Analyze the benefits of using data analytic techniques in every company department” 

The purpose of this program is to train computer engineers in the analyses performed in each company department so they can identify the most important needs and applications in each case. In this way, specialized knowledge will be acquired in order to choose an appropriate methodology. This is crucial if we take into account the enormous amount of data generated in companies on a daily basis. 

In view of the above, it is essential to have professionals who are familiar with current problems and can study viable solutions. There are different techniques and software tools thanks to which data can be analyzed and interpreted in a much more efficient way. With these tools, companies, analysts and scientists can understand and interpret data correctly. 

Each module contained in this Postgraduate diploma will review the fundamental aspects that computer engineers interested in this field need to know, which will allow them to develop the theoretical bases to produce the most appropriate graphical representations when using data science techniques. It also analyzes models that present greater versatility and adaptation for the analysis of time series, such as the models associated to economic series. 

Furthermore, it is a 100% online Postgraduate diploma that provides students with comfortable study and ease, wherever and whenever they want it. All you need is a device with Internet access to take your career one step further. A modality in accord with the current times and all the guarantees to position engineers in a highly demanded field. 

Gain specialized knowledge to perform predictive analytics and become a top level engineer” 

This Postgraduate diploma in Business Data Analysis contains the most complete and up to date academic program in the university landscape. The most important features of the program include:

• Practical case studies are presented by experts in engineering in data analysis 
• The graphic, schematic, and eminently practical contents with which they are created, provide scientific and practical information on the disciplines that are essential for professional practice  
• Practical exercises where the self assessment process can be carried out to improve learning  
• Its special emphasis on innovative methodologies   
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments  
• Content that is accessible from any fixed or portable device with an Internet connection 

Make this program the perfect opportunity to develop the formulation and basic properties in univariate time series models” 

The program’s teaching staff includes professionals from the sector who contribute their work experience to this training program, as well as renowned specialists from leading societies and prestigious universities.   

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive training programmed to train in real situations.   

The design of this program focuses on Problem Based Learning, which means the student must try to solve the different real life situations of that arise throughout the academic program. For this purpose, the student will be assisted by an innovative, interactive video system created by renowned and experienced experts. 

Determine the creation of dashboards and KPI's depending on the department with a program that will boost your career"

Gain specialized knowledge in data representation and analytics and stand out in a field of high international demand"

The syllabus for this Postgraduate diploma has been designed following the requirements and recommendations of the faculty, so each module offers a broad and detailed view of the spectral analysis of univariate time series, as well as the fundamental aspects related to the inference based on the periodogram and its interpretation. It also stands out in developing students' analytical skills, which will enable them to make coherent decisions in a competitive work environment.  

Estimate the probability and trend of a time series for a set time horizon and become a differentiating agent in your company” 

Module 1. Data Analysis in a Business Organization        

1.1. Business Analysis 

1.1.1. Business Analysis
1.1.2. Data Structure
1.1.3. Phases and Elements 

1.2. Data Analysis in the Business

1.2.1. Departmental Scorecards and KPIs 
1.2.2. Operational, Tactical and Strategic Reports
1.2.3. Data Analytics Applied to Each Department

1.2.3.1. Marketing and Communication
1.2.3.2. Commercial
1.2.3.3. Customer Service
1.2.3.4. Purchasing 
1.2.3.5. Administration
1.2.3.6. Human Resources (HR)
1.2.3.7. Production
1.2.3.8. IT

1.3. Marketing and Communication

1.3.1. KPIs to be Measured, Applications and Benefits
1.3.2. Marketing Systems and Data Warehouse
1.3.3. Implementation of a Data Analytics Framework in Marketing
1.3.4. Marketing and Communication Plan 
1.3.5. Strategies, Prediction and Campaign Management

1.4. Commerce and Sales 

1.4.1. Contributions of Data Analytics in the Commercial Area 
1.4.2. Sales Department Nees
1.4.3. Market Research 

1.5. Customer Service 

1.5.1. Loyalty 
1.5.2. Personal Coaching and Emotional Intelligence 
1.5.3. Customer Satisfaction

1.6. Purchasing  

1.6.1. Data Analysis for Market Research
1.6.2. Data Analysis for Competency Research
1.6.3. Other Applications

1.7. Administration  

1.7.1. Needs of the Administration Department
1.7.2. Data Warehouse and Financial Risk Analysis 
1.7.3. Data Warehouse and Credit Risk Analysis

