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PG Diploma in Business Intelligence

Universidad Católica de Murcia (UCAM)

This course provides an overview of the technology of Business Intelligence (BI) and the application of Business Intelligence to an organization’s strategies and goals.

Program Overview

Business Intelligence (BI) refers to technologies, applications, and practices for the collection, integration, analysis, and presentation of business information. The purpose of business intelligence is to support better business decision-making. This course provides an overview of the technology of BI and the application of BI to an organization’s strategies and goals.

Training Key Features

  • Flexible

    9 Months

  • Blended Learning

    Live classroom and Live online class.

  • +971 6 5310 843

    (09:00am - 17:30pm)

Partners of this Programme

Universidad Católica de Murcia (UCAM), founded in 1996, is a fully-accredited European University based out of Murcia, Spain. With learning centres in the Middle East and Southeast Asia, UCAM aims to provide students with the knowledge and skills to serve society and contribute to the further expansion of human knowledge through research and development.

The university offers various courses, including 30 official bachelor’s degrees, 30 master’s degrees and ten technical higher education qualifications through its Higher Vocational Training Institute, in addition to its in-house qualifications and language courses. The programmes offered are distinguished in Europe and worldwide, with good graduate employability prospects as well.

UCAM is accredited by ANECA (National Agency for Quality Assessment and Accreditation of Spain) and the Ministry of Education regarding 17 of its undergraduate degrees.

Why this Course ?

1 Course

Choosing a course of study that you have a strong inclination to pursue a UK qualifying degree or for a skilled set is a good start in pursuing your educational goals. At ECX, you get a triple power MBA degree.

2 Place of Study

In order to pursue their dream education, the key factor is that the students need ease in accessing the centre and at ECX we come to your nearest city to overcome any challenges faced in commutation or travelling abroad without compromising on the quality of education.

3 Affordable Fee

Quality education abroad is highly expensive. At ECX you get the benefit to enroll for course that is affordable with flexible payment options.

4 Academic Support

You get enrolled to a UK degree, with blended teaching methodology and 360-degree academic assistance through our faculties with international standards for attaining a business management degree.

5 Career Opportunities

You become an industry-ready business professional on completion of the degree as it brings in more of a realistic pursuit, thus transforming you with the better skill sets to approach the career market further.

Course Resources

For more detailed information about the course, please click on the links below.

Program Details

Eligibility

Students seeking admission to the course may have to fulfill the following criteria/requirements.

  • Bachelor’s Degree from a recognized University
  • Proficiency in the English language

Learning Path

Module description

This module discuss the basics of Python programming language and explores how to set up Python environment to work with data. Demonstrate different parts of Python code such as keywords, variables, data types, statements, functions, loops, and libraries, and get familiarized with programming in python. This module offers a guide to the parts of the Python programming language and its data-oriented library ecosystem and tools. The module focuses specifically on Python programming, libraries, and tools needed for data analysis. Essential Python libraries covered in this module are NumPy, pandas & matplotlib.

Learning Outcomes

L01. Learning python structure and how to write programs in it.

L02.Basic concepts of Python, its syntax, functions, and conditional statements.

L03. Operate Pandas to sort through and rearrange data, run analyses, and build data frames

L04.Understand packages to enable them to write scripts for data manipulation and analysis.

Content Covered

Basic Python Programming

  • Variable and data types
  • Conditional statements
  • Loops
  • Functions

Essential Python libraries for data

  • Pandas

  • Numpy

  • Matplotlib

 

Module description

Mathematics have a significant role in the foundation for programming and this module is designed to help students master the mathematical foundation required for writing programs and algorithms for Business Intelligence. A business intelligence system offers decision-makers information and know edge gleaned from data through mathematical models and algorithms. In certain circumstances, this activity may be reduced to calculations of totals and percentages, graphically represented by basic histograms, although more complex analyses need the development of comprehensive optimization. The module covers three main mathematical theories: Linear Algebra, Statistics, and Probability Theory

Learning Outcomes

L01. Acquire a fundamental understanding of the analytical techniques and software tools necessary to effectively generate useful information from structured and unstructured datasets of any size chart

L02. Gain experience in using the tools and techniques of Business Intelligence to structure and complete projects focused on obtaining actionable insights from complex data.

