Last updated on January 23rd, 2024 at 11:19 am
Data science creates coherent knowledge from incoherent structures of data using statistical processes and tools. In a world where technology reigns supreme even for the most minor issues, data science is a rapidly developing sector and is quite beneficial for anyone interested in exploring this area.
To understand and work with data science, one has to be familiar with the workings and significance of the tools used to arrange unstructured data. These include Tableau and Power BI, two of the most widely used analytical tools in organising data.
Read on to learn how a knowledge of these tools can help make a successful career in data science.
Data visualisation and its endless merits
Before using tools like Tableau and Power BI, it is imperative to know what data visualisation is and how it works. Imagine reading about the population statistics of a country over decades- the changes in the numbers become a lot easier to understand when presented with graphs or pie charts.
These graphs and charts in data visualisation are often created with the help of the programming language Python. Data visualisation with Python allows it to use libraries like Bokeh and Matplotlib, which contain unique intrinsic units, to create the required graphs from a mass of unstructured collected data. Tableau and Power BI help enhance such effortlessly receptive graphs and more. They also employ linear regression when sorting data- it helps predict the outcome of the dependent variables on the independent ones during data analysis and assists in data visualisation.
Data science using Tableau - Its uses and importance
Any organisation needs its data to be analysed in order to understand its progress and targets and take corresponding action. Founded in 2003, Tableau enters this scenario as an essential analytical tool that helps process the sea of data into great visuals allowing Data Scientists to understand and extract relevant information from the same.
How Tableau operates
- Tableau uses TDE, or the Tableau Data Extract, to store and collect mass data, sometimes also Hyper, a technology that enhances the collection and analysis of the data through fast and efficient processing.
- Sometimes, errors occur while data is being stored or analysed. Tableau uses ETL Integration (Extract Transform and Load) to recognise, extract and fix the error. With a visually appealing and easy-to-grasp interface, one can do all of this with the help of Tableau Prep Builder.
- Anyone examining and willing to explore all kinds of data can connect to a vast array of databases anywhere using the Tableau Desktop.
- Local businesses or non-profit enterprises also make use of Tableau Public which does all of the same things as Tableau Desktop, but the sharing of the reports of the data exploration remains limited to the public domain.
- Finally, Tableau Server and Tableau Online provide security, efficiency and a large amount of collective accessibility and inspection of the data in your organisation.
- Tableau even allows one system to connect with another for further examination and scrutiny.
Data science and its dependency on Power BI
Like Tableau, Power BI is a Business Intelligence (BI) technology that provides clarity to unorganised data, like creating a meaningful image by putting puzzle pieces together. Power BI was in its initial design in 2010 and was launched in 2011 by Microsoft as a desktop application that allowed anyone to inspect, understand and visualise data on their own.
Basic features of Power BI
- Power BI’s major appeal is the connection it offers to several data sources, from Excel to Google Analytics and even social media platforms like Facebook, Twitter and Reddit.
- It also allows analysts to connect to IoT (Internet of Things) devices, collect that data and transform it into creative visualisation and interaction.
- The Power BI Pro enables anyone to gather any data they want, survey it and create dashboards suited to their required needs.
- One can also control who has access to the new, processed data. Power BI simultaneously protects and shares data and insights across the organisation.
- It is regulated per General Data Protection Regulation (GDPR) and is easily operable on mobiles.
- With its Premium well-suited for large organisations and businesses, Power BI is a real step up for Data Scientists.
Conclusion
Data science as a branch has been extremely versatile in its growth in recent years, and a simple visual representation of anything complicated always yields a better understanding of the concept. This is why someone starting out with data science needs to be well-acquainted with Tableau and Power BI, their contribution to the field and their interchangeable usage because of their similarities.
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