TECH offers you the best knowledge in Big Data to become your passport to a career full of exciting opportunities and challenges” 

The Big Data discipline has emerged as a strategic solution, enabling organizations to transform complex data into valuable opportunities. This discipline has been characterized by its volume, variety and velocity, changing the way companies operate, make decisions and compete in the global marketplace. However, making the most of this resource requires experts who understand how to collect and analyze large amounts of information.  

Aware of this need, TECH's Advanced master’s degree in Big Data Management presents itself as a gateway to this fascinating and dynamic field. Designed to specialize the professionals who will lead the digital revolution, this program combines advanced technical knowledge with comprehensive training, covering both the study of cutting-edge platforms, algorithms and tools and solid strategic preparation. Today, virtually every interaction in the digital environment generates data, whether through online shopping, the use of social networks or sensors in devices connected to the Internet of Things. Therefore, the knowledge and management of Big Data have become key aspects for all business sectors. 

This Advanced master’s degree includes in its syllabus the study of the most advanced platforms, algorithms and tools in the sector, all taught through the innovative Relearning learning method, adapted to the needs and pace of study of each student. Best of all, the program is completely online and accessible from any device, which offers the flexibility to adjust schedules and combine work responsibilities, without leaving aside an active family life, while advancing in professional specialization. 

With TECH, boost your professional profile with specialized knowledge that will make you stand out in any industry” 

This Advanced master’s degree in Big Data Management contains the most complete and up-to-date educational program on the market. The most important features include: 

• Practical cases presented by experts in IT   
• 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 in Big Data Management 
• 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 

Master the future of data analysis by learning 100% online with the Relearning method, the most innovative and effective on the market” 

Its teaching staff includes professionals from the field of journalism, who bring to this program the experience of their work, as well as renowned specialists from reference 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 an immersive learning experience designed to prepare for real-life situations. 

This program is designed around Problem-Based Learning, whereby the student must try to solve the different professional practice situations that arise throughout the program. For this purpose, the professional will be assisted by an innovative interactive video system created by renowned and experienced experts.   

With the latest teaching methodology, build the future you want in a field where the demand for talent continues to grow"

Expand your ability to innovate in the world with the best faculty that will accompany you in this Advanced master’s degree in Big Data"

The Advanced master’s degree in Big Data Management offers comprehensive knowledge ranging from the fundamentals of Big Data to the most advanced strategies for its application in the business environment. Throughout the program, graduates will develop key skills in areas of high labor demand, giving them the ability to analyze and transform data into valuable assets. In addition, the program is designed for professionals to adapt to the constant technological evolutions, preparing them to lead data management in various sectors.  

With TECH's methodology, learn how to decipher the secrets behind the data and lead the digital revolution” 

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 
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 vs. Discrete 
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. “Classic” vs. Mass 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. 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. 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. 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 Index 
9.3.2. Structured Log Storage 
9.3.3. B-Tree 

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 of 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 the 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 

Module 11. Visual Analytics in the Social and Technological Context 

11.1. Technological Waves in Different Societies. Towards a ‘Data Society’ 
11.2. Globalization. Geopolitical and Social World Context 
11.3. VUCA Environment. Always Living in the Past 
11.4. Knowing New Technologies: 5G and IoT 
11.5. Knowing New Technologies: Cloud and Edge Computing 
11.6. Critical Thinking in Visual Analytics 
11.7. Knowmads. Nomads Among Data 
11.8. Learning to Be an Entrepreneur in Visual Analytics 
11.9. Anticipation Theories Applied to Visual Analytics 
11.10. The New Business Environment. Digital Transformation 

Module 12. Data Analysis and Interpretation 

12.1. Introduction to Statistics 
12.2. Measures Applicable to the Processing of Information 
12.3. Statistical Correlation 
12.4. Theory of Conditional Probability 
12.5. Random Variable and Probability Distribution 
12.6. Bayesian Inference 
12.7. Sample Theory 
12.8. Confidence Intervals 
12.9. Hypothesis Testing 
12.10. Regression Analysis 

Module 13. Data and AI Analysis Techniques 

13.1. Predictive Analytics 
13.2. Evaluation Techniques and Model Selection 
13.3. Lineal Optimization Techniques 
13.4. Montecarlo Simulations 
13.5. Scenario Analysis 
13.6. Machine Learning Techniques 
13.7. Web Analytics 
13.8. Text Mining Techniques 
13.9. Methods of Natural Language Processing (NLP) 
13.10. Social Network Analytics 

