
How To Conduct Data Analytics: A Complete Guide for Beginners
Data or information is in raw format. The increase in size of the data has lead to a rise in need for carrying out inspection, data cleaning, transformation as well as data modeling to gain insights from the data in order to derive conclusions for better decision making process. This process is known as data analysis.
Section 1: What is Data Analytics?
Data analytics is the analytical modeling and processing of vast amount of data to derive actionable insights into the data. Data analytics is different from data mining which is an investigation and collection of structured and unstructured data.
Data mining involves the manual labor of looking for hidden patterns in the data that may enable the machine learning algorithm to be useful. In contrast, data analytics involves the scientific process of analyzing data using statistical methods.
A typical data analytics process will have three phases: (1) data preparation, (2) data interpretation, and (3) decision making.
Data preparation involves cleaning of the data which may include filter and transformation of the data.
Types of Data
Unstructured data is, in short, the unordered database of data. It contains scattered information with no logical order. Data is naturally organized and structured. For example, text files and legal documentation are generally well-organized while spreadsheets and video presentations are not.
At DataScienceDepot, we use the most reliable and scalable data management tools and techniques to manage and organize huge and unstructured data sets. Data Analytics and Why is it of Interest to Companies.
Organizing data is the key to retaining and analyzing information. We offer professional data management services that help you to gather and maintain accurate and reliable data, enabling you to process it into useful information. We are always up-to-date with the latest technology so that we can provide high-quality service in a timely manner.
Data Analysis Tools
Data analysts need to carry out a wide range of processes and study the data collected from different sources. These tools or tools are primarily used for data analysis.
Google Analytics
Google Analytics is a data analytics software used to measure various channels for website traffic and user behavior. Analytics software collects and uses a variety of user data to deliver the best user experience to the website visitor.
Facebook uses data analytics to understand user’s behaviour on social media.Facebook uses data analytics for targeted advertising, and uses machine learning to analyze the behaviour and make ads more engaging.
eMarketer
eMarketer is a web analytics software, which creates a report on a website’s visitor.
Process Of Data Analysis
Data is categorized in to main categories of these issues:
Interpretation
Data cleansing
Data transformation
Data modeling
Data analysis
Why is data-driven analytics of interest to companies, analysts are able to collect, organize, manage, analyze and present to users with the desired insights. In the present scenario, businesses are trying to increase customer insights and data collection in order to build an integrated technology system.
Statistics For The Big Data Management
In order to get a detailed insight into the database, the analysts are required to work on the data structure, data type, make sense of the customer preference, and understand the target audience needs.
Conclusion
Data analytics involves the use of software tools, especially SQL-based databases and associated processing techniques to construct an easy to use interface for reading, creating, manipulating, and interpreting a set of data. The data analytics is used to perform data-driven and data-driven applications and processes to gain information, data insights and provide better performance to end-user application.
The advent of big data and advanced analytics has changed the nature of business operations. Answering and building a competitive edge in any market place can now be achieved with just a few clicks with tools like predictive data analytics, data mining, data science and more.