L03. Dive deeply into a chosen area of practice to fully prepare to use the knowledge gained in the program to add significant value in a professional setting

L04. Be able to utilize knowledge and skills to continue learning and adapting to new data science technologies

Content Covered

  • Linear algebra 
  • Probability 
  • Statistics 
  • Data Cleaning 
  • Data Pre-processing 
  • Statistical tools 
  • CSV 
  • Excel

 

Module Description

This Module covers fundamental topics related to the construction and usage of databases, database systems, and methodologies for data visualization. Organizations store data in two kinds of databases: operational and analytical. Operational database themes include database requirements, entity relationship modeling, relational modeling database constraints, update anomalies, normalization, Structures Query Language (SQL), and data quality. Once data is cleansed and saved, data visualization is utilized to best effectively convey the information contained in the data. The Modulecovers data visualization ideas borrowed from statistics, perception, graphic and informtion design, and data mining. Learners will study visual representation approaches that assist in comprehending complex data and concepts.

Learning Outcomes

L01.Acquire a fundamental understanding of the analytical techniques and software tools necessary to effectively generate useful information from structured and unstructured datasets of any size charts

L02.Gain experience in using the tools and techniques of data science to structure and complete projects focused on obtaining actionable insights from complex data

L03.Dive deeply into a chosen area of practice to fully prepare to use the knowledge gained in the program to add significant value in a professional setting

L04.Be able to utilize knowledge and skills to continue learning and adapting to new data science technologies

Content Covered

  • Data visualization introduction
  • Configuring Data Environment
  • Types of charts
  • Introduction to Database concepts
  • Database Environment
  • PostgreSQL Setup
  • Joins and Sub Queries
  • PostgreSQL Connectivity
  • Relational Model
  • Entity Relationship Model
  • ORM Overview
  • Basic SQL Tables
  • DB creation
  • Data modeling
  • Constraints and Data Manipulation
  • SQL CRUD operations
  • Django’s Database CRUD Operations
  • Exploratory Visualization

 

Module Description

Business intelligence projects are helping various firms utilize and analyze the massive quantity of information accessible today. This Module aims to present a data visualization project tutorial for Information Systems (IS) education. The applied BI lesson was aimed at assisting students in understanding how to develop and analyze a heat map using SQL and Server Data Tools (SSDT). Learners comprehend how to make judgments based on significant volumes of data by presenting it in visual shape. This Module introduces Learners to the decision-making capacity generated from data visualization. Learn to shape and change data before the data analysis with Tableau Query Editor. Filter the information in reports by location and decide how these filters interconnect and interact with other images in the report.

Learning Outcomes

L01. Describe the ideas and elements of Business Intelligence (BI) and critically examine the usage of BI for assisting decision-making in a business

L02. Learn to shape and transform your data before the data analysis using Tableau Query Editor.

L03- Filter the information in your reports by location and control how these filters interconnect and interact with other visuals in your report.

L04. Learn how to design a dashboard using a real dataset, several types of data visualization, PowerBI plots/charts, including Tableau’s Data, Model, and Report views.

Content Covered

  • What Is Business Intelligence 
  • Applications and use cases of Business intelligence 
  • BI in Decision Making 
  • PowerBI 
  • Tableau
  • Plots
  • Charts 
  • Data, Model and Report Views
  • Making Report Views
  • Data Visualization 
  • Tableau Query Editor
  • Dashboard design principles 
  • Dashboard interactivity 
  • Connected “drill-down” dashboards
  • Advanced Tableau
  • Large datasets 
  • Fiscal Year Calculations 
  • Parameters

 

Module Description

Business intelligence encompasses tools and strategies for data collection, analysis, and visualization to aid executive decision-making in any business. Data mining covers statistical and machine-learning approaches to develop decision-making models from raw data.In this Module, we desire to discuss and classify data mining activities concerning inquiry goals and analysis approaches. Learners will also examine the relevant qualities of the input data. Finally, we shall discuss the data mining process and its articulation in several stages.Data mining methods in this module include decision trees, regression, artificial neural networks, cluster analysis, and many more. Text mining, web mining, and big data are also addressed in an accessible method. An introduction to data modeling is offered for individuals untrained in this field.

