DEV Community: Diana Nadim

5 SEO Tasks to Automate with Python

Diana Nadim — Tue, 27 Apr 2021 16:51:41 +0000

You might have heard already that it’s all about automation now. Businesses adopt new technologies as soon as they are released to help with big data, research, testing, and checking. These tasks can be done by a whole team a business owner has to hire, pay, and spend time on. However, there’s an easier way that every professional developer knows about.

Automating repetitive tasks for your business with Python is one of the best things you can do. On the official website, you can even find scripts to make the process easier.

Here, we’ll overview the 5 easiest tasks you can automate with this open-source programming language. There won’t be any scripts here not to confuse the beginners. However, you can find most of the information on the website of the language.

Keep in mind that if you have a 3.0 version or newer, additional tools should be used for the scripts to run properly. You can also use libraries for faster results, but let’s start with the basics.

Why Python?

This is a programming language that is very easy to learn. Even a beginner can help with process automation due to the simplicity of the syntax of Python. It’s easy to read and write, which makes the risks of mistakes lower.

The language is used in data analysis, search/crawling, natural language processing, and now, in SEO as well. It’s a popular instrument in process automation, especially in the analysis of your website, SEO strategies, written content, and more.

So, if your team is tired from doing the same tasks all over again, it’s time to try automating at least one of the following processes. It will help you see issues with indexing and rating and recommend suitable adjustments at the end of the analysis.
Now, let’s review the 5 tasks you can automate right now.

Task 1: Your Website’s Links Status Analysis

During the link building process, you should pay attention not only to the donor website’s quality but to the state of your own links as well. Python is great with finding broken links, which may be difficult to do manually.

You should always check for damaged links on your site because they may drop your search engine rating and traffic quite a bit.
The script that can tell you how well your links feel is called Pylinkvalidator. You may need additions for it if you’re using the 3+ version. If you own a 2+ version, no add-ons are needed. To speed up the process, you’ll need lxml and other additions.

However, it’s worth trying the script without them to see how well you can work with it. They are also non-essential if you have a relatively small source.
If it’s a large website, add-on libraries are must-haves.

Task 2: XML Sitemap Making and Analysis

A proper XML map of your website shows search engines top-priority pages of the source that need the quickest indexing and ranking in order to provide value. Sometimes, when the map is too confusing, a page or two may be lost from the engine’s sight. But what if those are the most important ones?

To check which pages are indexed and which aren’t, you need that map. And if you have a large website like an online store with thousands of pages, it will take months to check each one. Regular XML maps mix pages of all priorities, which may confuse you even further.

Python comes to the rescue here as well! It will help you create a map with strategically important pages on it (bestsellers, most popular posts, etc.). As soon as an issue occurs, you’ll detect it and will be able to fix it in the shortest time.

Task 3: General SEO Analysis

Analyzing your SEO strategies with Python will take less time and provide clearer results. A script will see what your specialists may miss, like a missing keyword or title.

The language has a script called SEO Analyzer which investigates your website and gives results on:

Your keywords;
Missing metadata;
Lost alt-text for an image;
Every page’s word count (it’s important for SEO as well);
Warnings, etc.

This is a great tool if you need a general analysis without going too deep until you find out where to go. Sometimes even a small meta-data problem may make a very important page invisible for Google spiders.

So, if you have issues with ranking your pages, maybe the problem is in the title or the poor choice of keywords. Do SEO analysis first and then use other tools to improve your research.

Task 4: Comparison of the Website’s Visibility with the Competition

Many businesses want to simply see where they are compared to their competitors. Difficult terms and strategies aren’t their main priority; they need results in an easy-to-understand form.
To analyze the gap between the companies, you can turn to SEO giants like SEMrush and get info there. Then, you’ll have to add it to the Excel file and organize to see patterns. In case there are many opponents or non-branded keywords, such analysis will take days to complete. Not to mention there’s a considerable margin of mistake.

Python scripts can do it in a very short time, providing comprehensive data on the traffic difference and similar keywords. You’ll also see the gaps you may have in the content of the website that should be filled to catch up.

The process will take a couple of hours instead of days or even weeks. You also won’t need a full team of experts to run the script.

Task 5: Deep Learning for Intent Categorization

Deep learning makes machines do similar tasks to the human brain much faster and more effectively. These algorithms can be used in SEO for processes like the intent categorization of keywords.
For many purposes, you may have to create categories with the main and additional keyword by intent:

See;
Care;
Think;
Do, etc.

The process used to be manual. It would take weeks, especially if it’s a large online store or another big source. Thanks to AI technologies and Python scripts, you can automate categorization. It will take a couple of hours if not less.

The language is most commonly used behind deep learning in this case because it has an extensive number of libraries. It’s also accepted by most academics studying and working on AI, deep learning, big data, etc.

So, if you have an opportunity to avoid analyzing thousands of keywords yourself, trust technology.

Use Technology to Your Advantage

New technologies help businesses elevate to new levels. Especially nowadays, when it’s crucial to have your company online in the first place by the focus keyword, automation is the key to success.

Python is easy enough to learn and handle yet effective enough to provide quick analysis without human effort. The scripts we presented above will cover more than the basic needs of your business clients.

