CV

Full and Up-to-date Hybrid (Chronological + Functional) Curriculum Vitae: 


1001-2018

Miguel Angel Mudoy

[Data Scientist]

“I am what I repeatedly do. Excellence, then is not an act but a habit.”

PROFILE.


I was born in 1972 in Palma de Mallorca, Balearic Islands, Spain; and I did have the privilege of living in London, England, U.K. for twelve years (2007-1995).

Although the Art of uncovering the insights and trends in data has been around since ancient times, Data Science is now a new field of study across the world, and I am already a multi-talented and a highly-promising Data Scientist.

In fact, I am in the process of creating and designing a New Open Source Classification Supervised Machine Learning Algorithm to make the process of selecting people (job seekers) for different worldwide programmes and initiatives to promote and encourage jobs creation and hence decreasing the overall unemployment rate, within government public administrations (public sector); more efficient, transparent and, above all, fairer.

According to field experts and professionals on the following subject matter:

  • I have an above average fluid intelligence.
  • And I have an unusual high level of abstract and creative thinking, which is complimented by displaying a stunning grasp of logic and reasoning.

I am also an accomplished and fluent communicator with strong investigation, problem-solving, attention to detail and decision-making skills.

Overall, I have a passion for mathematics, statistics and modeling as well as high-quality code, algorithms, data analysis and digital analytics, creative problem-solving, empowerment, agility, communication, and teamwork.

KEY COMPETENCIES.


Curiosity. Inquisitiveness. Communication. Presentation. Facilitation. Creativity and innovation. Interviewing. Selling. Influence / persuasion. Teamwork. Management and leadership. Handling difficult people and situations. Networking. Negotiation. Organising and multitasking. Mentoring / coaching. Learning.

COMMUNICATION LANGUAGES.


Multi-lingual: Spanish and English. Excellent written and verbal communication skills. British Residency: (2007-1995).

DATA SCIENCE CURRENT SKILLS.


Fundamentals of Math. Algebra. Calculus. Discrete Math – Set Theory, Relations, and Functions. Data Science Math Skills. Probability and Statistics. Algorithmic Design and Techniques. Structured, Semi-structured, and Unstructured Data. Data Analysis. Digital Analytics and Regression. Predictive Modeling. Data Mining. Data Visualization. Data Privacy. Python Programming. R Programming. Microsoft Excel. Google Sheets. Structured Query Language (SQL). Databases. Tableau. Microsoft PowerPoint. Google Slides. IBM Cognitive Class Labs. Jupyter Notebooks. Apache Zeppelin Notebooks. RStudio IDE. Seahorse. OpenRefine. IBM SPSS Modeler. IBM Bluemix.

MACHINE LEARNING ENGINEERING CURRENT SKILLS.


BigML is a consumable, programmable, and scalable Machine Learning platform that makes it easy to solve and automate Classification, Regression, Time Series Forecasting, Cluster Analysis, Anomaly Detection, Association Discovery and Topic Modeling tasks.

Spark MLlib is a machine learning library that provides various machine learning algorithms such as classification, regression, clustering, and collaborative filtering. It also provides tools such as featurization, pipelines, persistence, and utilities for handling linear algebra operations, statistics and data handling.

VOLUNTEERING EXPERIENCE.


Year 2018 – Present day: Red Cross Volunteer. Majorca. Balearic Islands. Spain. 

Volunteering Background:

Ever since I was a kid, I’ve always wanted to make a difference. I always wanted to contribute to society. And I always wanted to help to make the world a better place for present and future generations.

(My) Volunteering Vision:

To officially create, develop, and lead the first ever department of the Red Cross dedicated exclusively to Data Science to help to make the world a better place for present and future generations.

PROFESSIONAL EXPERIENCE. 


Year 2018 – Present day: (Coming Soon)

Year 2018: Official Candidate for The Data Science Awards Spain in the Category of The Best Data Scientist.

Year 2018: Creator of the first algorithmic design of The Fair Play Algorithm [a New Open Source Classification Supervised Machine Learning Algorithm to make the process of selecting people (job seekers) for different worldwide programmes and initiatives to promote and encourage jobs creation and hence decreasing the overall unemployment rate, within government public administrations (public sector); more efficient, transparent and, above all, fairer]

Year 2017: Career Change from an Award-Winning High Technology Entrepreneur to a Multi-Talented and a Highly-Promising Data Scientist.

