SEO

Writing an SEO optimized blog is important because it can help increase the visibility of your blog on search engines, such as Google. When you use relevant keywords in your blog, it makes it easier for search engines to understand the content of your blog and to determine its relevance to specific search queries.

Consequently, your blog is more likely to rank higher on search engine results pages (SERPs), which can lead to more traffic and potential readers for your blog.

In addition to increasing the visibility of your blog, SEO optimization can also help to establish your blog as a credible and trustworthy source of information. By using relevant keywords and including external links to reputable sources, you can signal to search engines that your content is high-quality and valuable to readers.

5 things to consider for writing a top-performing blog

A successful blog reflects top-quality content and valuable information put together in coherent and comprehensible language to hook the readers.

The following key points can assist to strengthen your blog’s reputation and authority, resulting in more traffic and readers in the long haul.

1. Handpick topics from industry news and trends: One way to identify popular topics is to stay up to date on the latest developments in the data science and analytics industry. You can do this by reading industry news sources and following influencers on social media.

2.  Use free – keyword research tools: Do not panic! You are not required to purchase any keyword tool to accomplish this step. Simply enter your potential blog topic on search engine such as Google and check out the top trending write-ups available online.

This helps you identify popular keywords related to data science and analytics. By analyzing search volume and competition for different keywords, you can get a sense of what topics are most in demand.

3. Look for the untapped information in the market: Another way to identify high-ranking blog topics is to look for areas where there is a lack of information or coverage. By filling these gaps, you can create content that is highly valuable and unique to your audience.

4. Understand the target audience: When selecting a topic, it’s also important to consider the interests and needs of your target audience. Check out the leading tech discussion forums and groups on Quora, LinkedIn, and Reddit to get familiar with the upcoming discussion ideas. What are they most interested in learning about? What questions do they have? By addressing these issues, you can create content that resonates with your readers.

5. Look into the leading industry websites: Finally, take a look at what other data science and analytics bloggers are writing about. From these acknowledged websites of the industry, you can get ideas for topics that help you identify areas where you can differentiate yourself from the competition

Recommended blog structure for SEO:

Overall, SEO optimization is a crucial aspect of blog writing that can help to increase the reach and impact of your content. The correct flow of your blog can increase your chances of gaining visibility and reaching a wider audience. Following are the step-by-step guidelines to write an SEO optimized blog on data science and analytics:

1. Choose relevant and targeted keywords:

Identify the keywords that are most relevant to your blog topic. Some of the popular keywords related to data science topics can be:

• Big Data
• Business Intelligence (BI)
• Cloud Computing
• Data Analytics
• Data Exploration
• Data Management

These are some of the keywords that are commonly searched by your target audience. Incorporate these keywords into your blog title, headings, and throughout the body of your post. Read the beginner’s guide to keyword research by Moz.

2. Use internal and external links:

Include internal links to other pages or blog posts on the website you are publishing your blog, and external links to reputable sources to support your content and improve its credibility.

3. Use header tags:

Use header tags (H1, H2, H3, etc.) to structure your blog post and signal to search engines the hierarchy of your content. Here is an example of a blog with the recommended header tags and blog structure:

• H1: Top 10 Machine Learning books you must give a read6 books to help you learn data science
• H2: Machine Learning: 4 Books in 1
• H2: Mathematics for Machine Learning
• H2: Fundamentals of Deep Learning (O’Reilly)
• H2: Linear Algebra and Optimization for Machine Learning
• H2: The Hundred-Page Machine Learning Book
• H2: R for everyone
• H2: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron

4. Use alt text for images:

Add alt text to your images to describe their content and improve the accessibility of your blog. Alt text is used to describe the content of an image on a web page. It is especially important for people who are using screen readers to access your website, as it provides a text-based description of the image for them.

Alt text is also used by search engines to understand the content of images and to determine the relevance of a web page to a specific search query.

5. Use a descriptive and keyword-rich URL:

Make sure your blog post URL accurately reflects the content of your post and includes your targeted keywords. For example, if the target keyword for your blog is data science books, then the URL must include the keyword in it such as “top-data-science-books“.

6. Write a compelling meta description:

The meta description is the brief summary that appears in the search results below your blog title. Use it to summarize the main points of your blog post and include your targeted keywords. For the blog topic: Top 6 data science books to learn in 2023, the meta description can be:

“Looking to up your data science game in 2023? Check out our list of the top 6 data science books to read this year. From foundational concepts to advanced techniques, these books cover a wide range of topics and will help you become a well-rounded data scientist.”

