There is still more to explore beyond your traditional SEO knowledge and skills plus experience. You ought also to know that data SEO goes beyond data science, and an upgrade can enhance your team’s output. However, how much do you know about data SEO?
Data SEO revolves around analysis of the data before making search optimization (SEO) decisions. A data-backed SEO approach can help to quantify the quality results of an SEO campaign. However, this is optimized by venturing into other SEO skill sets. What are they?
Like the word analyst, an SEO data analyst leverages the available techniques and tools to solve a problem. He or she can process data, summarize it, provide reports and visualize data. He can visualize and engage in data mining and statistics. A data analyst helps an organization understand data through graphing and reporting.
A data analyst in most cases executes his work based on the data findings of data engineers and data scientists’ algorithm results. Data analysts with programming skills can take advantage of Shiny for R or Dash for Python.
There are varieties of methods, but here are a few;
- Y-axis and X-axis must illustrate measurable data
- Charts must be precise and simple.
- Time- ensure to track time-based data and compare daily, weekly, monthly and yearly results.
- Each dashboard must have relevant summaries to help readers understand them.
- A graph must focus on achievable metrics to avoid wasting time.
You must know that if data analysts, master SQL, they can resort to open source solutions like Superset and Metabase.
The major task of a data scientist is to interpret the data provided by a data engineer in order to be utilized by a company. A data scientist enriches the data with machine learning, analytical approaches, and statistical models. Unlike other players, a data scientist must possess adequate business knowledge and quality programming skills to design new algorithms.
Precisely, a data scientist must be able to communicate scientific data to non-scientists in an effective way.
How they Work
There are different technologies (languages and methodologies) that data, scientists use, and they include; Python, Java, R, Scala, and Julia. You may want to use a language most used by developers in the organization to lower maintenance costs and time. Then you can go in for the technologies you may prefer.
The work of a data engineer is to prep an organization’s mega data infrastructure. A data engineer is commonly a software engineer who designs, builds, and manages data from different sources. Data scientists help companies to access their own data easily.
The common skills and tools utilized include Hadoop, Hive, Pig, MapReduce, SQL, data streaming and programming. An organization needs to centralize its data or otherwise obtain the services of a data engineer because of the following reasons; Comparison is the major reason as to why an organization needs to put up a data infrastructure. Another is integrating data from different sources to ensure security and proper utilization. Also, the need to support SEO consultants’ work.