Learning Outcomes

L01. Discuss the fundamental data mining concepts such as classification, clustering, regression, and unsupervised learning.

L02. Introduction to data mining algorithms

L03. Understanding the data mining process and techniques

L04. Engaging in meaningful discussions about pattern evaluation metrics and investigating techniques for mining various patterns, including sequential and sub-graph patterns.

Content Covered

  • Data understanding 
  • Decision trees 
  • Regression analysis 
  • Cluster Analysis 
  • Introduction to Data mining
  • Artificial Neural Networks
  • Association Rule Mining
  • Data Mining in a Python-based environment
  • What is a data warehouse
  • How to find patterns?
  • Affinity Analysis
  •  Product Recommendation
  • Text mining
  • Web mining 
  • Data Preparation 
  • Data Modeling 
  • Identifying Patterns 
  • Data warehousing

 

Module Description

This module offers an introduction to both the theoretical and practical elements of the design and implementation of algorithms that allow computers to “learn” from examples (i.e., Machine Learning). The new paradigm will be established by providing machines with examples from which they can learn the relevant rules to accomplish a task, rather than programming machines by defining a set of instructions that specify precisely how they should perform a task. Learners receive an in-depth introduction to Supervised and Unsupervised Machine Learning topics. The Module will cover essential Machine Learning methods for classification, regression, clustering, dimensionality reduction, and data modeling along with the basics of HTML/CSS, and Git version control system (VCS) to Build and Deploy a model for WebView.

 

 

Learning Outcomes

L01. Learn about training data, and how to use a set of data to discover potentially predictive relationships.

L02. Master machine learning techniques, including supervised and unsupervised learning and hands-on modeling, rounding out your artificial intelligence education.

L03.Learn popular machine learning algorithms, Feature Selection, and the Mathematical intuition behind them. 

L04.Learn the basics of HTML/CSS, and Git version control system (VCS) to Build and Deploy a model for WebView

Content Covered

  • Introduction to machine learning
  • Machine Learning Algorithms 
  • Feature Selection
  • Git Version control system 
  • Supervised Learning
  • Unsupervised learning 
  • ML Deployment

 

Module Description

This module aims to discuss and explain the role of Business Intelligence in an organization and its influence on its overall performance and competence. Learners will be encouraged to pick a research/development project that displays their past learning in the Business Intelligence domain. In this project, learners will exhibit the abilities that they have gained in Business Intelligence by applying new knowledge to a real-life situation and utilizing that experience to produce an effective solution. The Business Intelligence Capstone project will allow learners to show mastery of the program curriculum as they produce their data warehouse and report significant results to colleagues and project stakeholders. Students will produce a final thesis paper that describes the project, techniques, essential outcomes, and suggestions. In addition to delivering a working data warehouse and executive dashboard, students will produce a presentation that explains their project, findings, and suggestions using suitable data visualization and infographics

Learning Outcomes

LO1: Conduct independent research and development within the context of Business Intelligence

LO2: Produce detailed documentation to a standard expected of a professional in the field of Business Intelligence

LO2: Produce detailed documentation to a standard expected of a professional in the field of Business Intelligence

LO4: Data modeled to fulfill the business goals and can be used in decision-making with inventive solutions for a range of situations

Content Covered

  • Clarifying the terms of the research
  • Suggested areas of reading
  • Apply the knowledge base and abilities taught throughout the course to a real-world scenario
  • Identify the methodology or algorithm that will handle the proposed challenge
  • Establishing a research timetable, including initial dates for further meetings between the student and supervisor
  • Advising students about appropriate standards & conventions concerning the assessment.
  • Providing means of contact in addition to tutorials
  • Educate learners to research and write their results and thoughts correctly, clearly, logically, and to a high-professional degree. 

 

Course Team