Share your data science insights with the world

If this blog helped you learn writing a search engine friendly blog, then without waiting a further, choose the topic of your choice and start writing. We offer a platform to industry experts and knowledge geeks to evoke their ideas and share them with a million plus community of data science enthusiasts across the globe.

A list of top machine learning algorithms for marketers that can help to understand trends in user behavior, which further assist with SEO and marketing-based decisions on big data.

The way to advertise and manage your SEO is changing. The tools of the trade for marketers, product managers, and SMBS are ever-evolving. This next wave of MarTech has been ramping up and might put some of us out of business.

We should keep an eye on the cutting-edge machine learning algorithms in marketing and SEO and neural network (AI) technologies being used to make our market assessments more accurate, campaigns more successful, and our customers ultimately more satisfied. However, don’t get too lost in how the algorithms work. Just remember their purpose:

“Is the end-user getting the result they want based on how they’ve communicated their search query?”

Understanding how machine learning algorithms work is critical to maximizing ROI. Here are the top 9 machine learning algorithms that work to influence keyword ranking, ad design, content construction, and campaign direction:

1. Support Vector Machines (SVM)

Classification is the process that facilitates segmentation. Simply put, SVMs are predictive algorithms used to classify customer data by feature, leading to segmentation. Features include anything from age and gender to purchase history and channels used.

SVM works by taking a set of features, plotting them in ‘n’ space  ̶ ̶  ‘n’ being the number of features  ̶, and trying to find a clear line of separation in the data. This creates classifications.

For example, Mailchimp is a popular customer relationship management (CMR) tool that uses its own proprietary algorithm to predict user behavior. This allows them to forecast which segments are likely to have high Customer Lifetime Values (LTV) and Costs Per Acquisition (CPA).

2. Information retrieval

Keywords, keywords, keywords…Sometimes the simplest solutions are the most powerful ones. A lot of machine learning algorithms designed to assess the market can be difficult to comprehend.

Information Retrieval algorithms — like the one that powers Google’s “Relevance Score” metric — use keywords to determine the accuracy of user queries. These types of machine learning algorithms are elegant, powerful, and to the point. This is part of the reason why SEO software such as SE Ranking uses a version of it called Elasticsearch to provide marketers with a list of keywords built using input from the user. The RL algorithm’s basic process follows a 4-step process:

• Get the user query

• Break up the keywords

• Pull a preliminary list of relevant documents

• Apply a Relevance Score and rank each document

In step 4, The Relevance Score algorithm takes the sum of specific criteria:

• Keyword Frequency (number of times the keyword appears in the document)

• Inverse Document Frequency (if the keyword appears too often, it actually demotes the ranking)

• Coordination (how many keywords from the original query appear in the document)

The machine learning algorithm then attaches a score that gets used to rank all of the documents retrieved in the preliminary pull.

3. K-Nearest neighbors algorithm

The K-Nearest Neighbors (K-NN) machine learning algorithm is one of the most basic of its kind. Also known as a “lazy learner algorithm,” K-NN classifies new data based on how similar it is to existing data points. Here’s how it works:

Say you have an image of some kind of fruit that resembles either a pear or an apple, and you want to know which of the two categories it belongs to. A KNN model will compare the features of the new fruit image to the datasets for pear images and the datasets of apple images and based on which category the new fruit’s features are most similar to, the model will sort the image into the respective category.

In a nutshell, that’s how the KNN algorithm works. It’s best used in instances where data need to be classified based on preset categories and defining characteristics.

For example, KNN machine learning algorithms come in handy for recommendation systems such as the one you might find on an online video streaming platform, where suggestions are made based on what similar users are watching.

If you want to learn further how to implement a K-NN algorithm in Python, sign up for a training program to get you started with Python.

4. Learning to Rank (LTR)

The Learning to Rank class of machine learning algorithms is used to solve keyword search relevancy problems. Users expect their search results to populate a page and be ranked in order of relevancy. Companies like Wayfair and Slack use LTRs as part of their search query solutions.

The LTR can be separated into three methods — Pointwise, Pairwise, and Listwise.

