Fundamentals of Data Analytics

BMAN11060 Fundamentals of Data Analytics 20 Full year Undergraduate Level 1 Ali Hassanzadeh Kalshani Unit coordinator 1 FHEQ level (Framework for Higher Education Qualifications) ' First part HE study/Bachelors ' European Credit Transfer & Accumulation System Rating : 10.0 The course sets the foundation for the Data Analytics theme of the ITMB curriculum. It introduces students to core concepts in data analytics, business intelligence and machine learning, while fostering a critical understanding of the assumptions underpinning these methodologies and the ethical and legal implications of data analysis. The course sets the foundation for the Data Analytics theme of the ITMB curriculum. It introduces students to core concepts in data analytics, business intelligence and machine learning, while fostering a critical understanding of the assumptions underpinning these methodologies and the ethical and legal implications of data analysis. The course unit aims to: 1.    To equip students with a critical understanding of data analytics, business intelligence and machine learning in a business setting; 2.    To help develop skills in the use of industry-leading software tools for business analytics, mainly Microsoft Excel and Tableau. At the end of the course students should: •    Understand the fundamentals of data analytics, business intelligence and machine learning, and be able to reflect on ethical and legal implications of data use. •    Appreciate the importance of data wrangling as the foundation of meaningful data analytics, business intelligence and machine learning pipelines. •    Be familiar with a variety of descriptive analytics and visualization tools, and understand the complementary function of these methodologies. •    Have the ability to competently select and apply relevant visualization and statistical tools to identify patterns and trends in large sets of data in real-world problems, and to critically evaluate the results obtained. •    Be able to formulate, test and interpret simple regression models. Students will gain proficiency in the use of Microsoft Excel and Tableau to communicate the findings of data analysis and support data-driven business decision-making. Introduction to Data Analytics<ul> <li> Data Analysis model (eg: CRISP-DM) and BI/DA tools.</li> <li> Ethical and Legal Aspects of Data Analysis</li> <li> Data Wrangling</li></ul>Introduction to Business Intelligence<ul> <li> Statistical Tools</li> <li> Data Visualization</li></ul>Introduction to Machine Learning<ul> <li> Regression</li> <li> Bias Variance Trade-Off / Overfitting</li></ul>Introduction to Data Visualization in TableauIntroduction to Data Analytics in Excel<ul> <li> Descriptive Statistics Toolbox</li> <li> Vlookups / Match & Index</li> <li> Pivot Tables</li> <li> Regression Analysis</li> <li> Use of Excel Macros</li> <li> VBA Scripting</li></ul> SEMESTER 1:1 hour of lecture and 1.5 hours of lab (10 weeks)SEMESTER 2:1 hour of lecture and 1.5 hours of labs (10 weeks) 0 Other 100% Formative assessment:<ul><li>Lab exercises</li></ul>Summative assessment:<ul><li>Individual on campus 20-minute quizzes (4 submissions @ 10% each), 40%</li><li>Individual Excel dashboard and accompanying coursework report, 60%</li></ul> Informal advice and discussion during lectures or seminars.Response to student emails and questions from a member of staff including feedback provided via an online discussion forum.Written and/or verbal comments on assessed and non-assessed work.Generic feedback posted on Blackboard regarding overall examination performance. Only available to students on BSc ITMB.Academic programmes that course is available to: ITMB N Alberto Ferrari, Analysing Data with Power BI and Power Pivot for Excel, 2016Anil Maheshwari, Data Analytics Made Accessible, 2019 Edition Lectures 20 Practical classes & workshops 30 0 150 For Academic Year 2024/25Updated: March 2024Approved by: March UG Committee