This module serves as an introduction to computational programming and data science fundamentals, exploring both theoretical concepts and practical applications. You will develop skills in extracting, storing, and analysing both numeric and textual data using the Python programming language.

1. Use the Python programming language to complete a range of programming tasks
2. Demonstrate familiarity with programming concepts and data structures
3. Use code to extract, store and analyse textual and numeric data
4. Carry out data analysis and statistical testing using code
5. Critically analyse and discuss methods of data collection, management and storage
6. Extract textual and numeric data from a range of sources, including online
7. Reflect upon the legal, ethical and social issues relating to data science and its applications

You’ll engage in a variety of learning activities tailored to your module, such as: 

- Face-to-face lectures and practical sessions serving as primary learning tools. Through the lectures, you will be taught theoretical aspects of each week’s topic with visually promoted examples. You will be involved the discussions via questions and interactive polls. Practical sessions will help you gain experience of the topics covered during lectures. Practical sessions will move from simple scenarios for which you can develop solutions at the beginning of the module through to more complex case-studies as the module moves on. 

- Self-paced online resources, including videos, web materials, e-books, and quizzes. Weekly lectures and practical sessions will be supported by sources from web/cloud for you to improve your experience more in cases when you could not get enough during the practical sessions. 

- Small group face-to-face sessions, including assistance classes and feedback sessions –> for some of the weeks, you will be given recap and feedback sessions depending on your feedback collected during the lectures.

Subject-Specific Skills:
•    Through this module, you will gain a solid foundation in Python programming, enabling you to write and understand code essential for data science applications, including reading and writing common data formats such as CSV, JSON, and Excel.
•    You will be trained to use key Python libraries like NumPy, pandas, and SciPy for data analysis, and master the use of visualization libraries such as Matplotlib, Seaborn, and Plotly to make complex data more comprehensible.
•    You will develop the ability to critically evaluate which data science tools are appropriate in various contexts, considering the ethical, social, and economic implications of their use.

Professional & Practical Skills:
•    Through practical programming exercises, you will enhance your programming skills, gaining hands-on experience in problem-solving and algorithmic thinking essential for real-world data science challenges.
•    By engaging in a comprehensive data science project, you will handle data analysis, reporting, and presentation, applying theoretical knowledge to practical scenarios and bridging the gap between classroom learning and industry practices.

Transferable/Employability Skills:
•    Throughout this module, you will build resilience and adaptability by tackling challenging programming tasks and data science projects, preparing you for dynamic and fast-paced work environments.
•    You will enhance your ability to communicate complex data findings through written reports and presentations, crucial for collaborating with stakeholders and conveying insights effectively.
•    This module will foster critical thinking by encouraging you to evaluate the suitability and implications of various data science tools and reflect on the ethical considerations and potential societal impacts of data science.

Summative Assessments: • 100% - Programming and Data Science Portfolio (Coursework) You will be given questions which are based on Python programming basics. This assessment will evaluate your capability to create algorithms, transferring this into programming language for some specific purposes using the learned programming structures (L.O. 1-3). Learning Outcomes 4-7 will be assessed by a piece of data analysis (including scraping, cleaning and pre-processing) and an associated report to communicate your findings. These artefacts will be submitted and assessed as a single portfolio that covers all learning outcomes of the module.

Formative Assessments: 1) Programming Exercises and Workshops: Weekly programming exercises and practice sessions will allow you to practice and receive feedback on your Python programming skills. These sessions will be directly aligned with the summative programming exercise, helping you build confidence and competence in creating algorithms and coding in Python.

These formative assessments are designed to provide continuous feedback and support, helping you to develop the necessary skills and knowledge to excel in the summative assessments.

The Opportunity for Reassessment In This Module:

Opportunities for re-assessment is only permitted provided you have not failed more credit than in the resit rule adopted by your programme. If the amount of credit you have failed is more than permitted by the relevant resit rule, you may be permitted to repeat study if you are within the threshold set for the Repeat rule adopted by your programme. You will be notified of your eligibility to resit/repeat any modules after the Examining Board in the Summer period.

PART 1
•    Algorithmic Thinking, and Int to Python Programming, 
•    Basic Variables and Sequences (lists, tuples and dictionaries)
•    Program Flow, If-Else, For-While Loops, 
•    User Defined Functions, “def” keyword
•    File I/O, 
•    Processing numerical, text and tabular data via Numpy, Regex, Pandas
PART 2
•    What is Big Data? 
•    Visualisation, 
•    Web Scraping, 
•    Descriptive Stats & Correlation Analysis,