Pointwise assesses the relevance score of one document against the keywords. Pairwise compares each document against the keywords and includes another document into the calculation for a more accurate score.

It’s like getting an ‘A’ on a test, but then you notice that the kid sitting next to you got one more correct question than you, and suddenly your ‘A’  isn’t so impressive. Listwise uses a more complicated machine learning algorithm based on probabilities to rank based on search result relevance.

5. Decision trees

Decision trees are machine learning algorithms that are used for predictive modeling. For a marketing analogy, as a user moves through a sales funnel, they’re likely to apply a few criteria:

• Behavior-based triggers – the user clicks or opens a link or field;

• Trait-based values – demographic, location, and affiliation information about the user;

• Numerical Thresholds – having now spent X dollars, the user is more likely to spend ‘X+’ in the future.

The simplicity of decision trees makes them valuable for:

• Classifications and regressions — plotting binary and floating values in the same model (ex. Gender vs. annual income);

• Handling many parameters at once — each ‘node’ in a tree can represent a single parameter without the entire model being overwhelmed;

• Visual and interpretive diagnostics — it’s easy to see patterns and relationships between values.

Word of caution: the more nodes you add to a decision tree, the less interpretive it becomes. You eventually start losing sight of the forest for the trees.

6. K-means clustering algorithms

K-means clustering algorithms are a part of unsupervised learning partitioning methods. In Layman’s terms, this means it’s a type of machine learning algorithm that can be used to break down unlabeled data into meaningful categories.

So, for example, if you owned a supermarket and wanted to divide your entire set of customers into smaller segments, you could use K-means clustering to identify different customer groups. This would then allow you to create specific marketing campaigns and promotions targeted to each of your customer segments, which would translate into more efficient use of your marketing budget.

What makes K-means clustering unique is that it allows you to predefine how many categories or “clusters” you’d like the machine learning algorithm to produce from the data.

7. Convolutional neural networks

Convolutional Neural Networks, or CNN for short, are machine learning algorithms used to help computers look at images the way humans do.

Whereas a human can readily identify an apple when shown an image of an apple, computers merely see another set of numbers and identify an object based on the pattern of numbers that make up the object.

CNN works by training a computer to recognize those number patterns of an object by feeding it millions of images of the same object. With each new image, the computer improves its ability to spot the object.

Now that almost anyone can pull out their phone and take a picture wherever they are, it’s easy to imagine how powerful CNN can be for any kind of application that involves picking out objects from images.

For example, companies like Google leverage CNN machine learning algorithms for facial recognition, where a face can be matched with a name by observing the unique features of each face in an image. Similarly, the CNN machine learning algorithm is being tested for use in document and handwriting analysis, as CNN can rapidly scan and compare an individual’s writing with results from big data.

8. Naïve Bayes

The Naive Bayes (NB) machine learning algorithm is built on Bayes’ famous theorem that determines the probability of two outcomes — the probability of A, given B. What makes this algorithm so ‘Naive’ is that it is based on the assumption that the predictor variables are independent.

For marketers, this can be retooled to determine the possibility of a successful lead magnet, campaign, advertisement, segmentation, or keyword, given that you know the relevant features like height, age, purchase history, or big data concerning your customer base.

If you want to get into math, Great Learning gives a wonderful introduction here. Suffice it to say, that the NB machine learning algorithm answers two questions,

• “Is this the type of person to perform X behavior?”

• “Is this the type of content to achieve X outcome?”

NB is mighty when dealing with large amounts of text-based behavior data like customer chatter online.

Feeding customer dialogue through an NB machine learning algorithm helps predict product and service reviews, measure social media & influencer marketing sentiment for trends, and predict direct marketing response rates.

9. Principal component analysis

Classification leads to evolved segmentations. Principal Component Analysis is used to find strong or weak correlations between two components by plotting them on a graph and finding a trend line.

But what happens when the target market comes with 30+ features? This is where the process of PCA in combination with a machine learning algorithm becomes incredibly powerful for analyzing multivariate big data sets.

Instead of having two groups that correlate, you start to get clusters correlating with one another, where the distance between clusters now suggests strong or weak relationships.

For marketers, the component axes are no longer single features you choose but are determined by the PCA machine learning algorithm.

All of this helps to answer the question: “Which features are strongly correlated and can therefore be used for better segmentation targeting?”

Begin your journey to understand machine learning algorithms