Top 5 Bookmarks Every Data Analyst Should Have by Josh Berry via @TDataScience

Online tools to save you time & effort.

These are handy whether you are a data analyst, data engineer, or any other data-related role.

8 Essential Python Techniques for Data Engineers and Analysts (with code samples) by @camwarrenm via @TDataScience

These are the Python code snippets he re-uses the most.

Something to bookmark for sure. I'm sure we all have a basic toolset of code scripts in various languages that we use and edit every time we need to do something.

10 Python operations for 70% of your Data Analysis by Anmol Tomar via @Medium

Day to day work of a Data Scientist involves a variety of tasks such as data pre-processing, data analysis, machine learning model creation, model deployment.

This is so good I almost feel as if we need to give a commission to Anmol every time we run this kind of code!

A Guide to 14 Different Data Science Jobs by Nate Rosidi via @kdnuggets

The field of data science is growing into one that features a variety of job titles This guide reviews different positions available for you to consider if you have a data science background.

Definitely worth a read as there are some great jobs out there that are NOT a data scientist but probably suit you and your skillset much better. I think I am closer to a data engineer a lot of the time, but not exclusively.

6 steps to creating data management as its own business department by Robert Vane via @infomgmt

Despite the repeated and justified calls from data governance and management practitioners to take a more holistic view, businesses generally still view data as a project.

Some great points and observations in this article which help to explain why this is really important.

SLIDESHOW: 7 top challenges to working with data by David Weldon via @infomgmt

Data pros are dealing with a skyrocketing amount of data, created and gathered by ever-more devices. Here are the top challenges this is creating, according to a new study by Nexla.

From my own perspective these are a good list of pain points to the use of data. I would add to this  list:

1.. Data Sources - do you know the best place to get your data from - there could be better alternatives do get the data from.

2. System of Record - related to 1. make sure you understand where your data really comes from and if the data is clean and pure of has been altered in some way.

3. Change control - I've been using a systems data to feed in some of the data I was using, but they have missed it in their change control and I've suddenly had different or no data arrive.

4.  Data Management - are fields with the same name really the same?

Why analytics will be the next competitive edge by Gary Cokins via @infomgmt

Organisations are drowning in data but starving for information. The application of data science is becoming commonly accepted, but will senior executives realise it?

I would add to this article that the data has got to be clean and of a consistent quality so that any analysis or decisions you make based on the data are correct. If you can't rely on the data or it is of a dubious quality you are better off not using it.

Top Big Data Skills To Help You Stand Out from the Crowd by Sarah Shannon via SmartDataCollective

Big Data is the latest buzzword hitting the technology sector with data analytics fast becoming the newest technique implemented by businesses to monitor their IT networks, and stop impending threats.

Definitely something to read and work out which skills you might be missing or would add to your offering if you worked on it.

19 top paying Internet-of-Things jobs by Bob Violino via @infomgmt

The Internet of Things remains one of the hottest trends in technology. Here's how the demand is translating to salaries for experienced professionals.

Good if you want to know what skills and roles you need to aim towards in your own career.

7 must-have traits for a successful data team by Daniel Mintz via @infomgmt

The need for technical prowess is a given, but there are also several personal qualities that analysts should have as a group.

Interesting set of skills.  I think the trick is to find a way to balance the team in such a way that they not only complement each other with skills, bu as in temperament so they really do gel and identify themselves as a team.

Historians versus futurists – the battle of analysts? by David Cokins via @infomgmt

Some analysts dig deep into historical information to glean insights once hidden. Others are obsessed with predictive analytics and Big Data to foresee the future.

I really like this opinion piece and think it is worth thinking about the two faces of analysis and what you are currently doing.

5 Career Paths in Big Data and Data Science, Explained by Matthew Mayo via @kdnuggets

Sexiest job... massive shortage... blah blah blah. Are you looking to get a real handle on the career paths available in "Data Science" and "Big Data?" Read this article for insight on where to look to sharpen the required entry-level skills.

I LOVE this article, it also have some great articles off the links in it.  I recommend reading this so that you can work out which role you are more like and what you need to do in order to improve your skills.