Software Product And Technical Report Assignment Sample

• Type Assignment
• Downloads735
• Pages11

Introduction

Get free samples written by our Top-Notch subject experts for taking Assignment Helper services.

The term “Data Visualization” can be defined as the representation of the data in the form of graphs and charts that has been representing the valuation as well as information of the provided dataset. The visualizing elements used within the data visualization are mainly consisting of the charts, graphs, maps, and tools for data visualization as well as certain plots. This task would show the relations between different variables within the selected dataset. This would be revealed using visualization tools and predictive models for better understanding. It has also been providing an accessible way of understanding the trends, patterns, as well as outliers of the given data. The data visualization has also been visualizing the data in order to for the data into patt4erns for better understanding as well as highlighting of the trends and outliers. 

Aim and Objectives

Aim

The main aim of the research is to enable the users to demonstrate better the part of the full data analysis with the identification of data visuals.

Objectives

The main objectives based on the research have been discussed below. These are:

• To prepare the data for assessing the quality of the dataset with suitable techniques used in visualization.
• To explore the data with the help of analytical techniques and data mining tools for finding the patterns as well as trends (Xin et al. 2018). 
• To use specific tools for performing the visuals of the data with the help of data modeling techniques and visualization. 
• To evaluate the results for comparing the findings and outcomes that are derived from the visualization. 

Methodology

The research has been conducted based on a deeper understanding with respect to the dataset by making it easily accessible to the users, which might help them for navigating the usage of the data in the future (Arleo et al. 2019). The exploration of the data has been considered as one of the major successes with respect to data analysis. It has also been revealing new paths for discovering the ideas as well as providing assistance for the identification of the refined analysis for future problems and questions. Python can be considered as one of the major used programming languages that have been utilized for visualization as well as exploration of the data. It has been suitable for secondary data analysis by using the provided data for analysis. The research has mostly been dealing with some major stages that include the understanding of the assigned variables or attributes that further helps in the better analysis of the data (Shamal et al. 2019). 

It has also been using the descriptions with respect to the field catalogs of the data as well as metadata that has been offering insights for the representation of the data fields that have been helping the discovery of the incomplete as well as missing data within the provided dataset. It has also been detecting the outliers as well as anomalies that have further been derailed by the distortion and analysis of the real dataset. It can be considered a significant aspect with respect to the identification of data at the early stage (Castillo et al. 2021). The numerical methods hypothesis, visualization of the data, as well as interquartile ranges, have been taken into account as the most common methods used for the detection of the outliers. 

Analysis and results

The performance of data sets the modeling and analysis required to visualize the data in a simple representation of the data (Wadiwala et al. 2019). The research of a variety of tasks the performance relates to the exploration of literature and findings are usually available for the data visualization. It included the sharing and finding of the code for the processing of writing, data and publishing in the evaluation of the research. The ultimate goals of the research in data visualize the communication of the data in the sense of making relationships with the presentations of other data. The data management requires the type of thoughtful data management that requires the type of use in data visualizations. In the academic manner of data visualization, the communication can be done clearly with the purpose of data (Tang et al. 2019). The type of data visualization requires the analysis with gridlines and access. The visualization of the data can be considered to improve the importance of data sets in an expensive manner. The way of development visualizes the facility with the point of fast communication to the policy and social implications. The promotion of availability tools like Vims is user friendly for the presentation and graphs for the animations. The tools of animations require the common data, which are available with the tools of converted word tools. The tools of IBM and a variety of data visualization (Siripanpornchana et al. 2019) usually develop the common data. If the data analysis can be made the data of modeling can be visualized easily. 

The figure mentioned above has been stating the process of importing the dataset in the pandas' data frame using relevant python codes. Certain libraries are imported such as “pandas, numpy, and seaborn” and “matplotlib.pyplot” in order to input the data values and visualize them as per the requirements. 

The figure mentioned above has been showing the scattered plot that has been designed for the comparison between two variables in the dataset with data such as Age and Body temperature. It has been observed from the above figure that the middle-aged people have higher body temperature as compared to others. 

The figure shown above has been representing the codes designed in python for the bar plot along with the output for the bar plot. The bar pot has been designed for two columns mentioned in the dataset including “Diastolic BP” and “Systolic BP”. It has been observed from the given figure that the reading of “Systolic BP” has been comparatively higher from 120 to 160 with respect to the x-axis.

