HOME

Learning Objectives

I will learn how to :

• Examine relevant industry practices of data science operations to build key skillsets for analyzing and transforming large data sets in response to different business problems and 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 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, 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.

Projects and Professional skills

Apart from the End of Module Evaluations described below, I also participated in two Collaborative Discussions with my peers in the form of essays where we discussed issues about the roles and responsibilities (legal, ethical, social, and professional) of a Data Science professional and the critical analysis of architecture, design, development methodology, querying and the lifecycle of managing large-scale datasets. I also completed coding exercises in the Codio online IDE and gained skills in Python programming and Advanced SQL. Lastly, I gained skills in html programming while building this e-Portfolio.

Data Analytics Report – Data Processing Pipeline and Evaluation

I learned how to critically analyze architecture, design, development methodology, querying, and the lifecycle of managing large-scale datasets.

Learn more

End of Module Assignment: Data Analytics Implementation

I learned how to distinguish between and critically reflect on the solutions of various data analytics approaches that support the business decision-making process as well as how to apply and evaluate critically the various methods, tools, technologies, and success factors applied to a data science project in order to develop an effective plan and delivery of solutions to a business problem.

Learn more