As aspiring Data Scientists, curiosity drives us everywhere we can find new exciting knowledge. A well told data story will keep us sat for hours and we all know that there are several top (say a number) blogs, newsletters, and websites dedicated to data science, I am as well an avid enthusiast of this wonderful area, thus this is my attempt to share a subjective top 6 blogs for beginners, with no order in particular. The list will be spiced with an article found in each of them, which I thought either it was worth of sharing or made me reflect, and for that reason the articles chosen are mostly non technical.

Data Science Dojo

A one week bootcamp, full immersion program, it is a mostly R based school, which is pleasant to see, considering Python is becoming predominant in the field. We can find some good articles where writers develop interesting ideas about different themes, and make their articles easy to read, I saw they were full of information. There is a reason for such amount of well though posts, and that’s because authors have the chance to be eligible for a free spot in the bootcamp.

SELF DRIVING CAR ETHICS

Ethics in artificial intelligence is a topic gaining massive exposure due to its importance in self driving cars for example. When it comes to choosing who the probable victim in a accident would be, some questions come to my mind: How much autonomy is allowed?, Do we need to audit algorithms?, How do we audit algorithms?, and What about explainability?. Hopefully, after a long, well thought debate, a fair solution can be developed, but as all matters related to ethics, it is not
going to be easy.

No Free hunch – Kaggle

The kaggle blog is quite important, some of the people writing are skilful kagglers sharing tips, so it would be difficult not to acknowledge its influence on the community. In kaggle some guys who start successful paths on the competitions gain visibility, build good portfolios, and afterwards are hired by important companies, which is just an example of why we should keep an eye, or both,
on this blog.

HELP! I CAN’T REPRODUCE A MACHINE LEARNING PROJECT!

Wow! many people will relate to this, I remember working with my group in a predictive modelling project, and of five members, two of them were doing most of the work. I was not one of them, thus my job was to check what they were doing and to try to reproduce it, in order to understand what was going on, and to make some notes; sadly this task was not easy, some of the parameters they chose were missing when I was trying to replicate or project, hence this article resonates with my own experience. But it also goes deep into many different issues you could find during your own projects. It suggests some actions to take if you want to avoid frustration. Overall I think is an enjoyable piece of advice.

Data Science Central

What we find in this site is that it supplies a wide spectrum of topics like data visualisation or analytics and it is convenient for people approaching an intermediate level. Themes are a bit deep, but I appreciate the level of expertise shown in the blog.

MBA VS. DATA SCIENCE QUALIFICATIONS: DOES #AI AND #DATASCIENCE EXPLAIN THE FALL IN MBA APPLICATIONS?

Is eye-opening to realise how traditional advanced programs are taking an impact from artificial intelligence, even when job automatisation has not even reached such replacement level. Middle management has not been automatised but yet its possibility has an enormous influence in the MBA education market right now. I certainly agree with the following “Today, for many people, AI and Data Science qualifications offer alternate avenues to career progression.”

KD Nugget

If what you are looking for is a place where you can find several uploads per week, you can find it here, and it is also one of the most refereed sources of content. This intense yellow colour will definitely catch your eye, and the huge repository behind it will do the same with your brain, don’t be afraid to dive deep into it. So it was somehow difficult to pick just one article, but here it goes.

DEVOPS FOR DATA SCIENTISTS: TAMING THE UNICORN

If the picture doesn’t blow your mind, wait until you read the post carefully. When my interest in data science began, there was not a definition for it, at least not one being commonly known, unlike now. It was the intersection of several skills and areas of expertise, surely many of us want to master them fast. To add more skills like linking production environments and data science projects would be of huge help to any team, and this post explain it in detail.

Code Mentor – Data Science tutorials.

Lets say that you’d like find someone expert in data science that could help you and let you see a bit of their work. Well, these articles were written for you: you can look for topics of your interest and usually not only the expert is sharing his knowledge, but is also willing to offer his time to advise you in this sort of market place.

UNDERSTANDING DIFFERENT COMPONENTS & ROLES IN DATA SCIENCE

Who doesn’t get confuse with the roles and fields related to data science? This post gives us a clear view of what differs from lets say from a data engineer to a data analyst. It shows the purpose of each of the roles across every step in a project, which can help us as well to understand how it gets done, and if we lean towards any role or we feel that one of them fit us well, then learn more deeply about it.

Dataquest blog

Dataquest is a learning platform for Python and data science, so when you want to learn concepts, have some well crafted approach to how to become a data scientist or let the knowledge flow into your brain, DataQuest is definitely the place where you will find the essentials. Shall we take a look at some basics?

One of the most useful blogs whenever I look for topics to build my portfolio. DataQuest is an online learning platform, thus they have collaborators who know what data science students are looking for. That was my case when I needed some hints on the Lending Club dataset, trying to figure out a classifier to predict default rates.

UNDERSTANDING REGRESSION ERROR METRICS

Is refreshing to read a well written summary of different types of errors in linear regression, often times when we analyse or model data, the focus is stressed on the predictive part, on the outcome, and it is a good approach, but we must not forget about the basics. To be able to understand them crystal clear and being able to use them with no doubt, make us feel more comfortable with our results. With that said, blog posts directing their attention to these details are always welcome and as everyone has their own interpretation on the topics, it is very important to diversify what we read.

Do you think there are more blogs I should include on my list? Let me know why you think so. And if you have any thoughts related to the posts I shared, please shoot!