The figure shown above has been representing the line graph that has been depicting the heart rate and age with respect to the provided dataset (Lai et al. 2020). The codes have also been consisting in the figure that has been representing the relevant use of the python codes for the generation of the line graph. It has been observed from the above figure that the plot has been designed for “Age” and “Heart Rate” with respect to the line plots.

Modelling and visualisation

Evaluation

This data exploration can be taken into consideration as the best method of statistical learning that has been lent to the machine learning algorithm (Yang et al. 2020). The process of data exploration can be considered as one of the most important aspects of the increment in working with the information on the basis of geographical data. This technique has been implemented for examining the information as well as determining the data for different visualization in order to express the ideas within the architecture (Kamw et al. 2019). The information related to the visualization has been providing the design of the data with the help of drawings, and data diagrams using scientific techniques for visualizations. The data visualization scheme has been gaining power for the presentation of aid designers (Chen, 2019). It also possesses the power of presenting multidimensional data for permitting efficacious communication. The users might require the perspective of viewing the similar data that insists on the exportation and importing of the provided data. 

Importance of Data Visualization

The practices of data visualization can be created by the process of data that are collected in the broader presentation of the elements in architecture (Siripanpornchanaet al. 2019). In the presentation of architectural data, the stakeholders can manipulate the function of the data. By the importance of data visualization, the importance of AI executes the massive collection of exploring the business in the trending of data. Data visualization usually provides an effective and quick way of communicating. The improvement of the need for more attention can be done with the predictions and self products with the volumes. The benefits of data visualization include the ability to increase the insight to improve the faster decisions. In an ability to maintain the interests of the audience the information for easy distribution can be created by the sharing of insights to involve everyone (Ruchikachorn et al. 2019). In the visualization of big data analysis, the visualization with the most important in the company sorted with the slow presence in the business. The visualization of big data requires the typical techniques for the presence of information in business. The beneficial data analysis of big data visualization can be posed into the various disadvantages in an organization. The tools of big data visualization can be done with the specialist which is hired. The sets of data analysis with the best sets of data can be guaranteed to the organization for the optimizations use of data (Surasvadi et al. 2020). The visualization of the data can be essential for the process of quality in control of the metadata and corporate data sources. 

Advantages and Disadvantages of Data Visualization

The better agreement of the period that happened required to look at a new convention of the experience in a massive manner required both afterwards and circumstances in the clear deal of the time. The superior method of the information requires the comprehension of the circumstances for the top venture of graph structures. The simple sharing of the data requires the sharing of visual data to pass and draw the data with more adorableness. In the precise investigation of the data, the information edges to the association of the edge over the adversaries. In the deal of investigations the causes of information created to help in understanding the business of purchasing the impact of topography (Shah et al. 2019). In the discovery of the business influenced by the online organization rapidly discover the occasion of comparing. 

The disadvantage is information possessed in the change of robustness for the data with the prompt at the end of theoretical portrayal data. The one-sided arrangement of the configurations prompts the end of portrayal data. The absence of help requires the issues of the plan in the prompt of clarification for the reason in the wrong engagement of the work. The changing of difficulties for the situation requires the change of protracted information and theoretical ends (Kunjir et al. 2019). The turns of prompt which may be included for the requirements can be rejected for the result of one-sided information. 

Improvements in Data Visualization

Improvement of data visualization can be compared in the category of a single measure

(Chang 2018). To replace the connection with the dashboard and common data analysis in the boxes of histograms, the connection of data points can compare the categories of the sample straightforwardly to the changes in visualization. In the use of predictions, nature can be drawn by the pattern of random sense to convey the visualization. The communication of visual language can be read for the adhering to communication. In the data of convincing the message, the cacophony can be caused by the blend for the process of faster information. The strategy to emphasize the pigment the clues and shapes mimic the values denote in the values (Singh et al. 2019). The manner of the value indicated for the same size of visualization with the differentiation of the easier point to navigate the task. The last choice of applications can be required for the improvement of visualization in the manner of distraction. It is sure to rely on the visual of point suffice for the details and the importance of process in patterns. In the place of most important view the top upper left corner is drawn into the area of limit into the visualization. Through the interactions of the visualization, the encouragement made for the exploration requires building an interaction of the subtle (Samuel et al. 2020). The size of the blend requires making the relative size to navigate the easier value of the market size. 

