With this program, you will be able to design and implement an effective data management strategy. This will enable you to be a successful Data Science Officer (DSO)”

Why Study at TECH?

TECH is the world's largest 100% online business school. It is an elite business school, with a model based on the highest academic standards. A world-class centre for intensive managerial skills training.   

TECH is a university at the forefront of technology, and puts all its resources at the student's disposal to help them achieve entrepreneurial success"

At TECH Global University

Innovation

The university offers an online learning model that combines the latest educational technology with the most rigorous teaching methods. A unique method with the highest international recognition that will provide students with the keys to develop in a rapidly-evolving world, where innovation must be every entrepreneur’s focus.

"Microsoft Europe Success Story", for integrating the innovative, interactive multi-video system.  

The Highest Standards

Admissions criteria at TECH are not economic. Students don't need to make a large investment to study at this university. However, in order to obtain a qualification from TECH, the student's intelligence and ability will be tested to their limits. The institution's academic standards are exceptionally high...  

95% of TECH students successfully complete their studies.

Networking

Professionals from countries all over the world attend TECH, allowing students to establish a large network of contacts that may prove useful to them in the future.  

100,000+ executives trained each year, 200+ different nationalities.

Empowerment

Students will grow hand in hand with the best companies and highly regarded and influential professionals. TECH has developed strategic partnerships and a valuable network of contacts with major economic players in 7 continents.  

500+ collaborative agreements with leading companies.

Talent

This program is a unique initiative to allow students to showcase their talent in the business world. An opportunity that will allow them to voice their concerns and share their business vision. 

After completing this program, TECH helps students show the world their talent. 

Multicultural Context

While studying at TECH, students will enjoy a unique experience. Study in a multicultural context. In a program with a global vision, through which students can learn about the operating methods in different parts of the world, and gather the latest information that best adapts to their business idea. 

TECH students represent more than 200 different nationalities.   

Learn with the best

In the classroom, TECH teaching staff discuss how they have achieved success in their companies, working in a real, lively, and dynamic context. Teachers who are fully committed to offering a quality specialization that will allow students to advance in their career and stand out in the business world. 

Teachers representing 20 different nationalities. 

TECH strives for excellence and, to this end, boasts a series of characteristics that make this university unique:   

Analysis

TECH explores the student’s critical side, their ability to question things, their problem-solving skills, as well as their interpersonal skills.  

Academic Excellence

TECH offers students the best online learning methodology. The university combines the Relearning method (a postgraduate learning methodology with the highest international rating) with the Case Study. A complex balance between tradition and state-of-the-art, within the context of the most demanding academic itinerary.  

TECH is the world’s largest online university. It currently boasts a portfolio of more than 10,000 university postgraduate programs. And in today's new economy, volume + technology = a ground-breaking price. This way, TECH ensures that studying is not as expensive for students as it would be at another university.  

At TECH, you will have access to the most rigorous and up-to-date case studies in the academic community”

The syllabus of this program covers the knowledge required to work as a Data Science Officer: from data analytics in the company, to architectures and systems for intensive use of data, among other issues. All this, from a practical perspective, with content presented in multimedia format and 100% online. This facilitates the consolidation of knowledge and the compatibility of study with other day to day tasks. 

TECH offers you an academic model based on high quality content, presented in multimedia format and 100% online. A system in line with the needs of today's manager and one that is breaking the foundations of online university education" 

Syllabus

As companies grow, so does their need to manage data efficiently. To this end, they must have a Data Science Officer on their staffs, a multi-skilled profile not only capable of managing the technical aspects of data management, but also the economic and resource management issues in the organization. Specifically, the CTO should be responsible for establishing policies and procedures for data management, working cross-functionally with the rest of the company's departments to obtain, prepare, organize, protect and analyze data so that it can be used to improve all areas of the business. 

For this reason, and thinking about the needs of the current labor market, TECH launches this program where the different algorithms, platforms and the most current tools for the exploration, visualization, manipulation, processing and analysis of data, complemented, in addition, with the necessary business vision for its value as a key element for decision making. 

The entire content of the program is designed to enhance the specific technical skills of professionals interested in the problems involved in data analytics and its subsequent transformation into knowledge. 

In addition, and throughout the 1,500 hours of the program, the student will analyze different practical cases through individual practice and teamwork. Therefore, it is a real immersion of real business situations integrated into the online academic process.   
This Executive Master's Degree is a 12-month program and is divided into 10 modules:

Module 1. Data Analysis in a Business Organization 
Module 2. Data Management, Data Manipulation and Information Management for Data Science
Module 3. Devices and IoT Platforms as a Base for Data Science
Module 4. Graphical Representation of Data Analysis
Module 5. Data Science Tools
Module 6. Data Mining: Selection, Pre-Processing and Transformation
Module 7. Predictability and Analysis of Stochastic Phenomena
Module 8. Design and Development of Intelligent Systems
Module 9. Architecture and Systems for Intensive Use of Data 
Module 10. Practical Application of Data Science in Business Sectors

Where, When and How is it Taught?

TECH offers the possibility of developing this Executive Master’s Degree in Data Science Management and the Data Science Officer completely online. Over the course of 12 months, you will be able to access all the contents of this program at any time, allowing you to self-manage your study time.

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. Scorecards and KPIs by Departments 
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. Needs of the Sales Department 
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 Management, Data Manipulation and Information Management for 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/safety

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 Robotics 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. Trend Seasonality of ST 
7.2.2. Typical Variations 
7.2.3. Waste Analysis

7.3. Typology

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 Forecast Methods

7.5.1. Media 
7.5.2. Naive 
7.5.3. Seasonal Naive 
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 Regression 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. Feedforward Networks

8.10. Deep Learning

8.10.1. Deep Feedforward 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. Trees B

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. Offline Data Processing

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 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 Analytics for Public Administration 
10.7.2. Use in Public Administration 
10.7.3. Potential Risks Related to the Use of AI

10.8. Educational 

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

It has a unique, key and decisive program to drive the professional development you need to become a leader”