In his latest book, a leading statistician Dr David Hand explores how we can be blind to missing or unseen data and how, in our rush to be a data-driven society, we might be missing things that matter, leading to dangerous decisions that can sometimes have disastrous consequences.
This is amazing and joins so many dots I didn't know I had. I recommend the full book makes it onto your Amazon wish list or similar as we are almost at Christmas and it would make a great present!!
An awesome feature that most programming languages do not offer.
It certainly is a very useful feature and I feel sad that other languages seem to be so against it when it really does enable some great logic.
The principle that will change how you make decisions.
This is a great rule to follow in most things and I think is a good start in programming too. Read this and have a good think about it.
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There are many branches to AI to learn, but a project-based approach can keep things interesting. Here is a list of 15 such projects you can get started on implementing today.
Some great ideas and definitely worth considering.
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Have a look at diverse applications of Python we have in the real world.
Some great examples of Python use and applications that would be great for providing those pieces of functionality.
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This article demonstrates how to get started using Python in Tableau.
Great code examples that really made it a lot easier to start experimenting with this.
Bloat-free Graphical User Interface for Python with minimal dependencies. DearPyGui is an all-included GUI framework, DearPyGui is as simple as creating a python script.
A great piece of functionality that deserves a bit of analysis in case you can use it in your Python code. I'm certainly glad I read about it.
4 ideas to make you a top-class performer without long study nights.
A great place to go for some new ideas on what you can do to improve. I would suggest some real projects and some online free courses with someone like Coursera or edX.
Set goals, manage your time, learn quickly, and apply immediately.
This is really useful and a must-read and can be applied to beginners and more advanced programmers too. I think the lessons can also be applied to any technology and any language - the main tenets are common to all.
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The key element for success in practical AI implementation is uncovering any issues early on and knowing what aspects of the workflow to focus time and resources on for the best results—and it’s not always the most obvious steps.
I found this really interesting and gives food for thought as well as a great short roadmap for success.
Use Python Dictionaries Like a Pro.
This is crucial for understanding the data structures and can often help you to understand how to relate data items together.
The python modules that every python developer should be familiar with.
I really like NLTK and find it incredibly useful.
The CDMP is the best data strategy certification you’ve never heard of. (And honestly, when you consider the fact that you’re probably working a job that didn’t exist ten years ago, it’s not surprising that this certification isn’t widespread just yet.)
Certainly, a certification to investigate - you might surprise yourself at how close you are to gaining this qualification and yet have not known about it.
How we overestimate the role of Data Science projects in the job process — and the 4 interviewing skills that actually matter.
An interesting perspective here. I have to admit to being just like Haebichan and thinking that projects are enough to show that you can do lots of cool sexy things. I'm going to suggest you need to look at these projects from the customer point of view - what problem are you solving and what are the likely benefits from the result of that project.
This is a great tool that needs to be used. Just change all of the settings via the cog at the top right hand.
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A unique python library that extends the Python programming language and provides utilities that enhance productivity.
I think this could be really useful and so well worth reading and learning.
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This is a rundown of amazing Java project ideas that will set off your career in programming.
Some great ideas. I think one of the most useful points is that as you learn a language you need to do projects all of the time to improve your knowledge of it.
Data science is everywhere. But what are the best ways to learn the field well enough to enter the profession? Read on for some tips and steps on doing so, and some great courses to help you get there.
Follow the link in the article to find could online courses that the authors recommend. Definitely, a must-read for anyone thinking of going into Data Science.
Automate Boring Stuff with Python and Bash For Loop.
This article contains example code and might be a good reminder of the various ways that you can automate in Python.
A quick rundown of some great analytical packages you should be using in Python.
Some great package suggestions here that I think you can use as part of learning Python in order to make sure you learn the right ones first.
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Theories and best practices on effective application logging.
I think logging is critical and a log is just as much a piece of data worthy of reporting against as something containing customer or order information. This is the place where you get clues about what is wrong with the system, where you find out who deleted what, and conversely who added what.
Another option is to also add a flag to every record in a file that signifies if it was added, updated or deleted. Then you don't lose data. Think about that combined with a log to capture the update details.
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