Data Analytics with Programming Tools

BMAN24771 Data Analytics with Programming Tools 20 Semester 1 Undergraduate Level 5 Luis Ospina-Forero Unit coordinator Alliance Manchester Business School 1 FHEQ level (Framework for Higher Education Qualifications) ' Last part of a Bachelors ' European Credit Transfer & Accumulation System Rating : 10.0 With the increasing availability of personal, social and business data, data analytics has become an essential and important part of business analytics and business intelligence. This importance is driven by the versatility and flexibility provided by the large variety of data analytics techniques and the more frequent and mainstream use of data analytic programming languages such as R, for problem-solving. This course unit will therefore continue to introduce students to new methods for data analytics, emphasizing the potential flexibility provided by a mainstream data analytics programming language. This unit will provide students with an introduction to a statistical/data analytics programming language in order to tackle multiple types of data sources and to be able to provide insights into different business avenues.  Students undertaking this unit will increase their data analytics toolbox, will learn how to use one of the most popular data analytic programming languages, and will learn how to create different visualisations for the effective communication of business insights.  With the increasing availability of personal, social and business data, data analytics has become an essential and important part of business analytics and business intelligence. This importance is driven by the versatility and flexibility provided by the large variety of data analytics techniques and the more frequent and mainstream use of data analytic programming languages such as R, for problem-solving. This course unit will therefore continue to introduce students to new methods for data analytics, emphasizing the potential flexibility provided by a mainstream data analytics programming language. This unit will provide students with an introduction to a statistical/data analytics programming language in order to tackle multiple types of data sources and to be able to provide insights into different business avenues.  Students undertaking this unit will increase their data analytics toolbox, will learn how to use one of the most popular data analytic programming languages, and will learn how to create different visualisations for the effective communication of business insights.  Students will learn how to use a prominent data analytics programming language. They will gain practical experience in different analytical techniques, such as network analytics and predictive modelling. This course, in addition to the analytical techniques, will also emphasize in the creation and usage of programmable visualisations for the communication of business insights by means of lab studies. <o:p></o:p> <ul><li>Explain the fundamental functionalities of one of the most popular programming languages for data analytics.</li><li>Explain the core theoretical principles underlying data analytics models and tools.</li></ul> <ul><li>Analyse and model business datasets using data analytics models and tools to support decision-making. </li></ul> <ul><li>Apply data analytical methods for descriptive tasks.</li><li>Apply data analytical methods for predictive tasks.</li></ul> <ul><li>Demonstrate the ability to use one of the most popular data analytics programming languages.</li><li>Demonstrate the ability to analyse different types of data.</li><li>Demonstrate the ability to read data visualisations.</li><li>Demonstrate the ability to produce effective data visualisations. </li></ul> Syllabus (indicative curriculum content):  <o:p></o:p><ul><li class="MsoNormal" style="mso-list:l1 level1 lfo1;tab-stops:list 36.0pt;">Introduction to programming in a leading data analytics programming language.   <o:p></o:p></li><li class="MsoNormal" style="mso-list:l3 level1 lfo2;tab-stops:list 36.0pt;">Programming basics: Data structures    <o:p></o:p></li><li class="MsoNormal" style="mso-list:l0 level1 lfo3;tab-stops:list 36.0pt;">Programming basics:  For-loops and conditional statements.  <o:p></o:p></li><li class="MsoNormal" style="mso-list:l7 level1 lfo4;tab-stops:list 36.0pt;">Programming basics:    <o:p></o:p></li></ul>   Usage of packages (libraries), read/write external data and standard statistical summary functions.   <o:p></o:p><ul><li class="MsoNormal" style="mso-list:l2 level1 lfo5;tab-stops:list 36.0pt;">Data visualisation.   <o:p></o:p></li><li class="MsoNormal" style="mso-list:l6 level1 lfo6;tab-stops:list 36.0pt;">Introduction to Network Analytics.   <o:p></o:p></li><li class="MsoNormal" style="mso-list:l5 level1 lfo7;tab-stops:list 36.0pt;">Introduction to Predictive modelling.</li><li class="MsoNormal" style="mso-list:l5 level1 lfo7;tab-stops:list 36.0pt;">Introduction to Text analytics.</li></ul> Lectures: 22 HoursPractical classes & workshops: 18 HoursIndependent study hours: 160 hours    0 Other 100% Formative Assessment:<o:p></o:p>Online quizzes reinforcing the understanding of content provided and of reading material, and which consist of a mix of open-ended questions, multiple choice questions, calculated response questions, fill in the blanks, and others. <o:p></o:p>Summative Assessment:<o:p></o:p>Two mid-term quizzes (2x 30% = 60%)<o:p></o:p>Coursework accompanied of coding script (40%)<o:p></o:p> <ul><li>General feedback released on Canvas</li><li>Individual feedback released on Canvas</li></ul> BMAN11060 Fundamentals of Data Analytics Pre-Requisite Compulsory Pre-requisite units: BMAN11060 Fundamentals of Data AnalyticsCore/Compulsory/Optional/ Free Choice: Core and only available for BSc ITMB with/without IPE.Programmes to which this course unit contributes (including cross faculty/school): BSc (Hons) Information Technology Management for Business/BSc (Hons) Information Technology Management for Business with Industrial Experience  N <ul><li style="margin-left:18.0pt;"><o:p></o:p>An introduction to statistical learning : with applications in R – Author: James, Gareth Additional Person Name: Witten, Daniela,; Hastie, Trevor,; Tibshirani, Robert<o:p></o:p></li></ul> <ul><li class="MsoListParagraphCxSpFirst" style="line-height:normal;margin:0cm 0cm 0cm 18.0pt;mso-add-space:auto;mso-list:l0 level1 lfo1;">Introductory Statistics for Business and Economics : Theory, Exercises and Solutions – Author: Ubøe, Jan.<o:p></o:p></li></ul> <ul><li class="MsoListParagraphCxSpMiddle" style="line-height:normal;margin:0cm 0cm 0cm 18.0pt;mso-add-space:auto;mso-list:l0 level1 lfo1;">Applied statistics for business and management using Microsoft Excel – Author: Herkenhoff, Linda, Additional Person Name: Fogli, John, <o:p></o:p></li></ul> <ul><li class="MsoListParagraphCxSpMiddle" style="line-height:normal;margin:0cm 0cm 0cm 18.0pt;mso-add-space:auto;mso-list:l0 level1 lfo1;">Networks : an introduction – Author: Newman, M. E. J. (Mark E. J<o:p></o:p></li></ul> <ul><li class="MsoListParagraphCxSpMiddle" style="line-height:normal;margin:0cm 0cm 0cm 18.0pt;mso-add-space:auto;mso-list:l0 level1 lfo1;">An Introduction to Applied Multivariate Analysis with R – Author: Everitt, Brian. author. Additional Person Name: Hothorn, Torsten. Author<o:p></o:p></li></ul> <ul><li class="MsoListParagraphCxSpMiddle" style="line-height:normal;margin:0cm 0cm 0cm 18.0pt;mso-add-space:auto;mso-list:l0 level1 lfo1;">Text mining in practice with R – Author: Kwartler, Ted, <o:p></o:p></li></ul> <ul><li class="MsoListParagraphCxSpLast" style="line-height:normal;margin:0cm 0cm 0cm 18.0pt;mso-add-space:auto;mso-list:l0 level1 lfo1;">Storytelling with Data: A Data Visualization Guide for Business Professionals – Author: Nussbaumer Knaflic, Cole <o:p></o:p></li></ul> <ul><li class="MsoListParagraph" style="line-height:normal;margin:0cm 0cm 0cm 18.0pt;mso-add-space:auto;mso-list:l0 level1 lfo1;">Wiley & Sons, Incorporated Place of Publication: Hoboken Publication Date: 2015 <o:p></o:p>  </li></ul> Lectures 22 Practical classes & workshops 18 0 160