Module 14. Data Analysis Tools 

14.1. Data Science R Environment 
14.2. Data Science Python Environment 
14.3. Static and Statistical Graphs 
14.4. Data Processing in Different Formats and Different Sources 
14.5. Data Cleaning and Preparation 
14.6. Exploratory Studies 
14.7. Decision Trees 
14.8. Classification and Association Rules 
14.9. Neural Networks 
14.10. Deep Learning 

Module 15. Database Management and Data Parallelization Systems 

15.1. Conventional Databases 
15.2. Non-Conventional Databases 
15.3. Cloud Computing: Data Distribution Management 
15.4. Tools for the Ingestion of Large Volumes of Data 
15.5. Types of Parallels 
15.6. Data Processing in Streaming and Real Time 
15.7. Parallel Processing: Hadoop 
15.8. Parallel Processing: Spark 
15.9. Apache Kafka 

15.9.1. Introduction to Apache Kafka 
15.9.2. Architecture 
15.9.3. Data Structure 
15.9.4. API Kafka 
15.9.5. Case Uses 

15.10. Cloudera Impala 

Module 16. Data-Driven Soft Skills in Strategic Management in Visual Analytics 

16.1. Drive Profile for Data-Driven 
16.2. Advanced Management Skills in Data-Driven Organizations 
16.3. Using Data to Improve Strategic Communication Performance 
16.4. Emotional Intelligence Applied to Management in Visual Analytics 
16.5. Effective Presentations 
16.6. Improving Performance Through Motivational Management 
16.7. Leadership in Data-Driven Organizations 
16.8. Digital Talent in Data-Driven Organizations 
16.9. Data-Driven Agile Organization I 
16.10. Data-Driven Agile Organization II 

Module 17. Strategic Management of Visual Analytics and Big Data Projects 

17.1. Intriduction to Strategic Project Management 
17.2. Best Practices in the Description of Big Data Processes 
17.3. Kimball Methodology 
17.4. SQuID Methodology 

17.4.1. Introduction to SQuID Methodology to Approach Big Data Projects 
17.4.2. Phase I. Sources 
17.4.3. Phase II. Data Quality 
17.4.4. Phase III. Impossible Questions 
17.4.5. Phase IV. Discovering 
17.4.6. Best Practices in the Application of SQuID in Big Data Projects 

17.5. Legal Aspects in the World of Data 
17.6. Big Data Privacy 
17.7. Cyber Security in Big Data 
17.8. Identification and De-Identification with Large Volumes of Data 
17.9. Data Ethics I 
17.10. Data Ethics II 

Module 18. Client Analysis. Applying Data Intelligence to Marketing 

18.1. Concepts of Marketing. Strategic Marketing 
18.2. Relationship Marketing 
18.3. CRM as an Organizational Hub for Customer Analysis 
18.4. Web Technologies 
18.5. Web Data Sources 
18.6. Acquisition of Web Data 
18.7. Tools for the Extraction of Data from the Web 
18.8. Semantic Web 
18.9. OSINT: Open Source Intelligence 
18.10. Master Lead or How to Improve Sales Conversion Using Big Data 

Module 19. Interactive Visualization of Data 

19.1. Introduction to the Art of Making Data Visible 
19.2. How to do Storytelling with Data 
19.3. Data Representation 
19.4. Scalability of Visual Representations 
19.5. Visual Analytics vs. Information Visualization. Understanding That its Not the Same 
19.6. Visual Analysis Process (Keim) 
19.7. Strategic, Operative and Managerial Reports 
19.8. Types of Graphs and their Application 
19.9. Interpretation of Reports and Graphs. Playing the Role of the Receiver 
19.10. Evaluation of Visual Analytics Systems 

Module 20. Visualization Tools 

20.1. Introduction to Data Visualization Tools 
20.2. Many Eyes 
20.3. Google Charts 
20.4. jQuery 
20.5. Data-Driven Documents I 
20.6. Data-Driven Documents II 
20.7. Matlab 
20.8. Tableau 
20.9. SAS Visual Analytics 
20.10. Microsoft Power BI 

A comprehensive curriculum that will lead you to master the area of Big Data and become a successful business strategy architect”