In this article, we are going to discuss model planning for data analytics in which we will cover all procedural steps one by one.
Model planning is phase 3 of lifecycle phases of data analytics, where team determines methods, techniques, and workflow it intends to follow for subsequent model building phase.
During this phase that team refers to hypothesis developed during discovery, where they first became acquainted with data and understanding business problems or domain area.
Common Tools for the Model Planning Phase :
- It is basic strength is the ease with which quality plots can be developed including mathematical formulas where needed.
- most famous use of SQL is as a base infrastructure to build its dashboards which are easy to use along with reporting tools.
- To create and interact with databases more rapidly, SQL has been adapted into a variety of tools, each with its own niche market including Microsoft Access and PostgreSQL.
- It is easily accessible, can be used to make complex models and quick analysis, and offers a deep ability for data manipulation.
- The SQL server monitoring section of application manager is in a table format which makes it easy to switch between live data screens and access of analytic features.
- Wide access to data through an intuitive interface, without considering that where it is stored.
- Easily access data with minimal knowledge of the data or the SQL required to surface it.
- Work effortlessly with seamless interfaces to loaders and utilizes without having detailed knowledge of each loader.
- Improved efficiency with basic storage options, including materialistic views, temporary tables and partitioned tables.
Tableau Public –
- It is a freely available software that connects to any data source to corporate web-based data.
- It allows access to download the file in different formats.
- The data can be shared through social media.
- This is a very good data source if anyone wants to see the superiorness of tableau.
- It is a programming environment and language for data manipulation.
- SAS is easy to manage, access and is used to observe data from various sources.
- SAS organizes modules for web, social media, and marketing analytics used broadly in the customer prospect.
- It is also used to predict the customer’s behaviours and communications.
- It is a strongly integrated platform in data science that performs the predictive analysis.
- It contains advanced analytics like data mining, machine learning without any programming.
- The tool is very powerful that can generate analytics based on real-life data.
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