BIG DATA ANALYTICS
Syllabus
EN
IT
Learning Objectives
LEARNING OUTCOMES:
The course provides an introduction to data preparation, data analysis and report creation
in SAS Visual Analytics.
Students will learn how to use this point-and-click SAS environment to easily access,
transform and modify data so that it's ready for analysis, and also how to visually explore
data to discover new insights.
This Data Visualization tool by SAS enable students to easily search for relationships,
trends and patterns to gain a deeper understanding of their data. Then create stunning
reports and dashboards that are shareable via the web and mobile devices.
In addition, students can enrich reports with powerful statistical models.
KNOWLEDGE AND UNDERSTANDING:
Knowledge and understanding of parametric and nonparametric statistical techniques applied to business, marketing, sales forecasting, and financial problems. By the end of the course, students should be able to understand (i) which data analysis and visualization tools to choose in order to represent statistical data in the best possible way; (ii) to know how to choose among different types of graphs those most suitable for the problem under consideration; and (iii) to explore data to discover possible patterns. In particular, students will know how to use:
- the techniques of graphical representation
- The techniques of tabular representation
- The use of SAS Visual Analytics
APPLYING KNOWLEDGE AND UNDERSTANDING:
Through examples on real datasets and the use of SAS Visual Analytics statistical software, various applications of the concepts explained in class will be shown. Students will be required to practice, both in class and at home, applying statistical methodologies to data sets and providing commentary and interpretation of the results obtained.
MAKING JUDGEMENTS:
Students will be able to choose the most appropriate statistical techniques and select the right set of variables from the data warehouse. Based on the results obtained, they will be able to provide an interpretation on the relationship between the variables under study. Students will increase their ability to critically and objectively analyze concrete situations, real phenomena and case studies.
COMMUNICATION SKILLS:
Students will be able to prepare statistical reports using graphs, tables, figures and more generally statistical software outputs and accompany them with appropriate comments.
LEARNING SKILLS:
Students will be able to access reading and understanding of scientific articles where graphical and tabular representation methods considered in the course syllabus are used. They will be able to identify the most appropriate methods for answering specific research questions.
The course provides an introduction to data preparation, data analysis and report creation
in SAS Visual Analytics.
Students will learn how to use this point-and-click SAS environment to easily access,
transform and modify data so that it's ready for analysis, and also how to visually explore
data to discover new insights.
This Data Visualization tool by SAS enable students to easily search for relationships,
trends and patterns to gain a deeper understanding of their data. Then create stunning
reports and dashboards that are shareable via the web and mobile devices.
In addition, students can enrich reports with powerful statistical models.
KNOWLEDGE AND UNDERSTANDING:
Knowledge and understanding of parametric and nonparametric statistical techniques applied to business, marketing, sales forecasting, and financial problems. By the end of the course, students should be able to understand (i) which data analysis and visualization tools to choose in order to represent statistical data in the best possible way; (ii) to know how to choose among different types of graphs those most suitable for the problem under consideration; and (iii) to explore data to discover possible patterns. In particular, students will know how to use:
- the techniques of graphical representation
- The techniques of tabular representation
- The use of SAS Visual Analytics
APPLYING KNOWLEDGE AND UNDERSTANDING:
Through examples on real datasets and the use of SAS Visual Analytics statistical software, various applications of the concepts explained in class will be shown. Students will be required to practice, both in class and at home, applying statistical methodologies to data sets and providing commentary and interpretation of the results obtained.
MAKING JUDGEMENTS:
Students will be able to choose the most appropriate statistical techniques and select the right set of variables from the data warehouse. Based on the results obtained, they will be able to provide an interpretation on the relationship between the variables under study. Students will increase their ability to critically and objectively analyze concrete situations, real phenomena and case studies.
COMMUNICATION SKILLS:
Students will be able to prepare statistical reports using graphs, tables, figures and more generally statistical software outputs and accompany them with appropriate comments.
LEARNING SKILLS:
Students will be able to access reading and understanding of scientific articles where graphical and tabular representation methods considered in the course syllabus are used. They will be able to identify the most appropriate methods for answering specific research questions.
Prerequisites
Basic knowledge of descriptive statistics, elements of probability, random variables (Probability density function, Cumulative density function, expected value and variance) and statistical inference (point estimation, properties of estimators, estimation methods, statistical test, confidence interval).
Program
Topic 1 Introduction to Big Data and Data preparation
Topic 2 Data Exploration; Association; Linear and nonlinear regression
Topic 3 Introduction to SAS Viya and Visual Analytics interface.
Topic 4 Loading, investigating and preparing data with SAS Visual Analytics.
Topic 5 Data analysis with Visual Analytics (descriptive statistics, distributions, correlations,
linear regression).
Topic 6 Designing interactive reports with Visual Analytics: prompts, actions, rules and
ranks.
Topic 2 Data Exploration; Association; Linear and nonlinear regression
Topic 3 Introduction to SAS Viya and Visual Analytics interface.
Topic 4 Loading, investigating and preparing data with SAS Visual Analytics.
Topic 5 Data analysis with Visual Analytics (descriptive statistics, distributions, correlations,
linear regression).
Topic 6 Designing interactive reports with Visual Analytics: prompts, actions, rules and
ranks.
