Maximize your professional potential by studying a program that will help you position yourself as a data science manager” 

The program addresses data science from a technical and business perspective, and it offers all the necessary knowledge, allowing professionals to extract as much information as possible. Therefore, computer engineers or similar professionals interested in this area will be able to analyze in detail the different algorithms and platforms as well as the most modern tools used in data exploration, visualization, manipulation, processing and analysis. All of the above is complemented with the business skills required to reach an executive level profile capable of making key decisions in a company. The new multidisciplinary knowledge that students will acquire after completing the program will help them to position themselves as Data Science Officers (DSO) in companies of all sizes.

Similarly, the approach to data analysis from both perspectives makes this program a perfect educational experience that covers everything professionals that need to know in order to handle information and subsequently apply it as a fundamental asset for any organization.

The program will initially address the importance of using a good analysis system in the company, where each department can benefit. Likewise, specialized knowledge focused on the typology and life cycle of available resources will be developed, for which students will be instructed in basic statistics.

As the program progresses, students will be introduced to the models that present greater versatility and adaptability for the analysis of time series, such as the models associated to economic series. The program will lastly provide a wide variety of cases of use and cases where artificial intelligence and data science have been implemented in today's world.

With the Master's Degree program, computer engineers will be able to specialize in Data Science, becoming the perfect opportunity to drive their professional career towards a managerial position in their respective departments. All this will be feasible thanks to a 100% online program, which adapts to the daily needs of its students. They will only require a device with an Internet connection to start developing a complete professional profile with international projection.   

If you are looking for a program that allows you to increase your skills and position yourself as a Data Science Officer (DSO) then then this is the course for you, in TECH you have found your place" 

The Master's Degree in Data Science Management (DSO, Data Science Officer) contains the most complete and up-to-date educational program on the market. The most important features include: 

• Practical cases studies are presented by experts in Engineering in data analysis
• The graphic, schematic, and practical contents with which they are created, provide scientific and practical information on the disciplines that are essential for professional practice
• Practical exercises where self-assessment can be used to improve learning
• 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

Prepare yourself to make decisions of scientific value and implement strategies that improve company department functions” 

The program’s teaching staff includes professionals from the sector who contribute their work experience to this 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 knowledge programmed to learn in real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise throughout the program. You will be assisted by an innovative, interactive video system made by renowned experts with extensive experience in Data Science Management and as Data Science Officers. 

Empower your career by creating dashboards and KPIs depending on the department you work in"

Develop specialized knowledge related to data management and manipulation for Data Science processes. You will become a successful DSO"

In a world dominated by data, it is important to know the main systems that are responsible for generating and storing it for further analysis. Therefore, a program has been designed to meet the preparatory requirements of professionals who wish to specialize in the most complete and current techniques used in data processing and knowledge extraction, from both a theoretical and practical perspective. As a result, computer engineers will be able to advance their technical knowledge while also developing a business profile.

Generate specialized knowledge in the software architectures and systems required for intensive data use” 

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. 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. Data and Information Management and Manipulation in Data Science    

2.1. Statistics. Variables, Indices and Ratios

2.1.1. Statistics 
2.1.2. Statistical Dimensions 
2.1.3. Variables, Indices and Ratios 

2.2. Type of Data 

2.2.1. Qualitative 
2.2.2. Quantitative 
2.2.3. Characterization and Categories 

2.3. Data Knowledge from the Measurements

2.3.1. Centralization Measurements 
2.3.2. Measures of Dispersion
2.3.3. Correlation 

2.4. Data Knowledge from the Graphs

2.4.1. Visualization According to Type of Data 
2.4.2. Interpretation of Graphic Information 
2.4.3. Customization of Graphics with R 

