Analyze the different software tools for graphing and exploratory data analysis and improve the management of your work team"

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 program designed for this Postgraduate diploma seeks to become an agent of positive change for computer engineers seeking excellence in their profession. With a 100% online modality, the information and contents will always be available to students, offering them the opportunity to take it anywhere in the world.  

Analyze the different software tools for graphing and exploratory data analysis with a 100% online program”

Syllabus

The Postgraduate diploma in Exploratory Data Analysis at TECH Global University will become an immersive experience in the management of a technology department focusing on improving the other structures of the organization.

To achieve the above, the program will enhance the theoretical and managerial skills of computer engineers interested in this field, helping them to understand data management, focusing at all times on its typology and life cycle and practical approach through the available resources.

Throughout the 450 hours of the educational program, the student will develop a critical attitude towards the strategies applied, being responsible for discerning in each case the most appropriate solution and explaining in a reasoned manner the results obtained in the different metrics. Therefore, it is an immersion of real business situations in the academic classroom.

As the syllabus progresses, special emphasis will be placed on determining the main characteristics of a Dataset, its structure, components and the implications of its distribution in the modeling. To this end, the results will be assessed analytically, understanding the impact of the chosen strategy on the different metrics. In this way, it becomes a syllabus that meets the needs of students and prepares them to face any professional challenge at the IT and management level.

This Postgraduate diploma takes place over 6 months and is divided into 3 modules:

Module 1. Data and Information Management and Manipulation in Data Science
Module 2. Graphical Representation of Data Analysis
Module 3. Data Science Tools

Where, When and How is it Taught?

TECH offers the possibility of developing this Postgraduate diploma in Exploratory Data Analysis completely online. Over the course of 6 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 and Information Management and Manipulation for Data Science

1.1. Statistics. Variables, Indices and Ratios

1.1.1. Statistics  
1.1.2. Statistical Dimensions  
1.1.3. Variables, Indices and Ratios

1.2. Type of Data 

1.2.1. Qualitative  
1.2.2. Quantitative  
1.2.3. Characterization and Categories

1.3. Data Knowledge from the Measurements 

1.3.1. Centralization Measurements  
1.3.2. Measures of Dispersion  
1.3.3. Correlation 

1.4. Data Knowledge from the Graphs 

1.4.1. Visualization According to Type of Data  
1.4.2. Interpretation of Graphic Information  
1.4.3. Customization of graphics with R

1.5. Probability 

1.5.1. Probability  
1.5.2. Function of Probability  
1.5.3. Distributions 

1.6. Data Collection 

1.6.1. Methodology of Data Collection  
1.6.2. Data Collection Tools  
1.6.3. Data Collection Channels 

1.7. Data Cleaning 

1.7.1. Phases of Data Cleansing  
1.7.2. Data Quality  
1.7.3. Data Manipulation (with R) 

1.8. Data Analysis, Interpretation and Evaluation of Results

1.8.1. Statistical Measures  
1.8.2. Relationship Indices  
1.8.3. Data Mining 

1.9. Data Warehouse 

1.9.1. Components   
1.9.2. Design 

1.10. Data Availability

1.10.1. Access  
1.10.2. Uses  
1.10.3. Security/safety 

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. Application of 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. Data Science Tools

3.1. Data Science

3.1.1. Data Science 
3.1.2. Advanced Tools for Data Scientists 

3.2. Data, Information and Knowledge

3.2.1. Data, Information and Knowledge 
3.2.2. Types of Data 
3.2.3. Data Sources

3.3. From Data to Information

3.3.1. Data Analysis 
3.3.2. Types of Analysis 
3.3.3. Extraction of Information from a Dataset

3.4. Extraction of Information Through Visualization

3.4.1. Visualization as an Analysis Tool 
3.4.2. Visualization Methods 
3.4.3. Visualization of a Data Set

3.5. Data Quality

3.5.1. Quality Data 
3.5.2. Data Cleaning 
3.5.3. Basic Data Pre-Processing

3.6. Dataset

3.6.1. Dataset Enrichment 
3.6.2. The Curse of Dimensionality 
3.6.3. Modification of Our Data Set

3.7. Unbalance

3.7.1. Classes of Unbalance 
3.7.2. Unbalance Mitigation Techniques 
3.7.3. Balancing a Dataset

3.8. Unsupervised Models

3.8.1. Unsupervised Model 
3.8.2. Methods 
3.8.3. Classification with Unsupervised Models

3.9. Supervised Models

3.9.1. Supervised Model 
3.9.2. Methods 
3.9.3. Classification with Supervised Models

3.10. Tools and Good Practices

3.10.1. Good Practices for Data Scientists 
3.10.2. The Best Model  
3.10.3. Useful Tools

You will learn through case studies how the dynamics of a team leader work, turning this program into your opportunity for professional improvement"