Data Analysis with python

Next, you’ll want a good understanding of using Python for data analysis. There are some good resources for this.

To get started, you should take at least one of the free parts of the data analytics learning path on dataquest . Dataquest provides complete learning paths for data analysts, data scientists and data engineers. There is quite a bit of content here, especially the free data analysis links available. If you have some money to learn, you can pay for a premium subscription within a few months. Paid courses provide a great foundation in the fundamentals of data science.

Dataquest Platform

Python for machine learning
If you’ve chosen to pay for a paid data science course on Dataquest, you’ll have a good grasp of the fundamentals of machine learning with Python. Or else there are plenty of other free resources like scikit-learn- the most commonly used Python library for machine learning.

Note: Machine learning is a subfield of Artificial Intelligence that uses algorithms that allow computers to learn from data to perform tasks instead of being explicitly programmed.

Python for machine learningPython for machine learning
SQL is an important skill if you want to be a data scientist, as one of the fundamental processes in data modeling is to extract the data from the start. If you haven’t opted into the full paid Dataquest course, SQL is a few free resources to learn this skill.

Codeacamdemy has a free introduction to the SQL course. This is very practical with transparent in-browser encryption. If you also want to learn about cloud-based database query then Google Cloud BigQuery will be a good choice.

Example :
To be a full-blown data scientist, it’s a good idea to diversify a bit from Python. Codeacademy has an introductory course on the free plan. It’s worth noting here that similar to Dataquest Codeacademy also offers a complete data science learning plan as part of its pro account at its site. The Dataquest course is much more comprehensive but this can be a bit cheaper if you are looking to follow a learning path on a single platform.

Software technology
This will make your code more readable and extensible for both you and others. Also, when you start putting models into production, you’ll need to be able to write good quality code and work with tools like version control.

There are two great free resources for this. Python like you means it covers things like PEP8 style guides, documentation and also covers object oriented programming really well.

scikit-learn’s contribution guides, while facilitating library contributions, actually include good practices. This covers topics like Github, unit testing, and debugging and is all written in the context of a data science application.