2.5. Probability

2.5.1. Probability 
2.5.2. Function of Probability 
2.5.3. Distributions 

2.6. Data Collection 

2.6.1. Methodology of Data Collection 
2.6.2. Data Collection Tools 
2.6.3. Data Collection Channels 

2.7. Data Cleaning 

2.7.1. Phases of Data Cleansing 
2.7.2. Data Quality
2.7.3. Data Manipulation (with R) 

2.8. Data Analysis, Interpretation and Evaluation of Results

2.8.1. Statistical Measures 
2.8.2. Relationship Indices 
2.8.3. Data Mining 

2.9. Data Warehouse 

2.9.1. Components  
2.9.2. Design 

2.10. Data Availability 

2.10.1. Access 
2.10.2. Uses 
2.10.3. Security

Module 3. Devices and IoT Platforms as a Base for Data Science

3.1. Internet of Things

3.1.1. Internet of the Future, Internet of Things
3.1.2. The Industrial Internet Consortium

3.2. Architecture of Reference 

3.2.1. The Architecture of Reference
3.2.2. Layers
3.2.3. Components

3.3. Sensors and IoT Devices 

3.3.1. Principal Components
3.3.2. Sensors and Actuators

3.4. Communications and Protocols

3.4.1. Protocols. OSI Model
3.4.2. Communication Technologies

3.5. Cloud Platforms for IoT and IIoT 

3.5.1. General Purpose Platforms 
3.5.2. Industrial Platforms
3.5.3. Open Code Platforms

3.6. Data Management on IoT Platforms

3.6.1. Data Management Mechanisms. Open Data
3.6.2. Data Exchange and Visualization

3.7. IoT Security

3.7.1. Requirements and Security Areas
3.7.2. Security Strategies in IIoT

3.8. Applications of IoT

3.8.1. Intelligent Cities
3.8.2. Health and Fitness
3.8.3. Smart Home
3.8.4. Other Applications

3.9. Applications of IIoT

3.9.1. Fabrication
3.9.2. Transport
3.9.3. Energy
3.9.4. Agriculture and Livestock
3.9.5. Other Sectors

3.10. Industry 4.0 

3.10.1. IoRT (Internet of Robotic Things)
3.10.2. 3D Additive Manufacturing
3.10.3. Big Data Analytics

Module 4. Graphical Representation of Data Analysis

4.1. Exploratory Analysis 

4.1.1. Representation for Information Analysis
4.1.2. The Value of Graphical Representation
4.1.3. New Paradigms of Graphical Representation

4.2. Optimization for Data Science 

4.2.1. Color Range and Design
4.2.2. Gestalt in Graphic Representation
4.2.3. Errors to Avoid and Advice

4.3. Basic Data Sources

4.3.1. For Quality Representation
4.3.2. For Quantity Representation
4.3.3. For Time Representation

4.4. Complex Data Sources

4.4.1. Files, Lists and Databases 
4.4.2. Open Data
4.4.3. Continuous Data Generation

4.5. Types of Graphs 

4.5.1. Basic Representations
4.5.2. Block Representation 
4.5.3. Representation for Dispersion Analysis
4.5.4. Circular Representations
4.5.5. Bubble Representations
4.5.6. Geographical Representations 

4.6. Types of Visualization

4.6.1. Comparative and Relational
4.6.2. Distribution
4.6.3. Hierarchical

4.7. Report Design with Graphic Representation 

4.7.1. Application of Graphs in Marketing Reports
4.7.2. Application of Graphs in Scorecards and KPIs
4.7.3. Application of Graphs in Strategic Plans
4.7.4. Other Uses: Science, Health, Business 

4.8. Graphic Narration

4.8.1. Graphic Narration
4.8.2. Evolution 
4.8.3. Uses

4.9. Tools Oriented Towards Visualization 

4.9.1. Advanced Tools
4.9.2. Online Software
4.9.3. Open Source

4.10. New Technologies in Data Visualization 

4.10.1. Systems for Virtualization of Reality
4.10.2. Reality Enhancement and Improvement Systems
4.10.3. Intelligent Systems

