Data Science and Data Analysis have become an integral part of Business Intelligence. And to study the productivity of your business, you need to understand the uniqueness of each data point.
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But before that, you need to have a better understanding of Data Science and Data Analytics.
Blending data in a consumable fashion is a bit risky for organizations. As a Data Engineer, your job revolves around providing complex data from multiple sources or performing Advanced Analysis to achieve specific goals.
To achieve the goals, you need some quick way to store such massive amounts of data and employ relevant Data for Analysis. This is why you need a platform that can make the job easy for you. With the powerful tools offered by Sisense, managing data has become convenient for organizations.
Seeing the complexity of data, organizations these days need a more advanced approach to Data Analysis while ensuring the rapid development of their business. This includes proper planning of the data capabilities, right from building a Data Pipeline to self-service BI tools and more. Sisense helps exactly with this by providing powerful tools for simplifying complex data, building data products, and delivering insights for both the inside and outside of the organizations.
Although we have discussed how Periscope Analytics helps businesses, still let’s talk about its benefits in this section:
Sisense employs an AI Algorithm that runs in the background and scans the entire dashboard associated with a particular data model. This Algorithm studies the pattern and behavior of all dashboard users.
With time, the Algorithm picks up more input from user activity and shows more accurate results targeted as per the user’s needs.
Sisense’s Embedded Analytics is an end-to-end solution that is integrated within your application. It lets your customer easily prepare, analyze and visualize complex data.
Sisense’s Embedded Analytics lets you accomplish the following goals:
Sisense’s Embedded Analytics is mainly for product managers and developers.
Sisense supports either static or interactive data visualization. In static visualization, users can have a single view of what’s in front of them while interactive visualization enables users to view different forms of the same datasets selecting any particular dataset.
Have a look at the various types of visualisations supported by Sisense:
Sisense helps you create a dashboard in two ways- the first is through the Sisense Analytics page and the second is through Sisense Rest API.
The difference between both methods is that the Analytics page provides you with an interface where you can put different widgets in the Dashboard.
Step 1: On the Analytics page, click on “+” above the Dashboard list or Right-click on the folder menu and select “New Dashboard”.
Step 2: A window will appear. In the displayed window, click on the Data Set on which you want to work.
Step 3: Enter a title to create a name for the Dashboard.
Step 4: This name appears at the top of the Dashboard list.
Step 5: Finally, click on “Create”. You will then be automatically guided through the process of creating your first Widget in the Widget Wizard.
Step 1: In Sisense, click on “Admin” at the top. After that click on “Rest API”.
Step 2: Next, select Version 1.0 on the top right of the screen.
Step 3: Click on “Dashboards” and then on POST/Dashboards.
Step 4: Define values for different calls as per the requirement.
Step 5: Lastly, click on “POST”. The Dashboard gets added to the Dashboard list on the Analytics Page.
Data Science focuses on finding answers to questions that one does not know even exists. It takes into consideration large sets of raw and Unstructured Data.
The experts use different techniques like Predictive Analysis, Statistics, Machine Learning, and more to parse massive datasets for solving complex business problems.
Data Analytics, on the other hand, is the Statistical Analysis of existing datasets. The main focus here is to find solutions to business problems for better decision-making. The result, thus obtained, can be immediately sent for improvement. Data Analytics focuses on some specific regions having specific goals.
Often, people think Data Science and Data Analysis fundamentally represent and involve the same process. However, this is not true. There is a slight difference between Data Science and Data Analysis, but both are said to be the different sides of the same coin.
Data Science is a broad concept that deals with massive sets of data, while Data Analysis is a part of Data Science.
How these two leverage datasets, is what makes them different. Data Science deals with the construction and designing of new processes, including Prototypes, Algorithms, and Custom Analysis. Businesses use Data Analysis to create a better strategy by examining a large set of data. Thus, it is more specific and concentrated than Data Science.
Bhavik is a seasoned writer in the data industry, renowned for crafting insightful and captivating content on data science. He skillfully combines his analytical prowess with his writing, transforming intricate subjects into easily understandable and engaging material for his readers.