
Explore Python basics for SEOs with practical examples and exercises, using blocks of code in Google Colab and building self-sufficiency before seeking help.
Explore Google Colab basics, including code blocks, uploading csv files, using print and input to collect data, print greetings, and add comments in Python.
Learn to use AI assisted coding with Python for data processing and results presentation, leveraging ChatGPT and Gemini to break tasks into steps and write, comment, and refine code.
Learn python basics for seo by handling integers, strings, and lists, and applying if statements to process input and display results.
Learn to create and access mixed lists in Python, use zero-based indexing, print elements, and apply loops and range to sum numbers and iterate names and letters.
Explore built-in and custom Python functions, including print, input, string, integer, len, max, min, sum, and round, and import time and random for their utilities.
Learn to define and reuse custom functions in Python, taking inputs like a name or age, and producing greetings or drive-eligibility messages through simple, reusable code.
Learn how to count keyword occurrences on a web page using python with requests and beautifulsoup, parsing HTML, extracting text, normalizing to lowercase, and printing the result.
Learn to scrape Google search results for a keyword with Python, retrieving organic URLs, using a programmable search engine API, and printing titles, links, and snippets.
Learn to detect keyword cannibalization with Python by analyzing urls, clicks, and impressions in a csv workflow, and output reports with page titles.
Leverage the Wikipedia API to fetch related entities for SEO keywords, process a CSV of queries, and visualize page views to gauge traffic.
Explore the page speed api to fetch seo-focused performance metrics for desktop and mobile, including CLS, time to first byte, first contentful paint, and LCP, and analyze pages from SERPs.
Learn to find internal linking opportunities with a Python script that crawls up to 100 URLs from your sitemap, ranks pages by keyword relevance, and suggests the top ten targets.
Learn to classify keyword queries into eight answer types—boolean, consequence, instruction, comparison, definition, reason, short fact, and other—by simple Python code that assigns categories and outputs a CSV.
Learn to extract urls from a sitemap with python, including nested sitemaps, and output urls and titles while removing duplicates for automated workflows.
Learn to scrape people also asked questions from search results using Python and Serp API in Google Colab, including API key setup and 100 free searches per month.
Learn to get data from spreadsheets with pandas or the csv module, reading csv files into a pandas data frame and inspecting columns with head, info, and describe.
Break tasks into steps and use a simple python script in google colab to upload a csv with columns date, clicks, impressions, ctr, and position, then plot date vs impressions. Debug data by removing comma separators in impressions to fix a broken graph, and verify search console exports.
Read the csv file bar chart into a data table and plot a bar chart. Label axes, title, save as png, and display; then reuse for a line chart.
Create a custom click curve for your site by binning rankings into ranges, computing CTR per range, and plotting and exporting to Excel with Python, pandas, and matplotlib.
Identify queries with high impressions but low clicks by filtering the top 25% impressions and bottom 25% clicks to uncover optimization opportunities in a content audit.
Create a bar chart of top queries by clicks using a CSV, sort by clicks descending, take the top N, and plot a horizontal bar chart with pandas and matplotlib.
Plot a histogram of position distribution to show how many keywords rank in ranges like 1–2, 2–3, and 3–4 using pandas and seaborn.
Learn to pull Google Trends data with Python using the unofficial Google Trends API, compare impressions to trends, and visualize time frames, regions, and search suggestions.
Forecasting models for seo are explained: linear regression, moving average with a top-down approach, exponential smoothing, and ARIMA. See code demonstrations to predict traffic and visualize forecast uncertainty.
Learn to use the Google Search Console api to pull more data and automate tasks beyond the console, including setting up credentials and testing with sitemap and url inspection.
Collab Notebook: https://colab.research.google.com/drive/1b9-bPFaDlb9PUQNNVzNdvveNA1UUg1nb
Use a pre-trained model and NLTK to extract n-grams from top search titles, count word frequencies, and reveal bigrams and trigrams to optimize SEO titles.
Learn to cluster keywords with tf-idf vectors and k-means in Python, using a file selector and sample 1000 keywords for SEO insights.
Learn to compute keyword relevance to a web page by converting text to vectors with word2vec and cosine similarity, comparing tf-idf and word2vec, and validating results from scraped content.
Explore keyword clustering with Word2Vec embeddings and k-means in Python for SEO. Learn how to choose cluster counts, run the code, and interpret clusters to refine keyword lists.
Learn how to generate meta descriptions from web pages with Python, using two methods: a simple non-llm approach and a Claude LLM workflow, via urls.txt or csv.
Join the recording of a live python coding session for SEOs, where learners copy a Colab notebook, import search console data, and build charts to analyze keyword performance over time.
Never coded before? No problem!
This course is designed specifically for SEOs who are new to coding but eager to unlock the power of Python. You don’t need any technical background, fancy math skills, or experience with programming—just a genuine curiosity and a willingness to learn.
Whether you're an in-house SEO, freelancer, or agency pro, you've probably hit limits with spreadsheets, manual audits, or clunky tools. That’s where Python comes in—and this course is your gateway. We’ll demystify the code, break things down step-by-step, and focus only on what’s truly useful for your day-to-day SEO work.
In just a few hours, you’ll go from zero to writing simple Python scripts, exploring real SEO datasets, and even building small automation tools to solve problems you face every day. You’ll learn through a mix of live demos, hands-on exercises, and practical examples pulled straight from the SEO world—no boring theory, just results you can use.
By the end of the course, you’ll have:
- A solid grasp of Python basics
- A toolkit of reusable scripts tailored for SEO tasks
- The confidence to start experimenting and exploring on your own
Whether you're looking to automate tedious reporting, analyze thousands of keywords in seconds, or just speak the same language as your dev team—this course is your first (and most important) step into technical SEO with Python.