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Form C4 Version 4.0 (2010/2011) Hectowatt University Course Descriptor Template 1. Course Code F21DV 2. Course Title Data Visualization and Analytics 3. CQF Level 5. School Mathematical and Computer

How to fill out data visualisation and analytics

How to fill out data visualisation and analytics:

1. Start by gathering and organizing the data: Collect all relevant data sources and ensure they are in a structured format. This includes data from various systems, databases, and applications.
2. Cleanse and prepare the data: Before visualizing the data, it is important to clean and transform it if necessary. This may involve removing duplicates, dealing with missing values, and converting data types.
3. Choose the right visualisation techniques: Depending on the type of data and the purpose of analysis, select appropriate visualisation techniques. This could include bar charts, line graphs, scatter plots, heat maps, or more advanced techniques like network diagrams or geographic visualisations.
4. Create visually appealing and informative visualisations: Design the visuals in a way that effectively communicates insights and trends. Use colors, labels, and titles to make the visualisations easily understandable to the target audience.
5. Analyse and interpret the visualisations: Once the visualisations are created, it is important to analyze and interpret the findings. Identify patterns, trends, outliers, and correlations in the data to gain insights and make data-driven decisions.

Who needs data visualisation and analytics:

1. Businesses: Data visualisation and analytics are essential for businesses of all sizes and industries. It helps them identify market trends, customer behavior, and make informed decisions.
2. Data analysts and scientists: Professionals in the field of data analytics rely on visualisation techniques to explore, understand, and communicate data insights effectively.
3. Researchers and academics: Researchers and academics from various domains use data visualisation and analytics to analyze large datasets, identify patterns, and validate their hypotheses.
4. Government agencies: Government agencies utilize data visualisation and analytics to track public health trends, analyze crime rates, and understand socioeconomic patterns.
5. Media and journalism: Data visualisation and analytics play a crucial role in investigative journalism and storytelling. Journalists use visualisations to present complex data in a more accessible and engaging manner.

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What is data visualisation and analytics?

Data visualisation and analytics is the process of analyzing data sets to summarize their main characteristics using visual elements such as charts, graphs, and dashboards.

Who is required to file data visualisation and analytics?

Data visualisation and analytics may be required to be filed by data analysts, data scientists, business intelligence professionals, and any individual or organization looking to gain insights from their data.

How to fill out data visualisation and analytics?

Data visualisation and analytics can be filled out by using various software tools such as Tableau, Power BI, Excel, or any other data visualization platform. First, the data needs to be collected, cleaned, analyzed, and then visualized to draw meaningful insights.

What is the purpose of data visualisation and analytics?

The purpose of data visualisation and analytics is to help organizations make informed decisions by presenting data in a visual format that is easy to understand. It can also help in identifying patterns, trends, and correlations within the data.

What information must be reported on data visualisation and analytics?

The information reported on data visualisation and analytics may include key metrics, trends, anomalies, correlations, and any other insights derived from the data analysis.

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