British Council Going Global Exploratory Grant


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)


Elective module in “Foundations of Data Analytics”

Course offered

Monsoon 2022 (Starting Aug 21, 2022): EC6435D

Learning Outcomes

  1. 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
  2. Critically evaluate the methodologies applied in data gathering, data processing and data exploration to disseminate findings using data visualisation tools.
  3. Apply different data science tools to create appropriate visualisations of high dimensionality data, aligned to the students area of interest


Short course certificate from Oxford Brookes University.


  1. Core Data Science Concepts
  2. Different types of data sources (admin data. Survey data, open data, big data)
  3. Data Gathering methods, e.g web scraping
  4. Challenging with unstructured Data
  5. Data Processing
  6. Data Analysis (structured and unstructured Data)
  7. Exploratory data analysis
  8. Methodologies for data visualisation



Weekly Lecture Plan

 Lecture 1Lecture 2
Week 1Introduction to Data ScienceIntroduction to Python and Libraries
Week 2Database Theory for Data Science(1/2) Database Theory for Data Science (2/2)
Week 3SQL for structured databaseXML for semi-structured data (1/2)
Week 4XML for semi-structured data (2/2)Data Pre-processing (1/3)
Week 5Data Pre-processing (2/3)Data Pre-processing (3/3)
Week 6Introduction to Big Data (1/2)Introduction to Big Data (1/2)
Week 7Introduction to Data VisualizationPlots and Grammar of Plots
Week 8Plotting with Matplotlib, Pandas, SeabornPractical aspects of data visualization
Week 9Data TransformationData Transformation with Python: numpy, pandas
Week 10Exploratory data analytics (1/2)Exploratory data analytics (2/2)
Week 11Advanced Data visualization: InteractivityBuffer
Week 12Course Project Week (No Lectures)Course Project Week (No Lectures)
Week 13Course Project Presentation (1/2)Course Project Presentation (2/2)

Lab schedule

Week 1Introduction to Python: Familiarization Exercises
Week 2Database design: Normal Forms
Week 3Buffer
Week 4SQL: Table creation, SQL queries, SQL and Python
Week 5XML, Python and XML
Week 6Buffer
Week 7Data Pre-processing in SQL
Week 8Plotting with Matplotlib (1/2)
Week 9Plotting with Matplotlib (2/2)
Week 10Data Transformation in Python
Week 11Exploratory 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:

  1. Transforming the data obtained into an appropriate normal formal and converting into a SQL database, and defining appropriate keys, data types and relationships.
  2. Data pre-processing using python and SQL queries from Python
  3. Data Transformation and Exploratory data analysis in Python
  4. Conclusion and Inference about the selected data set.

Lecture and Lab Material

Copyrighted: Available for reference on request