Module 5. Data Science Tools            

5.1. Data Science

5.1.1. Data Science
5.1.2. Advanced Tools for Data Scientists

5.2. Data, Information and Knowledge

5.2.1. Data, Information and Knowledge 
5.2.2. Types of Data
5.2.3. Data Sources

5.3. From Data to Information 

5.3.1. Data Analysis
5.3.2. Types of Analysis
5.3.3. Extraction of Information from a Dataset 

5.4. Extraction of Information Through Visualization

5.4.1. Visualization as an Analysis Tool
5.4.2. Visualization Methods 
5.4.3. Visualization of a Data Set

5.5. Data Quality

5.5.1    Quality Data
5.5.2. Data Cleaning 
5.5.3. Basic Data Pre-Processing

5.6. Dataset

5.6.1. Dataset Enrichment
5.6.2. The Curse of Dimensionality
5.6.3. Modification of Our Data Set

5.7. Unbalance 

5.7.1. Classes of Unbalance
5.7.2. Unbalance Mitigation Techniques
5.7.3. Balancing a Dataset

5.8. Unsupervised Models 

5.8.1. Unsupervised Model
5.8.2. Methods
5.8.3. Classification with Unsupervised Models

5.9. Supervised Models

5.9.1. Supervised Model
5.9.2. Methods
5.9.3. Classification with Supervised Models

5.10. Tools and Good Practices

5.10.1. Good Practices for Data Scientists
5.10.2. The Best Model 
5.10.3. Useful Tools

Module 6. Data Mining: Selection, Pre-Processing and Transformation

6.1. Statistical Inference

6.1.1. Descriptive Statistics vs. Statistical Inference
6.1.2. Parametric Procedures
6.1.3. Non-Parametric Procedures

6.2. Exploratory Analysis

6.2.1. Descriptive Analysis 
6.2.2. Visualization
6.2.3. Data Preparation

6.3. Data Preparation

6.3.1. Integration and Data Cleaning 
6.3.2. Normalization of Data
6.3.3. Transforming Attributes 

6.4. Missing Values

6.4.1. Treatment of Missing Values
6.4.2. Maximum Likelihood Imputation Methods
6.4.3. Missing Value Imputation Using Machine Learning

6.5. Noise in the Data 

6.5.1. Noise Classes and Attributes
6.5.2. Noise Filtering 
6.5.3. The Effect of Noise

6.6. The Curse of Dimensionality

6.6.1. Oversampling
6.6.2. Undersampling
6.6.3. Multidimensional Data Reduction

6.7. From Continuous to Discrete Attributes

6.7.1. Continuous Data vs. Discreet Data
6.7.2. Discretization Process

6.8. The Data 

6.8.1. Data Selection 
6.8.2. Prospects and Selection Criteria
6.8.3. Selection Methods 

6.9. Instance Selection

6.9.1. Methods for Instance Selection
6.9.2. Prototype Selection
6.9.3. Advanced Methods for Instance Selection

6.10. Data Pre-Processing in Big Data Environments

6.10.1. Big Data
6.10.2. Classical Versus Massive Pre-processing
6.10.3. Smart Data

Module 7. Predictability and Analysis of Stochastic Phenomena

7.1. Time Series

7.1.1. Time Series
7.1.2. Utility and Applicability
7.1.3. Related Case Studies

7.2. Time Series

7.2.1. Seasonal Trend of ST
7.2.2. Typical Variations
7.2.3. Waste Analysis

7.3. Types

7.3.1. Stationary
7.3.2. Non-Stationary
7.3.3. Transformations and Settings

7.4. Time Series Schemes 

7.4.1. Additive Scheme (Model)
7.4.2. Multiplicative Scheme (Model)
7.4.3. Procedures to Determine the Type of Model

7.5. Basic Forecasting Methods

7.5.1. Media
7.5.2. Naïve
7.5.3. Seasonal Naïve
7.5.4. Method Comparison

7.6. Waste Analysis 

7.6.1. Autocorrelation
7.6.2. ACF of Waste
7.6.3. Correlation Test

7.7. Regression in the Context of Time Series 

7.7.1. ANOVA
7.7.2. Fundamentals
7.7.3. Practical Applications 

7.8. Predictive Methods of Time Series

7.8.1. ARIMA
7.8.2. Exponential Smoothing 

7.9. Manipulation and Analysis of Time Series with R

7.9.1. Data Preparation
7.9.2. Identification of Patterns
7.9.3. Model Analysis
7.9.4. Prediction

7.10. Combined Graphical Analysis with R 

7.10.1. Normal Situations
7.10.2. Practical Application for the Resolution of Simple Problems 
7.10.3. Practical Application for the Resolution of Advanced Problems 

Module 8. Design and Development of Intelligent Systems        

8.1. Data Pre-Processing

8.1.1. Data Pre-Processing
8.1.2. Data Transformation 
8.1.3. Data Mining

8.2. Machine Learning

8.2.1. Supervised and Unsupervised Learning
8.2.2. Reinforcement Learning
8.2.3. Other Learning Paradigms

8.3. Classification Algorithms

8.3.1. Inductive Machine Learning
8.3.2. SVM and KNN
8.3.3. Metrics and Scores for Ranking

8.4. Regression Algorithms 

8.4.1. Lineal Regression, Logistical Regressiona and Non-Lineal Models 
8.4.2. Time Series 
8.4.3. Metrics and Scores for Regression 

8.5. Clustering Algorithms 

8.5.1. Hierarchical Clustering Techniques 
8.5.2. Partitional Clustering Techniques 
8.5.3. Metrics and Scores for Clustering 

