A decade on, big data challenges remain overwhelming for most organizations. Since ‘big data’ was formally defined and called the next game-changer in 2001, investments in big data solutions have become nearly universal. However, only half of companies can boast that their decision-making is driven by data, according to a recent survey from Capgemini Research Institute.
I love that this looks at the challenges and solutions to those challenges.
Data modellers and data architects have distinctly different roles, but they work in a complementary fashion to help enterprises unlock and capitalize on data's business value.
It's a pretty important job definition difference and a key one for people to understand.
How exactly does predictive analytics contribute to healthcare? Which risks hospitals are facing when deploying such tools? Keep reading this article to learn which type of events predictive analytics can reliably forecast.
This was interesting and it is always good to read about real-life uses of techniques.
| Sponsored News from Data Science Central | ||||||||||||||||||||||||||||
|
Intuitive explanations of the most important probability distributions.
This is very useful and well worth a bookmark. It helped me to check and clarify my own understanding of them.
The Digital Speaker and his great conversation with Dan Turchin on how to use AI in an ethical and responsible manner and why AI = Math + Data.
This was a great video of the conversation between them and really a must watch even if you are not interested in AI as a subject on its own.
How to stand out from your peers in the data world.
Some great advice that we could all benefit from no matter what we do.
In this article, Nicholas shows you five ways to load data in Python. Achieving a speedup of 3 orders of magnitude.
This is really useful as I knew there had to be a way to do it faster but this clearly gave me to the right ways to do it faster,
Learn Python regardless of your technical background.
Great links to resources that are very useful no matter how good or bad you are at Python.
Do you speak data? That’s the essential question Gartner poses to data and analytics leaders in promoting data literacy.
Definitely a reminder of some basic skills that we all need if we are going to use data for anything meaningful (which we absolutely should).
An unconventional way Python dictionaries can clean up your code.
I'm not convinced but give it a go - maybe you will be comfortable using this more than I was.
Data product management is growing fast but what does a data product manager do, exactly? Ask five people and you might get five different answers. Here's a good starting point for understanding this burgeoning field.
A great breakdown via themes and a good place to start thinking about this role and what you might like or dislike about doing it. Even if you did not want to become one I'm sure it is likely that you might need to work either with one or use the output from one in your job.
| Data Science Central Webinar Series Event |
|
They can make your daily work easier and faster.
I really like that it contains code segments and that you can see what it can really do. I would suggest doing a bit of playing around and try them - some of them might be able to save you some time or even give you a better result.
Discussing the 17 code blocks that will help you to effectively tackle most tasks and projects as a data scientist
I think everyone needs to print and keep this great article and you can refer back to it.