ACADEMIC BACKGROUND.


[DATA SCIENCE]

Data Science for Business:

Year 2018: 5 – Digital Analytics and Regression Course. Cognitive Class A.I. Cognitive Class is an IBM Community Initiative which is formerly known as Big Data University. (Course Successfully Completed)

A Case Study Approach to Analytics:

  • Understand the business context.
  • Formulate the business objective.
  • State the hypothesis.
  • Assess available data.
  • Assign data for use.

Data Scientist Workbench:

  • Data Scientist Workbench.
  • What is R?
  • Loading data into R with Data Scientist Workbench.
  • Upload a CSV data file into Data Scientist Workbench and RStudio.

Google Trends Data in R:

  • Access Google Trends data in R.

Simple Linear Regression in R:

  • Regression and Google Trends Data in R.
  • Box Plots and Histograms in R.
  • Scatterplots and Lines of best fit in R.
  • Simple Linear Regression in R.

Presenting Data Analytics in Business:

  • Using data to answer a business question.
  • Summarizing the data analytics process.
  • Presenting data insights.
(MAX) Data-science-for-business-level-1
IBM Data Science for Business – Level 1 Badge.

Year 2018: 4 – Data Privacy Fundamentals Course. Cognitive Class A.I. Cognitive Class is an IBM Community Initiative which is formerly known as Big Data University. (Course Successfully Completed)

An Overview of Privacy Laws:

  • (Case Study) Student Loans Data Breach (Canada) – A breach involving the personal information of about more than half a million clients of Human Resources and Skills Development Canada (HRSDC) and 250 departmental employees.

An overview of the Personal Information Protection and Electronic Documents Act (PIPEDA):

  • (Case Study) Target Corp. (USA) – A data breach involving information on 40 million payment cards (i.e., credit, debit, and ATM cards) and personally identifiable information (PII) on 70 million customers.

Dr. Ann Cavoukian’s 7 Foundational Principles of ‘Privacy by Design’:

  • (Case Study) Think W3 (UK) – A data breach involving 1.2 million credit and debit card details.
  • (Case Study) Doritex Corp. (USA) – A data breach exposed the social security numbers of over 500 job applicants.

Data Breaches and Passwords:

  • (Case Study) Home Depot (USA) – A data breach estimated to have put payment card information at risk for approximately 56 million unique payment cards.

Analysing Hacked Passwords in R:

  • Predicting Passwords in R.
  • 10 Privacy Tips for Companies.

Data Science Foundations:

(MAX) IBM Data Science Foundations Level 2 Badge 2
IBM Data Science Foundations – Level 2 Badge.

Year 2018: 3 – Data Science Methodology Course. Cognitive Class A.I. Cognitive Class is an IBM Community Initiative which is formerly known as Big Data University. (Course Successfully Completed)

From Problem to Approach:

  • Why we are interested in data science.
  • What a methodology is, and why data scientists need a methodology.
  • The data science methodology and its flowchart.
  • How to apply business understanding and the analytic approach to any data science problem.

From Requirements to Collection:

  • Data requirements and data understanding.
  • What occurs during data collection.
  • How to apply data requirements and data collection to any data science problem.

From Understanding to Preparation:

  • What it means to understand data.
  • What it means to prepare or clean data.
  • Ways in which data is prepared.
  • How to apply data understanding and data preparation to any data science problem.

From Modeling to Evaluation:

  • What the purpose of data modeling is.
  • Some characteristics of the modeling process.
  • What it means to evaluate a model.
  • Ways in which a model is evaluated.
  • How to apply modeling and model evaluation to any data science problem.

From Deployment to Feedback.

  • What happens when a model is deployed.
  • Why model feedback is important.  

Year 2018: 2 – Data Science Hands-On with Open Source Tools Course. Cognitive Class A.I. Cognitive Class is an IBM Community Initiative which is formerly known as Big Data University. (Course Successfully Completed)

Introducing Cognitive Class Labs (Data Scientist Workbench):

  • LAB: Getting Started with the Data Science Tools.
  • What is Cognitive Class Labs (Data Scientist Workbench)?
  • Data Scientist Workbench Account features.
  • Creating a Data Scientist Workbench account.
  • Managing data within My Data.

Introducing Jupyter Notebooks:

  • LAB: Getting Started with Jupyter Notebooks.
  • What are Jupyter notebooks?
  • Data and Notebooks in Jupyter.
  • Sharing your Jupyter Notebooks and data.
  • Apache Spark in Jupyter Notebooks.

