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Introduction to ChatGPT SEO Prompts These ChatGPT SEO Prompts are highly useful prompts for every person who does search engine optimization (SEO) for his/her own website or on the job. Before moving to these prompts let's have a look at some very simple guidelines about h ow to use these ChatGPT SEO Prompts: Replace "URL" with your website link Replace "article link" with the link to the article which is published on your website Replace "keyword" with your target keyword Replace "article title" with the title of the article for which you want to do SEO Replace "number" with the appropriate integer Replace "article" with the content of the article you have whether published or not Replace "niche" with your niche 41 ChatGPT SEO Prompts 1. Create SEO strategy for "URL" 2. Suggest a list of keywords for "URL" website to target for SEO 3. Suggest blog title ideas for "URL" 4. How to opt...

Introduction to Google Colaboratory Google Colaboratory is a product that allows anyone to use Python through the browser. Google Colaboratory is a long name so Google gave it a short name Colab to make it easy for users. Few things about Google Colaboratory: Google Colaboratory is developed by the Google Research team and it is particularly well suited to machine learning, data analysis, and education.  Google Colaboratory let everyone create and execute any Python program through the browser.  Technically speaking, Google Colaboratory is a hosted Jupyter notebook service that offers free access to computer resources, including GPUs, and requires no setup to use. That means you do not have to pay to use it. Limitations of Google Colaboratory Resources provided by Google Colaboratory are not guaranteed but they are unlimited with some variations, which means that its usage limit varies. Google says that this non-guarantee and variation in the usage limit is imposed because it ...

Image by Author I used only 3 prompts to convert an unstructured data column in Excel to a structured column. First prompt Give me a function that will extract only email addresses from a cell and make sure it removes all the other text and special characters Output:  =TRIM(RIGHT(SUBSTITUTE(LEFT(A1, FIND(“ “, A1&” “, FIND(“@”, A1))-1), “ “, REPT(“ “, LEN(A1))), LEN(A1))) This function resulted in <emily.jones@example.com>, Second prompt The function you provided is working but it also extracted special characters <, >, and , that are associated with the email value but I do not want these special characters so now create a formula that remove only these characters from < emily.jones@example.com >, Output:  =FILTERXML(“<t><b>” & SUBSTITUTE(B1, “<”, “”) & “</b></t>”, “//b”) This function gave me emily.jones@example.com>, Third prompt The function you provided remove only < from < emily.jones@example.com >,...

Python vs. R for data science: In data science, Python and R are the two most widely used programming languages and thousands of people around the world question which one they should use. Photo by Pixabay In this article, I am breaking down the demand for these two languages based on LinkedIn search results that were carried out in two to three days. This search was done for Python and R jobs worldwide and in the top 5 countries. It was impossible to differentiate the demand based on data science only, so I considered all jobs (Mostly developer jobs, specifically data science jobs, or data science-related jobs). Since Python and R are widely used in data science, we will get similar results even if we consider all jobs. This breakdown covers the Python vs R demand based on the following aspects: 1. Python vs R Worldwide demand 2. Python vs R Demand in Top 5 countries 3. Python vs R Demand based on Exp Level – Internship, Entry Level, Associate Level, Mid-Senior Level, D...