A crucial thing to do after the analysis is damage control, for which Python may also be very effective. Don’t miss a chance to let technology help you achieve results. And recommend your clients to use the scripts to implement those adjustments.

7 Great Resources for Learning Data Science Online

Diana Nadim — Wed, 21 Apr 2021 10:43:32 +0000

Thanks to technology, we can learn skills and knowledge in any field. It’s a fantastic opportunity, and we should use it!
Do you want to become a data science expert? There are many platforms out there where you can find relevant learning materials to study and practice.

We have hand-picked the best resources for learning data science online. Check them out!

Data Science for Everyone Course by DataCamp

If you are a newbie in the field of data science, enroll in a free course Data Science for Everyone designed by DataCamp. This course is an introduction to data science. It includes 15 videos and 48 exercises and doesn’t involve coding.
Videos and exercises will help you learn more about the different data scientist roles, the fundamental principles of A/B testing, and the concepts of time series analysis and machine learning.
Upon completing this course, you will understand how data scientists extract knowledge and insights from real-world data and how they further use them.

The Data Science Course 2021 at Udemy

Udemy offers lots of great courses for programmers, and it might be challenging for you to pick the one that is right for you.
If you don’t have previous knowledge in the area of data science, we recommend you enroll in the course called The Data Science Course 2021: Complete Data Science Bootcamp.
It’s a comprehensive course that includes 28.5 hours of on-demand video, 90 articles, and around 500 downloadable resources. The course involves coding – you will start coding in Python and learn how to use this programming language for statistical analysis.
Also, you will learn some basic principles of how to improve machine learning algorithms by studying training, validation, n-fold cross-validation, and testing.
The only requirements for this course are that you need to install Anaconda and Microsoft Excel.

Data Science Course by SimpliLearn

If you have already chosen data science as your career path, consider getting an industry-recognized Data Scientist Master’s certificate from SimpliLearn.
This Data Science course was developed in collaboration with IBM, and it includes 220+ hours of live interactive learning.
As a student, you will be exposed to advanced technologies including R, Python, machine learning, Tableau, Hadoop, and Spark. You will interact with practitioners (in an online mode) and participate in real-life industry projects.
On top of that, you will get an opportunity to attend exclusive hackathons and enjoy live interaction with IBM leadership.

Data Science Specialization Course at Coursera

At Coursera, you can get free access to Data Science Specialization that includes ten courses starting from the basic Data Scientist’s Toolbox course to Developing Data Products course. This specialization program is offered by Johns Hopkins University.
What will you learn from these courses? You will learn how to use R to clean, analyze, and visualize data. Also, you will acquire practical skills that will allow you to perform regression analysis and use regression models.
Another benefit of this educational resource is that it will familiarize you with GitHub and give you a better understanding of data science project management.

Data Science Course by Harvard Extension School

In case if money is not an object for you, you can use learning materials offered by Harvard and obtain Data Science Graduate Certificate.
At Harvard Extension School, you will learn the key concepts related to data investigation, such as data wrangling, cleaning, sampling, management, exploratory analysis, regression and classification, prediction, and data communication. Top-tier instructors will introduce you to numerical algorithms, parallel computing, simulation, and analysis.
You will become an expert in such subject areas as game theory, statistical quality control, exponential smoothing, seasonally adjusted trend analysis, and data visualization.

Analytics Vidhya

Analytics Vidhya is a data science community that unites 725K members and more than 500 companies worldwide. At this platform, you can find not only free courses but also interesting-to-read blog posts and information about the upcoming hackathons.
Here is a short list of free Analytics Vidhya’s tutorials that you may found helpful:

A Complete Tutorial to learn Data Science in R from Scratch
A Complete Tutorial on Time Series Modeling in R
Complete R Tutorial To Build Probabilistic Graphical Models

Non-technical reading

To become a successful data scientist, you don’t really need to learn programming languages 24/7. As a student, you should find time for additional reading in order to expand your worldview. You should read articles, essays, research papers, and non-technical literature to get ideas on how you can apply data science to solve real-life problems.
Here is a non-technical reading list for data scientists:

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O’Neil
Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are by Seth Stephens-Davidowitz
Algorithms to Live By by Brian Christian and Tom Griffiths
Superforecasting: The Art and Science of Predictionby Philip E. Tetlock and Dan Gardner
The Signal and the Noise: Why Most Predictions Fail – but Some Don’t by Nate Silver
How Not to Be Wrong: The Power of Mathematical Thinking by Jordan Ellenberg.

Dev.to

If you want to boost your learning efforts, you should join the online community of data scientists and programmers.
Dev.to is a platform where you can read blog posts written by IT specialists, listen to podcasts, and participate in discussions with other experts in the field.
It’s a place where you can gain extra knowledge, make new connections, and learn more about the real challenges, struggles, and achievements of modern data scientists.

In conclusion

Don’t wait for “the right moment” to start learning data science. Make the first step today. Check out the resources listed above and immerse yourself in the learning process.
The sooner you will take this step, the sooner you will accomplish your dream and become a data science pro.

BIO: Diana Adjadj is a tech writer, researcher, and blogger. She worked for edtech and biotech companies for four years. Now she is a freelancer. Diana is passionate about advanced technologies, data science, and digital marketing.