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#Windows or mac for data science how to#
#Windows or mac for data science install#
#Windows or mac for data science upgrade#

My main laptop from 2011 to 2016 was a MacBook, so I know its limitations very well. They are intended for professionals from the design field and music producers, like photographers, video and photo editors, UX/UI, and even developers who don't need to run heavy workloads, like Web Developers. Apple MacBooks: for a variety of reasons, you should avoid an Apple laptops unless you really (and I mean, really) love OSX.If you are reading this post and belong to a middle to big sized organization, the best option for you is probably reaching a leasing agreement directly with the manufacturer. Many of these second hand markets can provide warranty and an invoice (in case you are a company). Many of these laptops end in the second hand market. Its handicap is the price, but you can find lots of second hand Thinkpads in very good using conditions as many big corporations have leasing agreements and dispose laptops every 2 years. Thinkpads are excellent professional laptops we have been using for years and never failed us. I have an second hand P50 I bought for 500€ which meets all features listed above: david-laptop specs My personal recommendation is getting a second hand Thinkpad workstation laptop.

#Windows or mac for data science upgrade#

Possibility to upgrade its capabilities, like adding a bigger SSD, more RAM, or easily replace battery.

A SSD of at least 256GB should be enough.

Make sure your laptop can handle it without melting. You are going to run workloads for at least hours. Remember that you can't train serious Deep Learning models from scratch in a laptop. It will be orders of magnitude faster than almost any CPU for that task. Only if you need to prototype or fine-tune simple Deep Learning models.

It will save you a lot of time while processing data for obvious reasons. This is the most important feature as it will limit the amount of data you can easily process in memory (without using tools like Dask or Spark).

Do not need to break your bank account to buy expensive hardware and software.

Allows to cover the full spectrum of Data related tasks, from Small to Big Data, and from standard Machine Learning models to Deep Learning prototyping.

Most libraries just work out of the box with little extra configuration.

#Windows or mac for data science install#

Standard Data Science tools like Python, R, and its libraries are easy to install and maintain.

Why this guide? Over time, we found many students and fellow Data Scientists looking for a solid environment with some fundamental features: This is the standard setup both Pedro and me use at WhiteBox. Never missed a single feature while using it. I have been using this setup for more than 5 years with little changes (mainly hardware improvements), in many companies, and helped me in the development of dozens of Data projects.

#Windows or mac for data science how to#

In this post I would like to describe in detail our setup and development environment (hardware & software) and how to get it, step by step.