Field of machine learning and artificial intelligence

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Reference no: EM133886323

Assignment Description

Introduction:
In this assignment, you will be creating a data visualization project to communicate insights from a given dataset. Data visualization is an essential skill for effectively presenting and interpreting data. By creating meaningful visual representations of data, you can identify patterns, trends, and relationships that might otherwise be challenging to perceive.

Task:
Your task is to select a dataset and design a data visualization project that effectively communicates key findings and insights. The dataset can be from any domain or topic of your choice, such as social media, healthcare, finance, sports, etc. The objective is to create visually compelling and informative representations of the data.

Requirements:
Dataset Selection: Choose a dataset that is relevant and interesting to you. Ensure that the dataset is appropriate for creating visualizations and contains enough data points to derive meaningful insights. You can use publicly available datasets from sources like Kaggle, or below data sources.

See the web links for the sample datasets. Choose only one.
Kaggle Data Set
Inside Airbnb
Births, deaths and marriages data
National Statistics
Data on employment and labour market
UCI Machine Learning Repository
Data on employment and labour market

Data Cleaning and Preparation: Clean and pre-process the dataset as necessary. Remove any irrelevant or incomplete data, handle missing values, and format the data appropriately for visualization purposes.

Visualization Techniques: Utilize a variety of visualization techniques to present the data effectively. Consider using graphs, charts, maps, and other visual elements that best represent the patterns and relationships in the data. Choose appropriate visualization types based on the data characteristics and the insights you want to convey.

Storytelling and Interpretation: Create a narrative flow in your data visualization project. Clearly explain the purpose of your visualization, the insights you have gained from the data, and the conclusions you draw. Use annotations, captions, or descriptions to provide context and guide the viewer through your visualization.

Aesthetics and Design: Pay attention to the aesthetics and design principles of your data visualization project. Ensure that the visuals are visually appealing, easy to understand, and well- organized. Use appropriate color schemes, labels, and titles to enhance clarity and readability.

Interactive Elements (optional): If you have the skills and resources, consider adding
interactive elements to your visualization project. Interactive features like tooltips, filters, or animations can enhance the user experience and allow for deeper exploration of the data.

7. Documentation: Provide clear documentation of your data visualization project. Include a written report that describes your dataset, data cleaning process, visualization choices, and the insights you have gained. Additionally, provide any code or scripts used to generate the visualizations, along with instructions on how to run them if applicable.

Learning Outcomes

Critically apply skills, techniques, and knowledge from a range of data analysis methods and algorithms for enhancing and solving problems in various domains.
Develop abstract thinking and design ability to analytically demonstrate concepts relating to data science.
Use research-based knowledge for the design of experiments, analysis, and interpretation of data to provide valid results.
Critically evaluate and analyse advanced data science topics, and concepts, and implement them in workplace.
Identify and implement appropriate programming and software tools to critically analyse big data applications in workplace.
7.8 Critically analyse the data and apply predictive modelling technique in the field of Machine Learning and Artificial Intelligence.

7.9 Critique legal, social, and ethical issues within the field of data science and applicable ancillary sectors, as applied to contemporary research and industry practice.

Reference no: EM133886323

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