MDS502: ability to create a database system and design a model for comprehensive data management: Data Management Assignment, CU, Malaysia


curtin university (CU)

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MDS502: Data Management

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This assignment has been designed to assess students’ ability to create a database system and design a model for comprehensive data management. This assignment addresses the following learning objectives for this module:

CLO1: Create database systems using appropriate design techniques and scripting languages (C6, PLO1, MQF1)

CLO2: Design models for storage, cleansing, transforming, integrating of data to meet organizational standards (P7, PLO2, MQF2)


These criteria will be used to evaluate your assignment/project submission:
• The assignment/project is well presented (Professional standard)
• The proposed data management system is suitable and feasible
• The database creation and meeting organizational requirements are well addressed
• Assumptions are listed


You are to create a comprehensive data extraction, transformation, and loading (ETL) project based on a real dataset. You are free to choose any dataset of your preference as long as it is a freely shared valid dataset generated by a legitimate organization or company.

The whole ETL project would mainly consist of data extraction, data transformation, and data loading processes along with supporting activities, which will be mentioned by the tasks in the next section. Data extraction could be either internal data sourcing or external data sourcing. Accordingly, data can be obtained through web scraping or a database management system.

Extract, Transform, Load (ETL) Case Study

You have been assigned to an ETL project that would process a dataset in ETL and analytics activities. You would start from a web scraping activity that extracts public data available on a website, for example: web scraping data on a weather website or Covid-19 pandemic-related website. For web scraping purpose, you may use an automation tool, such as Python-based Beautiful Soup. The related tasks that need to be done are divided in the upcoming sections.

Accomplishing these tasks properly would mean successful completion of this project. You may use the Python programming language to do the tasks. As such, an Integrated Development Environment (IDE) tool for Python such as Jupyter Notebook could be used. At the end of the extraction, the file must have more than sufficient number of attributes (guide: 10 attributes) including a range of data types consisting of character, integer, number/float, date, and another. In addition, the extracted file may have missing values and outliers.

Task 1: Data Extraction and Understanding the Dataset

Select a source website where you will do your web scraping on and justify your choice of the website. You may refer to this web scraping tutorial or if you prefer using Beautiful Soup library, After you complete your web scraping activity, list down all the available column names and explain each of them.

Task 2: Identifying the Variables/Columns

1. Analyze your dataset and recommend how it could be used for decision making. For example, whether it would be appropriate for prediction, classification, or other types of analysis.

2. Explain how each variable in the dataset, relevance as independent or dependent variable for the proposed model would be.

Task 3: Data Categorization and Data Profiling

1. As part of data management is data categorization. Select columns or variables according to two categories i.e., independent and dependent variables in your code. Include a screenshot of this activity.

2. Explain data profiling and data profiling components.

3. Appraise and evaluate your dataset with data profiling components.

Task 4: Data Transformation [20 marks, CL02]

It is likely that your data needs transformation in order to prepare it for future analytics, thus observe your data and decide whether you would eliminate duplicate rows, drop rows with missing or null values – or replace these values with median/mean value – Include your justifications. Also convert the categorical columns to numerical columns (if there is any).

Furthermore, detect the rows with outliers and decide what to do with them.
Moreover, standardize the data so that every column’s range of values would be the same e.g., 0-1 or 1-10 in order to support the analytics algorithm’s performance. Include the screenshots of the above activities.

Task 5: Analysis (Factor analysis / Classifications

Describe your data with justifications, so that you could present it to your management. The story telling could be established by discovering the correlations between the variables based on factor analysis. Alternatively, you may conduct classifications. Afterwards, explain how the discovered relevant factors affect the dependent variable. Also, plot or visualize these relevant factors. And finally, drop the unnecessary variables from your data and keep the relevant ones. Include the screenshots of the above activities.

Task 6: Data Loading

A relational database provides data query in simple and meaningful manner which would be a better data management practice, hence load the previously transformed data frame into  local SQL database (create the Database) e.g., MySQL, PostgreSQL, or alike. Include a table specification using the following template:

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