Big data visualisation in practice with tableau

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Reference no: EM132765775

CIS8025 Big Data Analytics - University of Southern Queensland

Course Objectives -

1. Synthesize academic and professional knowledge of recent developments in big data visualisation design principles;

2. Evaluate relationships between big data and visualisation design concepts;

3. Explore and apply big data conversion into visual forms for decision-making purposes;

4. Critique innovative visualisation approaches to provide solutions to real-world problems;

Task 1 - Hadoop and Spark big data management platforms (1500 words)

Hadoop and Spark are two big data management tools that in turn underpin big data visualisation applications. Describe and explain (using diagrams where appropriate) how each:

Task 1.1 Hadoop contributes to the effective management of big data in organisations in your answer also highlight any shortcomings of this big data management technology (750 words);

Task 1.2 Spark contributes to the effective management of big data in organisations in your answer also highlight any shortcomings of this big data management technology (750 words).

Task 2 - Big Data Visualisation approaches and challenges (1000 words)

Previously in this course we reviewed a number of approaches to big data visualisation. For Task2 critically review and discuss one big data visualisation approach and one or more of the challenges that organisations face in undertaking a big data visualisation initiative. Here you should emphasise the importance of Data Story Telling.

Task 3 - Big Data Visualisation in practice with Tableau (500 words)

Brief: Queensland Main Roads (QMR) wants to build a dashboard to better understand the severity, nature and type of road traffic crashes occurring by location and road conditions over time. In particular, they would like to see if there are any distinct patterns in relation to (1) crash severity, type and nature (2) location of each crash (3) time of each crash and (4) location road conditions and (5) number of vehicles and pedestrians involved.

These insights can be gained from the extensive data available for road traffic crashes across the state of Queensland contained in the qldcrashes.csv data set. This dataset contains data on over 300,000 road traffic crashes from January 2001 through to December 2016 from over 400 different postal locations in the State of Queensland.

See the data dictionary qldcrashesDataDictionary.doc for a detailed description of the qldcrashes.csv data set. Note read the data set qldcrashes.csv into Tableau Desktop as text file type .csv to keep Tableau packaged workbook a manageable size.

Task 3.1 Conduct an exploratory data analysis (EDA) of qldcrashes.csv data set using Tableau Desktop so that you gain a better understanding of the important characteristics of the data set qldcrashes.csv data set. Summarise the key findings in a Table 3.1 with the Caption: Key Findings of EDA of QLD Road Traffic Crashes. Provide an accompanying discussion of the key findings of exploratory data analysis of qldcrashes.csv data set making use of Tableau views of variables in the qldcrashes.csv data set to highlight visually important key findings from your exploratory data analysis. Is there is any missing data? Will some variables need to be converted into categories or different types of variables etc? (250 words)

Task 3.2 Based on the brief provided by QMR for a QLD Road Traffic Crashes (QLD RTC) Dashboard Outline and describe your planned Tableau dashboard for the qldcrashes.csv data set that you will implement in Assignment 3. Your planned dashboard for Assignment 3 will include at least four views and at least one geomap view of important aspects of Qld road traffic crashes data for time period of 2001 to 2016. For Task 3.2 also provide screenshots of and briefly describe two sample views of qldcrashes.csv data set and include packaged Tableau workbook file (250 words)

Report structure -

1. Cover page

2. Title of Contents

3. Body of Report with relevant main Task headings and sub headings

4. Task 1, Task 1.1, Task 1.2

5. Task 2

6. Task 3, Task 3.1, Task 3.2

7. References

8. Appendices

Attachment:- Big Data Analytics Assignment Files.rar

Reference no: EM132765775

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