The aim of this course is to familiarize students with programming tools

Overview
The aim of this course is to familiarize students with programming tools which allow them to
access, collect, manage, analyze, visualize, and understand urban big data efficiently and
effectively. It will cover techniques required to extract data from on-line Application Protocol
Interfaces (APIs), set up a database for holding data in a way which enables efficient analysis,
and statistic/machine learning and visualization tools. It will cover best practices in relation to
coding (Python, SQL/NoSQL queries), collaborating on coding projects (Unix Shell, Git, and
GitHub), and reproducibility of analyses (Jupyter Lab/Notebook).
Students will undertake a data science project which requires them to demonstrate the skills
which they have acquired during the course. You will be required to submit a single Jupyter
Notebook and a HTML file generated from the Jupyter Notebook including all your Python
code and markdown-style written report, with a maximum of 3,000 words (not including code
and reference). You will need to submit your final assignment through Moodle.
2. Content and Format
The final data science report should broadly follow the style of a quantitative journal article,
with the exception that you should focus on the data analysis and explanation of your data
analysis. It is not necessary to include a detailed literature review, though you may choose to
cite papers to support some of your choices e.g., your research question, your choice of
variable, the assumptions you make, and so on. The data science report should outline what
your research question is and what data you will use to address it. You will analyze your data
by using the tools and packages you have learnt in the classroom though using extra Python
packages to achieve your project goals is highly favorable. Your analysis should include:
• Research questions and project objectives with the support of academic literature
• Data collection methods, either through API, online scraping, or explaining the data
sources
• Understanding your data (data types, summary statistics, data visualization, etc.)
• Data cleaning (missing values, outliers, date/time transformation, data errors, etc.)
• Feature engineering (categorical variables to dummy variables,
normalization/standardization, feature combination, etc.)
• Data analysis (time series analysis, machine learning, spatial analysis, advanced
regression analysis, etc.)
• A summary of your findings and suggestion from your data analysis
Students are required to keep to within an additional 10 percent of the word limit given for
an assignment – there are penalties on assignments that are longer than this. Submissions
that go 10-14% over the word limit on an assessment will be subject to a 1 point deduction;
15-19% over a 2 points deduction; 20-24% over a 3 points deduction and 25% or more over
will be awarded a fail (zero) and required to resubmit as a second attempt.
3. Marking
As usual, the purpose of the assignment is to give students the opportunity to demonstrate
their learning in relation to the course’s intended learning outcomes. The outcomes of this
course are:
• Set up, connect to and query a simple relational and non-relational database by Python
• Retrieve and analyze data from an Application Protocol Interface (API)
• Perform basic machine learning tasks
• Write code according to best practice and produce tidy data
• Collaborate effectively with other analysts using appropriate tools
• Produce documentation for their work which makes the processes behind analyses
transparent and reproducible.