Forem: Data Professor

How to Master Python for Data Science

Data Professor — Fri, 18 Jun 2021 17:27:04 +0000

Here’s the Essential Python you Need for Data Science

So you’re embarking on your journey into data science and everyone recommends that you start with learning how to code. You decided on Python and are now paralyzed by the large piles of learning resources that are at your disposal. Perhaps you are overwhelmed and owing to analysis paralysis, you are procrastinating your first steps in learning how to code in Python.

In this article, I’ll be your guide and take you on a journey of exploring the essential bare minimal knowledge that you need in order to master Python for getting started in data science. I will assume that you have no prior coding experience or that you may come from a non-technical background. However, if you are coming from a technical or computer science background and have knowledge of a prior programming language and would like to transition to Python, you can use this article as a high-level overview to get acquainted with the gist of the Python language. Either way, it is the aim of this article to navigate you through the landscape of the Python language at their intersection with data science, which will help you get started in no time.

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How to Paraphrase Text using Python

Data Professor — Tue, 08 Jun 2021 07:23:49 +0000

As writers, we often seek out tools to help us become more efficient or productive. Tools such as Grammarly can help with language editing. Text generation tools can help to rapidly generate original contents by just giving the AI a few keyword ideas to work with.
Perhaps this could help end writer’s block? This is a debatable question that is best saved for a later time.

Paraphrasing content is also another great way to take existing content (either from your own or from others) and add your own spin to it. Wouldn’t it be great if we could paraphrase text automatically?

In this article, you will learn how to paraphrase text for FREE in Python using the PARROT library. Particularly, under the hood PARROT’s paraphrasing technology is based on the T5 algorithm (an acronym for Text-To-Text Transfer Transformer) that was originally developed by Google (for more information refer to the T5 resource at Papers with Code). At a high-level, text generation is niche area of the exciting area of natural language processing (NLP), which is generally referred to as artificial intelligence or AI when explained to the general audience.

It should be noted that an accompanying YouTube video (How to paraphrase text in Python using the PARROT library (Ft. Ken Jee)) to this article is shown below on my YouTube channel (Data Professor).

How to Create and Deploy a Machine Learning App to Heroku

Data Professor — Tue, 08 Jun 2021 07:09:54 +0000

Deployment of a machine learning model is an important phase in the data life cycle. Such a model could be a minimum viable product (MVP) that would allow relevant stakeholders access to the model from which to test and experiment with, which could lead to valuable feedback for further model improvement.

Model deployment may seem like a difficult and daunting task but it does not have to be. In this article, you will learn how to easily deploy a machine learning app to the cloud using Heroku. The advantage of deploying to Heroku is that we don’t have to worry about anything related to the underlying operating system (i.e. no more installing updates, dependencies or maintenance) on which the app is running on.

It should be noted that this article has an accompanying video (How to Deploy Data Science Web App to Heroku) that can serve as a supplement or visual aid from which to refer to.

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How to Build an EDA App in Python

Data Professor — Tue, 08 Jun 2021 07:04:09 +0000

Exploratory data analysis (EDA) is an essential and preliminary first steps for exploring and summarizing the main characteristics of datasets. EDA provides the means to help us better understand variables and their relationships. This is achieved by non-graphical (descriptive statistics) and graphical (data visualization) techniques.

In this article, we will be creating an EDA web app that you can use to speed up your EDA analysis or allow your colleagues to perform EDA without having to code in Python. A simple upload of the input CSV file is all it takes to perform EDA analysis.

You can also refer to my YouTube video How to build an Exploratory Data Analysis app using Pandas Profiling on my YouTube channel (Data Professor) as a supplement to this article.

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How to Build a Simple Portfolio Website for FREE

Data Professor — Tue, 08 Jun 2021 06:58:54 +0000

In this article, you will learn how to build a portfolio website for free in order to showcase your projects whether it be for data science, software development or web development. Some of the benefits of a portfolio website helps potential employers see the breadth and depth of your experience, which may be particularly helpful if you’re coming from an unconventional background (such as being a self-taught professional, etc.).

The portfolio website that we will be building today will be hosted for free on on GitHub pages. I will assume that you have minimal to no HTML experience. But if you have prior experience, this tutorial should take you even less time to complete.

We have a lot to cover here and without further ado let’s get started!

Also check out the accompanying YouTube video by the same name (How to Build a Simple Portfolio Website for FREE) that you can watch alongside reading this blog post.

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How to Build a Machine Learning App in Python

Data Professor — Tue, 08 Jun 2021 06:56:41 +0000

Have you ever wished for a web app that would allow you to build a machine learning model automatically by simply uploading a CSV file? In this article, you will learn how to build your very own machine learning web app in Python in a little over 100 lines of code.

The contents of this article is based on a YouTube video (https://youtu.be/eT3JMZagMnE) by the same name that I published a few months ago on my YouTube channel (Data Professor), which serves as a supplementary to this article.

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How to Build your First Machine Learning Model in Python

Data Professor — Thu, 03 Jun 2021 07:45:53 +0000

A while back I wrote a blog on How to Build a Machine Learning Model (A Visual Guide to Learning Data Science) which takes you on a visual and conceptual journey on how a machine learning model is built. What the article did not show was how to implement the actual building of the model.

In this article, you will learn how to build your first machine learning model in Python. Particularly, you will be building regression models using traditional linear regression as well as other machine learning algorithms.

I’ve created the following YouTube video to serve as a supplement to this article, particularly it will get you up to speed on the concepts of machine learning model building as also covered in the first blog post mentioned above.

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Data Science Starter Kit

Data Professor — Thu, 03 Jun 2021 07:35:50 +0000

This article presents you the Data Science Starter Kit that will serve as a self-help guide to help you get started in your data science journey. Nope, I’m not selling you a course. Nor is it going to be a magical formula that will effortlessly instill you with data science knowledge and skills.

This Data Science Starter Kit is going to cost you ZERO dollars (although the learning service providers mentioned herein does). What this starter kit can do for you is provide a framework that will help pinpoint you in the right direction and help you take your first steps.

It’s going to be tough journey. You might even want to give up, but with perseverance and the right mindset you can do this. There’s a lot to cover here and without further ado, let’s get started!

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