Introducing Zeppelin Notebooks:

  • LAB: Getting Started with Apache Zeppelin Notebooks.
  • What are Zeppelin Notebooks?
  • Zeppelin for Scala.
  • Managing your Interpreters in Zeppelin.
  • Apache Spark in Zeppelin Notebooks.

Introducing RStudio IDE:

  • LAB: Getting Started with RStudio IDE.
  • What is RStudio IDE?
  • Uploading files, Installing Packages and loading libraries in RStudio IDE.
  • RStudio Environment and History.
  • Apache Spark in RStudio IDE.

Introducing Seahorse:

  • LAB: Getting Started with Seahorse.
  • What is Seahorse?
  • A Glimpse of Seahorse’s Features.
  • Creating and uploading Seahorse Workflows on DSWB.
  • Exporting and Cloning the Seahorse Examples on DSWB.

Introducing OpenRefine:

  • LAB: Getting Started with OpenRefine.
  • What is OpenRefine?
  • What are the features of OpenRefine?
  • Using OpenRefine.
  • Preparing data with OpenRefine.
data-science-foundations-level-1
IBM Data Science Foundations – Level 1 Badge.

Year 2018: 1 – Introduction to Data Science Course. Cognitive Class A.I. Cognitive Class is an IBM Community Initiative which is formerly known as Big Data University. (Course Successfully Completed)

Defining Data Science:

  • What is data science?
  • Data science is a new field.
  • There are many paths to data science.
  • Any advice for a new data scientist?
  • What is the cloud?

What do data science people do?

  • A day in the life of a data science person.
  • R versus Python?
  • Data science tools and technology.

Data Science in Business:

  • How should companies get started in data science?
  • Tips for recruiting data science people.
  • Where does data science fit in an org structure.
  • How should companies approach data privacy.
  • What is the future of data science?

Use Cases for Data Science:

  • Applications for data science.

[MACHINE LEARNING ENGINEERING]

Year 2018: 1 – Introduction to Machine Learning Course. Miríadax_ / Universitas Telefónica (Fundación Telefónica). (Course Successfully Completed)

  • Introduction to Machine Learning.
  • Data and Machine Learning.
  • Machine Learning Solutions: Supervised Learning.
  • Machine Learning Solutions: Unsupervised Learning.

[DATA FOUNDATIONS]

Year 2018: 1 – Data Foundations Degree (Nanodegree Program). Udacity School of Data Science in collaboration with Tableau and MODE. (Currently Studying)

Mastering data fundamentals applicable to any industry by learning to manipulate, analyze, and visualize data with Excel, SQL, and Tableau.

  • Introduction to Data: Learning how to calculate statistics and build visuals used in industry to best display and communicate data insights. Developing proficiency in Microsoft Excel and mastering the skills necessary to inform decision makers and make an impact using data.
  • SQL for Data Analysis: Learning to use Structured Query Language (SQL) to extracting and analysing data stored in databases.
  • Data Visualization: Learning to apply design and visualization principles to create impactful data visualizations, building data dashboards, and telling stories with data.

[SOFTWARE DEVELOPMENT and ENGINEERING]

R Programming:

Year 2018: 2 – Advanced R Programming Course. Coursera / Johns Hopkins University – Baltimore, Maryland, U.S.A. (Currently Studying)

  • Advanced R Programming.
  • Functions.
  • Functional Programming.
  • Debugging and Profiling.
  • Object-Oriented Programming.

Year 2018: 1 – The R Programming Environment Course. Coursera / Johns Hopkins University – Baltimore, Maryland, U.S.A. (Currently Studying)

  • Basic R Language.
  • Data Manipulation.
  • Text Processing, Regular Expression, & Physical Memory.
  • Large Datasets.

Python Programming:

Year 2018: 2 – Python Data Structures Course. Coursera / School of Information, University of Michigan – Michigan, U.S.A. (Course Successfully Completed)

  • Strings.
  • Files.
  • Lists.
  • Dictionaries.
  • Tuples.

Year 2017: 1 – Getting Started with Python Course. Coursera / School of Information, University of Michigan – Michigan, U.S.A. (Course Successfully Completed)

  • Why we Program?
  • Installing and Using Python.
  • Why we Program (Continued).
  • Variables and Expressions.
  • Conditional Code.
  • Functions.
  • Loops and Iteration.