8.6. Association Rules Techniques 

8.6.1. Methods for Rule Extraction 
8.6.2. Metrics and Scores for Association Rule Algorithms 

8.7. Advanced Classification Techniques. Multiclassifiers 

8.7.1. Bagging Algorithms 
8.7.2. Random “Forests Sorter” 
8.7.3. “Boosting” for Decision Trees 

8.8. Probabilistic Graphical Models 

8.8.1. Probabilistic Models 
8.8.2. Bayesian Networks. Properties, Representation and Parameterization
8.8.3. Other Probabilistic Graphical Models 

8.9. Neural Networks 

8.9.1. Machine Learning with Artificial Neural Networks 
8.9.2. Feed-Forward Networks 

8.10. Deep Learning 

8.10.1. Deep Feed Forward Networks 
8.10.2. Convolutional Neural Networks and Sequence Models 
8.10.3. Tools for Implementing Deep Neural Networks

Module 9. Architecture and Systems for Intensive Use of Data

9.1. Non-Functional Requirements: Pillars of Big Data Applications 

9.1.1. Reliability 
9.1.2. Adaptation 
9.1.3. Maintainability 

9.2. Data Models 

9.2.1. Relational Model 
9.2.2. Document Model 
9.2.3. Graph Type Data Model 

9.3. Databases: Storage Management and Data Recovery 

9.3.1. Hash Indexes     
9.3.2. Structured Log Storage 
9.3.3. B-Trees 

9.4. Data Coding Formats 

9.4.1. Language-Specific Formats 
9.4.2. Standardized Formats 
9.4.3. Binary Coding Formats 
9.4.4. Data Stream Between Processes 

9.5. Replication 

9.5.1. Objectives of Replication 
9.5.2. Replication Models 
9.5.3. Problems with Replication 

9.6. Distributed Transactions 

9.6.1. Transaction
9.6.2. Protocols for Distributed Transactions 
9.6.3. Serializable Transactions 

9.7. Partitions 

9.7.1. Forms of Partitioning 
9.7.2. Secondary Index Interaction and Partitioning 
9.7.3. Partition Rebalancing 

9.8. Processing of Offline Data 

9.8.1. Batch Processing 
9.8.2. Distributed File Systems 
9.8.3. MapReduce 

9.9. Data Processing in Real Time 

9.9.1. Types of Message Broker 
9.9.2. Representation of Databases as Data Streams 
9.9.3. Data Stream Processing 

9.10. Practical Applications in Business 

9.10.1. Consistency in Readings 
9.10.2. Holistic Focus of Data 
9.10.3. Scaling of a Distributed Service

Module 10. Practical Application of Data Science in Business Sectors 

10.1. Health Sector 

10.1.1. Implications of AI and Data Analysis in the Health Sector 
10.1.2. Opportunities and Challenges

10.2. Risks and Trends in the Health Sector 

10.2.1. Use in the Health Sector 
10.2.2. Potential Risks Related to the Use of AI

10.3. Financial Services

10.3.1. Implications of AI and Data Analysis in the Financial Services Sector
10.3.2. Use in the Financial Services 
10.3.3. Potential Risks Related to the Use of AI 

10.4. Retail 

10.4.1. Implications of AI and Data Analysis in the Retail Sector 
10.4.2. Use in Retail 
10.4.3. Potential Risks Related to the Use of AI 

10.5. Industry 4.0

10.5.1. Implications of AI and Data Analysis in Industry 4.0 
10.5.2. Use in the 4.0 Industry 

10.6. Risks and Trends in Industry 4.0 

10.6.1. Potential Risks Related to the Use of AI 

10.7. Public Administration

10.7.1. Implications of AI and Data Analysis in Public Administration 
10.7.2. Use in Public Administration 
10.7.3. Potential Risks Related to the Use of AI 

10.8. Education

10.8.1. Implications of AI and Data Analysis in Education 
10.8.2. Potential Risks Related to the Use of AI 

10.9. Forestry and Agriculture

10.9.1. Implications of AI and Data Analysis in Forestry and Agriculture 
10.9.2. Use in Forestry and Agriculture 
10.9.3. Potential Risks Related to the Use of AI 

10.10. Human Resources      

10.10.1. Implications of AI and Data Analysis in Human Resources 
10.10.2. Practical Applications in the Business World
10.10.3. Potential Risks Related to the Use of AI

A program designed for computer engineers who want to change or boost their professional career”