1.8. Human resources.

1.8.1. HR and the Benefits of Data Analysis
1.8.2. Data Analytics Tools in the HR Department
1.8.3. Data Analytics Applications in the HR Department

1.9. Production  

1.9.1. Data Analysis in a Production Department
1.9.2. Applications
1.9.3. Benefits 

1.10. IT 

1.10.1. IT Department
1.10.2. Data Analysis and Digital Transformation  
1.10.3. Innovation and Productivity

Module 2. Graphical Representation of Data Analysis        

2.1. Exploratory Analysis 

2.1.1. Representation for Information Analysis
2.1.2. The Value of Graphical Representation
2.1.3. New Paradigms of Graphical Representation

2.2. Optimization for Data Science 

2.2.1. Color Range and Design
2.2.2. Gestalt in Graphic Representation
2.2.3. Errors to Avoid and Advice  

2.3. Basic Data Sources

2.3.1. For Quality Representation
2.3.2. For Quantity Representation
2.3.3. For Time Representation

2.4. Complex Data Sources

2.4.1. Files, Lists and Databases 
2.4.2. Open Data
2.4.3. Continuous Data Generation

2.5. Types of Graphs 

2.5.1. Basic Representations
2.5.2. Block Representation 
2.5.3. Representation for Dispersion Analysis
2.5.4. Circular Representations
2.5.5. Bubble Representations
2.5.6. Geographical Representations 

2.6. Types of Visualization

2.6.1. Comparative and Relational
2.6.2. Distribution
2.6.3. Hierarchical

2.7. Report Design with Graphic Representation 

2.7.1. Application of Graphs in Marketing Reports
2.7.2. Using Graphs in Scorecards and KPIs
2.7.3. Application of Graphs in Strategic Plans
2.7.4. Other Uses: Science, Health, Business 

2.8. Graphic Narration

2.8.1. Graphic Narration
2.8.2. Evolution 
2.8.3. Uses

2.9. Tools Oriented Towards Visualization 

2.9.1. Advanced Tools
2.9.2. Online Software
2.9.3. Open Source

2.10. New Technologies in Data Visualization 

2.10.1. Systems for Virtualization of Reality
2.10.2. Reality Enhancement and Improvement Systems
2.10.3. Intelligent Systems

Module 3. Predictability and Analysis of Stochastic Phenomena

3.1. Time Series

3.1.1. Time Series  
3.1.2. Utility and Applicability
3.1.3. Related Case Studies

3.2. Time Series

3.2.1. Seasonal Trend of ST
3.2.2. Typical Variations
3.2.3. Waste Analysis

3.3. Typology

3.3.1. Stationary
3.3.2. Non-Stationary
3.3.3. Transformations and Settings

3.4. Time Series Schemes 

3.4.1. Additive Scheme (Model)
3.4.2. Multiplicative Scheme (Model)
3.4.3. Procedures to Determine the Type of Model

3.5. Basic Forecast Methods

3.5.1. Media
3.5.2. Naïve
3.5.3. Seasonal Naïve
3.5.4. Method Comparison

3.6. Waste Analysis 

3.6.1. Autocorrelation
3.6.2. ACF of Waste
3.6.3. Correlation Test

3.7. Regression in the Context of Time Series 

3.7.1. ANOVA
3.7.2. Fundamentals
3.7.3. Practical Applications 

3.8. Predictive Methods of Time Series

3.8.1. ARIMA
3.8.2. Exponential Smoothing 

3.9. Manipulation and Analysis of Time Series with R

3.9.1. Data Preparation
3.9.2. Identification of Patterns
3.9.3. Model Analysis
3.9.4. Prediction

3.10. Combined Graphical Analysis with R 

3.10.1. Normal Situations
3.10.2. Practical Application for the Resolution of Simple Problems 
3.10.3. Practical Application for the Resolution of Advanced Problems 

A unique, key and decisive training experience to boost your professional development”