[MATHEMATICS and STATISTICS]

Year 2018: 3 – Data Science Math Skills Course. Coursera / Duke University – North Carolina, U.S.A. (Course Successfully Completed)

  • Set theory, including Venn diagrams.
  • Properties of the real number line.
  • Interval notation and algebra with inequalities.
  • Uses for summation and Sigma notation.
  • Math on the Cartesian (x,y) plane, slope and distance formulas.
  • Graphing and describing functions and their inverses on the x-y plane.
  • The concept of instantaneous rate of change and tangent lines to a curve.
  • Exponents, logarithms, and the natural log function.
  • Probability theory, including Bayes’ theorem.

Year 2018: 2 – Discrete Math – Set Theory, Relations and Functions Course. Udemy / Engineering Education Hub. (Course Successfully Completed)

  • Set – Introduction.
  • Classification (Types) of Sets.
  • Subset, Proper Subset and Superset.
  • Power Set.
  • Universal Set.
  • Set Operations: Union, Intersection, Set Difference, Complement and Cross Product.
  • Venn Diagrams.
  • Relations.
  • Types of Relations.
  • Function – Introduction.
  • Types of Functions.
  • Even and Odd Functions.
  • Composition of Functions.
  • Inverse of a Function.

Year 2018: 1 – Mastering the Fundamentals of Math Course. Udemy / Krista King. (Course Successfully Completed)

  • Numbers.
  • Negative numbers.
  • Factors and multiples.
  • Decimals.
  • Fractions.
  • Mixed numbers.
  • Ratio and proportion.
  • Exponents.
  • Radicals.
  • Scientific notation.

[PSYCHOLOGY and NEUROSCIENCE]

Year 2017: 1 – Motivating Leadership through Emotional Intelligence Course. Coursera / Case Western Reserve University – Cleveland, Ohio, U.S.A. (Course Successfully Completed)

  • Resonant Leadership and the Neuroscience Behind It.
  • Renewal as an Antidote to Chronic Stress.
  • Emotional Intelligence and Its Link to Leadership.
  • Inspiring and Motivating Sustained Development, Growth and Learning.
  • Coaching with Compassion to Inspire Sustained Learning and Development & Peer Coaching.
  • Inspiring Change through Hope and Vision.
  • The Multilevel Nature of Sustained, Desired Change.
  • The Real Self and Learning Agenda.

PREVIOUS PROFESSIONAL EXPERIENCE (Transferable Skills).


Years 2017 – 2000: High Technology Entrepreneur – London, England, U.K. / Spain.

Founder and Chief Executive Officer: 

3 – You Hotel Star, S.L. Spanish hotel review Internet platform allowing guests to post videos of the lodgings they stay in real time. Major Achievements: Introducing the world´s first and only Internet company specialising on hotel guest video reviewing that harness the potential of TripAdvisor. Award winning technology start-up company. 1.000.000,00 Euros company valuation in 2017.

2 – The Ramming Skis Company Ltd. British high technology winter sports engineering business specialised in enhancing both, the dynamic performance and the technical capability of alpine racing skis. Major Achievements: Introducing the world´s most advanced alpine racing skis. Commercial collaboration with Expert Engineering  and Queen Mary, University of London. Media coverage.

1 – The U.W.O.A.A. (Universal Window Of Accelerating Action) Company Ltd. British high technology engineering business specialised in a ground-breaking environmentally friendly propulsion system that permits increased terminal velocities to make any object, capable of moving forward: To drive faster, to navigate faster, to hover faster or to fly faster; without using an additional engine nor increasing the power of an existing engine nor using any fuel resources and nor harming the environment.

Main Duties and Responsibilities: 

  • Cash flow.
  • Product vision.
  • Team building.
  • Investor management.
  • Brand management.
  • Corporate development.

Awards and Honours:

Year 2017: Award Nomination Cercle d’Economia de Mallorca al Impulso de Ideas Innovadoras para la Mejora de la Sociedad.

Year 2017: TED Prize Award Nomination.

Year 2016: The Founder Institute Madrid (Scholarship Winner).

Year 2015: Semifinalist in Spain Start Up South Summit Award.

Year 2015: Semifinalist in Top Seeds Lab Award.

Year 2014: Distinguished by the Prestigious C.A.E.B. for contributing to Business Innovation.

