The Data Professional

Learning Objectives

  • Examine relevant industry practices of data science operations in order to build key skillsets for analysing and transforming large data sets in response to different business problems and to different needs..
  • Develop knowledge of fundamental programming concepts and problem-solving techniques used in data analysis, using suitable programming languages such as Python and SQL.
  • Develop an ethos of continuous professional development in the area of data science that enables the students to evaluate critically, design, and develop an effective data science system using contemporary tools and techniques.
  • Develop an understanding of the stakeholders’ perspective - consolidation of data sources, data preparation and aligning data to business objectives.
  • Develop an understanding of the cyber security risks that exist in the data management process.
  • Critically appraise the emerging trends, professional and ethical requirements for dealing with data science projects, and within which a Data Science professional must operate.

Collaborative Discussion 1

The aim of the assignment was to gain a basic understanding of Data Science, and how data scientists will shape the future, especially with the use of AI and machine learning. Explaining the legal, ethical, social, and professional responsibilities of Data Scientists. We were also asked to explain how the lifecycle of managing large-scale datasets.

To access the assignment, please click the link here.

Collaborative Discussion 2

For our second marked discussion, the focus was on GDPR and how organisations implement compliance procedures. We were tasked with using our own works 'IT Code of Conduct'. Encryption and pseudonymisation are the most common and effective strategies for ensuring the protection of personal data.

To access the assignment, you can view it here.

Data Analytics Report

Our third assignment for the module involved our first steps in analysing data. Using Transport for Wales as our case study, we embarked on preparing data analytics report. Using Unified Modelling Language (UML) to describe the dataset we were tasked with analysing. This 1500-word assignment prepared us for the final module ‘End of Module Assignment: Data Analytics Implementation’.

To access the assignment, you can view it here.

End of Module Assignment: Data Analytics Implementation

Following on from my assignment 3, ‘Data Analytics Report’. We set out to identify correlations and suggestions from our own analysation of the summary tables, provided by Transport for Wales. For my analysis, data was cleaned and saved into actionable CSV files. Those files were then imported into python (Jupyter Notebook) and the python libraries of Pandas, Numpy, and Plotly were used for visualisations.

To access my code, you can look at my notebook file, and you can view my full 2000-word report here.

Structured Query Language and MySQL

Structured query language (SQL) has been one of the focuses in this module. The lessons learnt in this module has been on the basics of SQL, which are:

  • SELECT statements (including select distinct).
  • JOINS statements (Difference between Left, Right and Inner joins).
  • LIMIT Command.
  • WHERE Clauses.
  • ORDER BY Function.
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    I’ve become very accustomed with SQL as I use the MySQL workbench in my current place of work. I’ve previously completed a Udacity course on SQL which covers some of the bullet points above. You can view some of the basic queries I learnt here.