Books
•Teaching materials
SAS Visual Analytics 1 for SAS Viya: Basics –PDF
•E-learning - SAS Visual Analytics 1 for SAS Viya: Basics
SAS Visual Analytics 1 for SAS Viya: Basics –PDF
•E-learning - SAS Visual Analytics 1 for SAS Viya: Basics
Bibliography
•SAS Visual Analytics 1 for SAS Viya: Basics –PDF
•E-learning - SAS Visual Analytics 1 for SAS Viya: Basics
•E-learning - SAS Visual Analytics 1 for SAS Viya: Basics
Teaching methods
Classroom teaching: a SAS expert will describe the main topics of the course and will
answer students’questions.
E-learning: a collection of videos, demos, and practices, that summarize the concepts
shown in classroom.
Case studies: in which students can practice with the supervision of the teacher.
answer students’questions.
E-learning: a collection of videos, demos, and practices, that summarize the concepts
shown in classroom.
Case studies: in which students can practice with the supervision of the teacher.
Exam Rules
•There will be two different parts for the evaluation:
First part, Project Work, that will include a team work to be finished in one day with a final presentation. The teamwork is based on a re-elaboration of an ugly report, that is not visually appealing, to be transformed in a report that is easy to read. You will be completely independent in the execution. The project work will attribute up to 4 points to be added to the final mark.
The groups can be decided by the students and must be made up of a minimum of 2 students up to a maximum of 4 for each group. It is mandatory to send the list of groups and their names. The Project Work will be online but it will not be a lesson. The groups of students will have to carry out the project work and present it at the end (power point is not necessary, they will present it directly from the software)
Second part, final exam: divided in two parts, one theoretical with questions to be answered with pure knowledge (with multiple choice questions, 1 h) and a practical part (with multiple choice and open answer questions, 1h), with action to be performed on the software to answer the questions. The theoretical questions are not related to the two introductory lessons of the prof. Borra, but they only refer to the use of SAS Visual Analytics.
For not attending students: the grade will be based only on the second part identical to the one for attending students.
The score obtained in this mid-term exam will be added to the final exam grade.
For assessment purposes, the following scheme will be used:
Unsuitable: major deficiencies and/or inaccuracies in the knowledge and understanding of the topics; limited capacity for analysis and synthesis, frequent generalisations and limited critical and judgmental skills, the topics are set out inconsistently and with inappropriate language;
18-20: barely sufficient knowledge and understanding of the topics with possible generalisations and imperfections; sufficient capacity for analysis synthesis and autonomy of judgement, the topics are frequently exposed in an incoherent way and with inappropriate/technical language;
21-23: Routine knowledge and understanding of topics; ability to analyse and synthesise correctly with sufficiently coherent logical argumentation and appropriate/technical language
24-26: Fair knowledge and understanding of the topics; Good analytical and synthetic skills with arguments expressed in a rigorous manner but with language that is not always appropriate/technical.
27-29: Comprehensive knowledge and understanding of the topics; considerable capacity for analysis and synthesis. Good autonomy of judgement. Arguments presented in a rigorous manner and with appropriate/technical language
30-30L: Excellent level of knowledge and thorough understanding of topics. Excellent analytical and synthetic skills and independent judgement. Arguments expressed in an original manner and with appropriate technical language.
First part, Project Work, that will include a team work to be finished in one day with a final presentation. The teamwork is based on a re-elaboration of an ugly report, that is not visually appealing, to be transformed in a report that is easy to read. You will be completely independent in the execution. The project work will attribute up to 4 points to be added to the final mark.
The groups can be decided by the students and must be made up of a minimum of 2 students up to a maximum of 4 for each group. It is mandatory to send the list of groups and their names. The Project Work will be online but it will not be a lesson. The groups of students will have to carry out the project work and present it at the end (power point is not necessary, they will present it directly from the software)
Second part, final exam: divided in two parts, one theoretical with questions to be answered with pure knowledge (with multiple choice questions, 1 h) and a practical part (with multiple choice and open answer questions, 1h), with action to be performed on the software to answer the questions. The theoretical questions are not related to the two introductory lessons of the prof. Borra, but they only refer to the use of SAS Visual Analytics.
For not attending students: the grade will be based only on the second part identical to the one for attending students.
The score obtained in this mid-term exam will be added to the final exam grade.
For assessment purposes, the following scheme will be used:
Unsuitable: major deficiencies and/or inaccuracies in the knowledge and understanding of the topics; limited capacity for analysis and synthesis, frequent generalisations and limited critical and judgmental skills, the topics are set out inconsistently and with inappropriate language;
18-20: barely sufficient knowledge and understanding of the topics with possible generalisations and imperfections; sufficient capacity for analysis synthesis and autonomy of judgement, the topics are frequently exposed in an incoherent way and with inappropriate/technical language;
21-23: Routine knowledge and understanding of topics; ability to analyse and synthesise correctly with sufficiently coherent logical argumentation and appropriate/technical language
24-26: Fair knowledge and understanding of the topics; Good analytical and synthetic skills with arguments expressed in a rigorous manner but with language that is not always appropriate/technical.
27-29: Comprehensive knowledge and understanding of the topics; considerable capacity for analysis and synthesis. Good autonomy of judgement. Arguments presented in a rigorous manner and with appropriate/technical language
30-30L: Excellent level of knowledge and thorough understanding of topics. Excellent analytical and synthetic skills and independent judgement. Arguments expressed in an original manner and with appropriate technical language.