Learn Python

On this page, I have summarized the blogs written for "Back to Basics" series and additional content that I think is the best out there to start as a beginner on your journey to become a pro and create trading robots/algorithms and conduct advanced data analysis.

So, let's get started 😀

  • When googling on how to install Python, every beginner stumbles upon various methods to do it. Still, for people involved in analyzing large datasets, it's best to use the Anaconda distribution since it comes with pre-packages libraries optimized for that and had the power of conda, read this article to find out more.

  • After you have installed Python the recommended way, what next? You need to understand how you can install additional packages like APIs from stock-brokers where you can access Historical and Live Market Data for your algorithms and even place live orders through your algorithm in your brokerage account, read this article to find out more on how to install packages properly.

  • Once you know how to install packages, before getting on all fired up and creating your first program, it's essential to understand the concept of virtual environments. Why is that? Because when you write code, your code can too large to handle, and it's best to learn how to manage your code from the beginning itself; read this article to find out more about virtual environments.

Okay, I hope you now understand things a little bit better and getting the hang of it; if you are not, don't worry at all, there is a considerable learning curve involved at the start, it is a steep climb, but once you are through - it all starts making sense. 🌄

Additional Content to get started! (My curated list)

I will split this into two sections, one for the free content list and second for the paid content list, which I absolutely recommend as well; a lot of free content out there is indeed perfect, but sometimes you have to pay for that extra quality.

Free Content

  • A fantastic beginner course by freeCodeCamp will talk about all different data types, data structures, if-else statements, loops, functions, and much more.
  • Now, we are in the business where we need to analyze big datasets, so we should know how to work with those datasets and visualize them by creating interactive charts; after all, this data analysis helps us understand the fundamentals behind our algorithms. Another fantastic course by freeCodeCamp

Just so you know, a lot of content regarding data types and structures will overlap in the above two videos. However, it's good to still go through them to retain more information and listen to different interpretations.

Clarification: I haven't been sponsored by freeCodeCamp for this post; I am recommending them because they are literally on a mission to teach everyone to code! Check their website out here.

  • The below course helped me learn Python's basics, it's by Jose Portilla. Jose has mastered the art of teaching, and at the time of writing this article, 1.3M students have already enrolled in this course. I highly recommend this introductory course. This has 22 Hours of content to watch.
  • Again, similar to the free content, there is very good paid content to learn how to deal with large datasets, analyze and visualize them. This course is again by Jose, and he makes it really simple for you to understand very complex problems. At the moment, 165,000 students have enrolled in this course. This has 21 Hours of content to watch.

Clarification: Neither Udemy nor Jose has sponsored this post. The recommendations are purely mine based on my experience.

Now, honestly, you can choose either the free or the paid content, I have taken all the courses I mentioned above, and they all have helped me somehow. If you are starting out, I would recommend Jose's course; Udemy runs a sale every month and sells these courses for as low as $10/£10.

If you do all of this, you will be pretty much ready to tackle any problems on Python; of course, you cannot know it all, but a simple Google search will give you all the answers; guess why? Because Python has one of the largest communities and people are very helpful to each other.

And again, practice practice practice!!!

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