Conclusion

The research has been done on the basis of the data visualization used in python. The research has mainly been dealing with certain objectives based on the exploration and visualization of the data. The python codes have been used for data visualization and modeling with the help of certain plots and graphs used for the visuals. The histogram plot, bar plot, scattered plots, line graph along with other plots have been used in the entire research using relevant codes designed in python. The efficiency in the visualization has been representing the delicate act of balancing the infographic of the provided data between the function and the form. The visuals as well as the data must have been working together for better combination and greater data analytics.

References

Arleo, A., Tsigkanos, C., Jia, C., Leite, R.A., Murturi, I., Klaffenböck, M., Dustdar, S., Wimmer, M., Miksch, S. and Sorger, J., 2019, October. Sabrina: Modeling and visualization of financial data over time with incremental domain knowledge. In 2019 IEEE Visualization Conference (VIS) (pp. 51-55). IEEE.

Shamal, A.D., Kamw, F., Zhao, Y., Ye, X., Yang, J. and Jamonnak, S., 2019, October. An open source trajanalytics software for modeling, transformation and visualization of urban trajectory data. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC) (pp. 150-155). IEEE.

Xin, S., Zhenhua, L. and Junyu, D., 2018. Complex Network Modeling and Visualization Analysis for Ocean Observation Data. Journal of System Simulation, 30(7), p.2445.

Kamw, F., Al-Dohuki, S., Zhao, Y., Eynon, T., Sheets, D., Yang, J., Ye, X. and Chen, W., 2019. Urban structure accessibility modeling and visualization for joint spatiotemporal constraints. IEEE Transactions on Intelligent Transportation Systems, 21(1), pp.104-116.

Chen, T., 2021, May. Feedback Analysis of Geotechnical Structure Simulation Based on 3D Modeling and Visualization. In Journal of Physics: Conference Series (Vol. 1915, No. 2, p. 022062). IOP Publishing.

Yang, L., Peng, J. and Shao, P., 2020. Modeling and visualization of rice roots based on morphological parameters. IEEE Access, 8, pp.23407-23416.

Lai, X. and Hu, J., 2020, March. Research on Application Modeling and Visualization Based on Aviation Information Exchange Model. In IOP Conference Series: Materials Science and Engineering (Vol. 790, No. 1, p. 012124). IOP Publishing.

Di Stefano, M., Castillo, D., Serra, F., Farabella, I., Goodstadt, M.N. and Marti-Renom, M.A., 2021. Analysis, Modeling, and Visualization of Chromosome Conformation Capture Experiments. In Capturing Chromosome Conformation (pp. 35-63). Humana, New York, NY.

Fayolle, P.A., McLoughlin, L., Sanchez, M., Pasko, G. and Pasko, A., 2021. Modeling and Visualization of Multi-material Volumes. Scientific Visualization, 13(2), pp.117-148.

Chang, V., 2018. Data analytics and visualization for inspecting cancers and genes. Multimedia Tools and Applications, 77(14), pp.17693-17707.

Conner, C., Samuel, J., Kretinin, A., Samuel, Y. and Nadeau, L., 2020. A picture for the words! textual visualization in big data analytics. arXiv preprint arXiv:2005.07849.

Kunjir, A., Shah, J., Singh, N. and Wadiwala, T., 2019. Big data analytics and visualization for hospital recommendation using HCAHPS standardized patient survey. IJ Information Technology and Computer Science, 3(3), pp.1-9.

Surasvadi, N., Ruchikachorn, P., Siripanpornchana, C., Thajchayapong, S. and Plangprasopchok, A., 2019, April. Tpmap: a data analytics and visualization platform to support thailand target poverty alleviation programs. In IEEE Computer Society (pp. 325-326).

Blake-Plock, S., Hoyt, W., Casey, C. and Zapata-Rivera, D., 2020. –DATA ANALYTICS AND VISUALIZATION FOR XAPI LEARNING DATA: CONSIDERATIONS FOR A GIFTSTRATEGY. Design Recommendations for Intelligent Tutoring Systems, p.163.

Chong, S., Lee, Y.H. and Tang, Y.W., 2020, August. Data Analytics and Visualization to Support the Adult Learner in Higher Education. In 2020 The 4th International Conference on E-Society, E-Education and E-Technology (pp. 126-131).