Year 2014: Award Nomination Premio Empresarial Lanza de Palma Activa.

Year 2014: Award Nomination Premio Emprendedores a la Mejor Idea Empresarial.

Year 2014: Award Nomination Premio Emprendedores XXI.

Mass Media Appearances:

Years 2016 – 2013.

  • Interviewed by Mallorca Startups.
  • Interviewed by Más Que Negocio.
  • Interviewed by GSBIT.
  • Interviewed by IB3 Radio. (2)
  • Interviewed by Diario Tecno Hotel. (2)
  • Interviewed by IB3 Radio. (1) 
  • Interviewed by Radio Bellver.
  • Interviewed by Diario Tecno Hotel. (1) 
  • Interviewed by Agencia EFE Tur.
  • Interviewed by IB3 Televisión. (2)
  • Interviewed by Revista Preferente.
  • Interviewed by Periódico El Económico.
  • Interviewed by TodoStartups.
  • Interviewed by Diario de La Industria Turística.
  • Interviewed by Revista Emprendedores.
  • Interviewed by IB3 Televisión. (1) 

Years 2000 – 1996: Night Auditor / Accounts Clerk / Revenue Auditor. Accounts Department, Radisson Edwardian Hotels – London, England, U.K.

Main Duties and Responsibilities: 

  • Nightly revenue auditing.
  • Daily revenue auditing.
  • Banking reconciliations.
  • Overall assistance to the Financial Controller (Financial accounting, preparation, reporting, analysis, budgeting and project and incident management).

Projects and Initiatives:

  • Creator of JOIN US! Management Cross-Training Programme Project.
  • Initiator of ENGLISH LESSONS for Company Foreign Employees Initiative.
  • Proposal of NEW MARKETING Campaign Project.

Awards and Honours:

“It gives me great pleasure to honour you with the Chairman’s Award in recognition of your exceptional performance at Radisson Edwardian. Thank you for your outstanding commitment and valuable contribution to the company. If we can aspire to these standards on a daily basis then we have a very exciting future ahead of us. Congratulations and keep up the good work.” – Jasminder Singh OBE. Chairman. Radisson Edwardian Hotels.

 

“Apart of his 100% score, Miguel Angel was polite, helpful and efficient; the professionalism is reflective in his high percentage score.” – Trent Walsh. Managing Partner. GAP Analysis International. 

 

“My Primary source of inspiration for this and future company exercise (From General Manager to Room Maid for one working day) was provided by the -Join us, cross-training proposal- submitted for consideration by Miguel Angel Mudoy. He suggests that the “Leaders” within our organisation have become too far removed from the day-to-day reality of company work and would benefit from – getting back to their roots- to see how life really is at the sharp end.” – Alison Smith. General Manager. Reality check Report. 100% Employee Satisfaction Programme. Radisson Edwardian Hotels.

PREVIOUS ACADEMIC BACKGROUND (Transferable Knowledge).


Year 2016: Communication Course. Madrid Emprende – Madrid.  

Year 2016: Admission to The Founder Institute. The Founder Institute – Madrid.   

Year 2015: Admission to the Programme The Innovation Factory of the Balearic Islands (FDI). Escuela de Organización Industrial (EOI) – Majorca.  

Year 2014: Admission to the Programme of Acceleration of Technological – based Companies. Microsoft / Barrabes Next – Majorca.  

Year 2014: Admission to the Business Incubator of Technological – based Companies of the Business Centre of Technological Innovation. PARCBIT – Majorca.  

Year 2014: Communication Course. Xesca Vidal – Majorca.  

Year 2013: Social Media Management Course for Businesses. Bárcena Formación – Majorca.  

Year 2013: Admission to the Programme S.E.E.R.F. Institute of Business Innovation of the Balearic Islands – Majorca.  

Year 2013: Value Proposition Design and Business Model Generation (Lean Startup Course). Javier González – Majorca.  

Year 2013: Business Training Course. Palma Activa – Majorca.  

Year 2012: Microsoft Office Computer Course. SOIB – Majorca.  

Year 2011: Communication Course. IMFOF – Majorca.  

Year 2010: Business Management Course. IMFOF – Majorca.  

Year 2005: Business Development Course (N.E.S. Programme). Business Enterprise Centre – London.  

Year 2004: Business Training Course (N.E.S. Programme). Business School of the University of Westminster – London.

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