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1. #Miniconda postgresql install how to#
2. #Miniconda postgresql install install#
3. #Miniconda postgresql install software#
4. #Miniconda postgresql install code#

#Miniconda postgresql install software#

While most software engineers use pip, most data scientists like conda. Python has two main package managers: pip and conda. This means that we need a tool like conda if we want to use anything other than vanilla Python (e.g., tools for plotting, numpy, pandas, etc.).

#Miniconda postgresql install install#

This is necessary because unlike a language like R where you can install packages with the install.packages() command, Python doesn’t have an internal tool for installing packages. The first thing you’ll likely want to do on any computer you work with is install both Python and the package manager conda. Every note you find in these readings I put there because either I ran into a problem or one of my students has, so please take your time and try to be very methodical! Installing Python with Miniconda ¶ In addition, this will give us a chance to learn a little about how the command line works, which will be really important to effective troubleshooting.īut that’s a lot, so let’s take things one step at a time! First, let’s install PYTHON! Reading These Setup Instructions ¶Īs you work through these set up readings, be certain to follow the directions very carefully! As a data scientist you are working at the frontier of software, which often means that there are little quirks and issues with the tools that we use that are just waiting to trap you.

#Miniconda postgresql install code#

With that in mind, most Duke MIDS courses have decided to coordinate around VS Code to allow you the opportunity to get really, really good at VS Code. But even more importantly, I think everyone who works in data science would agree that more important than picking the “correct” editor is becoming proficient in whatever editor you use.

Visual Studio Code (VS Code): There are a lot of opinions (most strongly held) about what editor is “best.” My own view is that what editor is best depends entirely on not just the kind of work you do and your own working style, but also what the people around you use (nothing better than being able to ask ther person next to you for help!). Python and the conda package manager: This is a Python-centric course, so the first thing we’ll need to do is install Python and a robust, data-science-appropriate package manager. To set ourselves up for this course (and hopefully our careers!), we’ll need to set up the following things: That will probably mean you’ll get a little annoyed at the fragility of many of these tools, and you may get frustrated spending hours trying to find a setting that got set wrong (though we’ll try to minimize these experiences!), but try to think of this time not as wasted, but instead as part of your data science education! What We’ll Be Setting Up ¶ So in this course, we’re going to address environment setup head-on. Moreover, it means you may not know enough about how data science tools work to debug problems on your own when they come up. For example, if the MIDS Python Bootcamp included a module on setting up Python environments instead of providing you with a clean virtual machine, you’d probably end up learning ~25% less programming!īut the problem with this approach is that if every course you take pursues this strategy, you may find that you don’t feel empowered to go do data science yourself when those clean VMs are taken away at the end of the semester. But it is a skill that takes time and energy to learn, and so in most classes - which want to focus on teaching topics like statistical analysis or programming concepts - instructors choose to provide students with clean, ready-to-use environments so everyone can focus on those topics.

#Miniconda postgresql install how to#

Getting data science tools installed and working together is, for better or worse, a pretty core part of the day-to-day life of data scientists, and learning how to troubleshoot problems quickly is an important skill for being productive in the profession. Why deal with all the headaches of setting up your own environment, you may ask? Why not just use a cloud platform like Google Colab or a virtual machine with everything already set up? One of the major learning goals of this class is for you to be comfortable managing all the software and settings required for you to do data science on your own computer. Setup Python and miniconda Setup Python and miniconda Contents.