Why Data is important?

Our lecturers frequently give us advice about the assignments and dissertations that we must write as part of our coursework. To pass the course, all students—graduate, postgraduate, or PhD—must produce at least one report.

As a result, advice for such an important piece of writing is essential; as a result, our teachers and seniors frequently give us advice on the dos and don'ts of writing a thesis report. Every time we receive instruction on how to write a report by assignment writers, we are instructed to take into account a few crucial elements that would enable the writer to summarise all the data in a clear, concise manner. Several crucial aspects of a report include; (Stanford, n.d.)

• Accurate and clear abstract that would briefly define the nature of the study
• The reader-friendly structure that can be easily comprehended
• Precise and clear research design
• Correct grammar and punctuation
• Correct usage of terminology
• Accurate data

As was already mentioned, data is one of the crucial elements that must be taken into account before beginning to write a thesis report because, without it, there would be no foundation for the report.

What is Data?

Engineering and computer science fields frequently utilise the term "big data." Big data is used in many fields than just these two, though. Every industry, regardless of its size, deals with data; therefore, every sector needs to be familiar with the concept. (Chai, n.d.)

Big data refers to a significant collection of data sets that are always growing and were gathered by various operating organisations. Engineers, scientists, and data analysts employ both quantitative and qualitative methods to extract, examine, and organise the gathered data.

This collected data is found in multiple forms, for example;

• Scattered data
• Structured data 
• Unstructured data 

Managers frequently use this information to get insights that help them make wise decisions. Leaders and companies are able to predict future trends, threats, and viable solutions thanks to the data gathered from the enhanced data.

Data analysts typically forecast future events by studying human behaviour, keeping an eye on what they do, and coming up with fresh ideas that advance and improve decision-making.

Branches of Data

Although the many types of data have been addressed thus far, it is still required for the researcher to identify their own type of data and select an appropriate one. Qualitative and quantitative data are the most comprehensive kind of data. (WEPREP, 2021)

• Qualitative data is used to quantify non-countable characteristics or characteristics that are tangible but intangible, such as feelings, emotions, age, gender, race, and other comparable variables. Additionally, qualitative data does not require the solution of mathematical equations.
• Quantitative Data: On the other side, quantitative data is the polar opposite of qualitative data. As its name implies, it is composed of elements that can be expressed in numerical form and that must be used in mathematical equations in order to produce results.

How data helps in writing 

Data gives valuable information which can be helpful in drafting good pieces of writing even if it is for accounting dissertation topics. However, before that, you must comprehend the purpose of your assignment in order to make it more manageable from the outset.

If you only read the prerequisites or topic once, you might not be able to click and get the entire picture. Instead, make sure you read the directions or demands provided to you at least twice. You could learn what you are actually expected to work on by reading the content repeatedly. (Jessica, 2022)

Understanding what is written and the tone of the standards is crucial since even the smallest errors could eventually compound into a major catastrophe. You can seek assistance from someone if you find it increasingly difficult to comprehend the need fully; this won't be a really onerous favour to request.

Best Tools for data collection and its analysis 

Quick View 

Quickview is a tool for data analytics, data visualisation, and self-service business intelligence. With capabilities like Data Integration, Data Literacy, and Data Analytics, it strives to accelerate the value that data can bring to businesses by being recognised as a leader in Gartner's Magic Quadrant 2020 for Analytics and BI platforms.

Apache Spark 

is an open-source cluster computing framework that is used for real-time processing, making it one of the Apache Software Foundation's most popular projects.

It has a great open-source community, and a programming interface, and is now the most active Apache project. This interface ensures implicit data parallelism and fault tolerance.

KNIME 

A free and open-source data analytics, reporting, and integration tool designed for analytics on a GUI-based workflow is called Konstanz Information Miner, or KNIME.

Products KNIME offers these two pieces of software:

• The open-source KNIME Analytics Platform is used to gather and clean data, make reusable components available to everyone, and develop Data Science workflows.
• KNIME Server is a platform used by businesses to implement data science workflows, manage teams, and automate processes.

Tableau 

Create reports and share them across desktop and mobile platforms, in a browser, or embedded in an application using the data visualisation and analytics platform Tableau.

It can operate locally or on the cloud. VizQL, the foundational query language of the Tableau platform, powers a large portion of it.

This reduces the requirement for end-user performance improvements by converting drag-and-drop dashboards and visualisation components into effective back-end queries. Tableau does not, however, enable complex SQL queries. (Stitch, n.d.)

Jupyter Notebook 

After being installed using the Anaconda platform or Python's package manager, pip, Jupyter Notebook is a free and open-source web tool that may be used in a browser or on desktop systems. Developers can use live code to generate reports with data and graphics.

There are about 40 programming languages supported by the system. Jupyter Notebook, formerly known as IPython Notebook, was created using the Python programming language and enables programmers to leverage the extensive library of Python packages for analytics and visualisations. There is a sizable developer community for the tool that also uses other languages.

References

Chai, W. (n.d.). Big Data Analytics. Retrieved from Techtarget.com: https://searchbusinessanalytics.techtarget.com/definition/big-data-analytics

Jessica. (2022, April 14). How You Can Complete Your Assignment in 5 Steps? Retrieved from ukssignmenthelp.uk: https://www.ukassignmenthelp.uk/how-you-can-complete-your-assignment-in-5-steps/

Stanford, U. (n.d.). Characteristics of Good Thesis. Retrieved from Tomorrowsprofessors.sites.stanford.edu: https://tomorrowsprofessor.sites.stanford.edu/posting/1301

Stitch. (n.d.). Top 24 tools for data analysis and how to decide between them. Retrieved from https://www.stitchdata.com/resources/data-analysis-tools/

WEPREP. (2021, April 03). Top 6 Data Collection Tools That Helps Take Sensible Decisions. Retrieved from Weprep.com: https://wperp.com/88793/best-data-collection-tools/