the Carpenter, Alison Gopnik talks about how parents should think of raising children as gardening, not carpentry.

Aspiring analysts want to know how data analysis is done.

One of the most common questions I get from students near the end of a data science course is "what next?".

I'm at an interesting crossroads in my career.

How to use your impostor syndrome to learn anything I have a theory that data science is a field where impostor syndrome is particularly rife, more so than other fields.

Citizen Data Science This was a popular phrase a few years back.

You know how there are lots of blog posts out there about machine learning algorithms "in plain English"?

This post originally appeared on my blog in 2017 I'm a big fan of the quiz show QI.

This post originally appeared on my blog in 2017 I've often read, and agreed with, the advice that side projects should be solving problems or answering questions that you yourself are interested in.

This post originally appeared on my blog in 2017 The "Best FIFA Football Awards" took place recently and while the final outcome was not surprising, I've always wanted to know which countries vote for which players.

When you work with pandas, you'll often perform multiple operations on a DataFrame.

Interpretability is often an important concern with a machine learning algorithm (despite spellcheck telling me it's not even a word).

From what I can tell, the biggest difference between data science curricula and data science job postings is usually knowledge of SQL.

In Part 1 I described the k-means clustering algorithm and some of its uses along with a quick Python implementation.

This is the first of a two-part post on K-means clustering.

I read an article today called Data Scientists Need More Automation.

In an attempt to bridge the gap between the two disciplines of programming and data science I will occasionally talk about programming concepts useful for data scientists, and vice versa.

In most data science and machine learning tutorials you typically encounter csv files.

Markov chains are a popular way to model sequential data.

When you're a data scientist, unless you're in a job that's very research-focused (and likely requires a PhD) you'll mostly be using machine learning algorithms invented anywhere between say 5 and 30 years ago.

As most data scientists quickly realise, there's a difference between the kind of data science you do while learning about it, and the kind you do at a real job.

I read an article on Machine Learning Mastery recently about getting started with machine learning.

In Part 1, I introduced the concept of Self-Organising Maps (SOMs).

Introduction When you learn about machine learning techniques, you usually get a selection of the usual suspects.

This post originally appeared on my blog in 2016 Introduction The Problem A few months ago I was playing one of my favourite games of 2015 - Fallout 4.

This post originally appeared on my blog in 2016 Introduction London is expensive.