British Council Going Global Exploratory Grant
Objective
Exploring partnerships for the co-creation of international joint programmes in “Data Analytics”.
Lead Investigators
| Anup Aprem (National Institute of Technology Calicut) | Arantza Aldea (Oxford Brookes University) |
|---|---|
 |  |
Outcome
Elective module in “Foundations of Data Analytics”
Course offered
Monsoon 2022 (Starting Aug 21, 2022): EC6435D
Learning Outcomes
- Demonstrate ability to identify and integrate data of various types from a variety of sources, and make informed judgements about their use in data science research
- Critically evaluate the methodologies applied in data gathering, data processing and data exploration to disseminate findings using data visualisation tools.
- Apply different data science tools to create appropriate visualisations of high dimensionality data, aligned to the students area of interest
Bonus
Short course certificate from Oxford Brookes University.
Outline
- Core Data Science Concepts
- Different types of data sources (admin data. Survey data, open data, big data)
- Data Gathering methods, e.g web scraping
- Challenging with unstructured Data
- Data Processing
- Data Analysis (structured and unstructured Data)
- Exploratory data analysis
- Methodologies for data visualisation
Syllabus
Weekly Lecture Plan
| Lecture 1 | Lecture 2 | |
|---|---|---|
| Week 1 | Introduction to Data Science | Introduction to Python and Libraries |
| Week 2 | Database Theory for Data Science | (1/2) Database Theory for Data Science (2/2) |
| Week 3 | SQL for structured database | XML for semi-structured data (1/2) |
| Week 4 | XML for semi-structured data (2/2) | Data Pre-processing (1/3) |
| Week 5 | Data Pre-processing (2/3) | Data Pre-processing (3/3) |
| Week 6 | Introduction to Big Data (1/2) | Introduction to Big Data (1/2) |
| Week 7 | Introduction to Data Visualization | Plots and Grammar of Plots |
| Week 8 | Plotting with Matplotlib, Pandas, Seaborn | Practical aspects of data visualization |
| Week 9 | Data Transformation | Data Transformation with Python: numpy, pandas |
| Week 10 | Exploratory data analytics (1/2) | Exploratory data analytics (2/2) |
| Week 11 | Advanced Data visualization: Interactivity | Buffer |
| Week 12 | Course Project Week (No Lectures) | Course Project Week (No Lectures) |
| Week 13 | Course Project Presentation (1/2) | Course Project Presentation (2/2) |
Lab schedule
| Week 1 | Introduction to Python: Familiarization Exercises |
| Week 2 | Database design: Normal Forms |
| Week 3 | Buffer |
| Week 4 | SQL: Table creation, SQL queries, SQL and Python |
| Week 5 | XML, Python and XML |
| Week 6 | Buffer |
| Week 7 | Data Pre-processing in SQL |
| Week 8 | Plotting with Matplotlib (1/2) |
| Week 9 | Plotting with Matplotlib (2/2) |
| Week 10 | Data Transformation in Python |
| Week 11 | Exploratory data analytics |
Course Project (Individual)
In the course project you will identify a suitable data problem, obtain the dataset, create a database and perform data visualization on the problem. You will produce a final report, an article, a poster or a flyer where you display the data visually to a specific audience. The evaluation would focus on the design process to achieve the final visualization. Evaluation sub-tasks:
- Transforming the data obtained into an appropriate normal formal and converting into a SQL database, and defining appropriate keys, data types and relationships.
- Data pre-processing using python and SQL queries from Python
- Data Transformation and Exploratory data analysis in Python
- Conclusion and Inference about the selected data set.
Lecture and Lab Material
Copyrighted: Available for reference on request