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Big Data: Predicting the future

What does the course consist of?


When
: July 1 - 5, 2019
Where: University of Deusto, Bilbao Campus
Language: English
Duration: 5 days
Credits: 1 ECTS
Registration: Registration will open on May 8th 

Course content

  • The paradigm of Big Data: what it is and what new features it brings to companies
  • From the description of the past, to the prediction of the future to prescribe the best decision and action
  • Types of data that affect the decision making of companies
  • Knowledge discovery techniques
    • Descriptive techniques
      • Cluster analysis
      • Association rules
      • Factorial analysis
    • Predictive techniques
      • Classification
      • Regression
    • Optimization models
    • Text mining and semantic text analysis
    • Social Networks Analysis (SNA)
  • SMART methodology
    • S → Data Strategy.
    • M → Metrics and data indicators.
    • A → Analytical methods to discover opportunities and knowledge.
    • R → Reporting or visualization of results.
    • T → Transformation or organizational decision making.
  • Analytical models: statistics and artificial intelligence (differences and applications to the company)
    • Processing data of the functional areas of the company for the improvement of the Annual Management Plan: some examples
    • Making marketing decisions to get more market or more profitability
    • Customer personalization: right message, right channel, right time and right person
    • Financial decision making for the optimization of the cost structure
    • Propensity models to predict the success, failure or departure of employees of my company (People Analytics)
    • Identifying financial efficiency frameworks in the procurement process of my organization.
    • Optimizing prices in the omnichannel era
  • Design and development of a KPI dashboard using libraries and dashboard tools
     

Managing and processing large volumes of data, or “Big Data”, and gaining meaningful insights is a significant challenge facing the future of many companies. As a consequence, many business are demanding data analytics competencies for the job positions that are opening. This has a significant impact on a wide range of domains, including health care, marketing, human resources, digital economy, finance, etc.

Despite considerable progress in high performance, storage capacity, and computation power, challenges remain in identifying, clustering, classifying, and interpreting a large spectrum of information. That’s why it is necessary to acquire skills and knowledge in the fields of analytics, machine learning, and high-performance computing.
Thus, the goal of this workshop is to train professionals capable of completing cycles of data analysis (extraction, management, processing and visualization) to offer business analytics services to organizations, companies and individuals. Accordingly, participants will learn to master the main technologies of analysis and processing of large volumes of data, as well as other tools to enhance the value of the analyzed data and thus allow organizations to make more informed decisions.

Target group

People who want to acquire a solid base of data analysis and processing technologies to enable organizations to make more informed decisions. It is not necessary to have a computer science profile to participate in the workshop, although minimum analytical bases are desirable. We usually have a heterogeneous group in terms of academic profiles, a diversity that creates an enriching environment for learning full of experiences, problems and shared solutions.

Course aim

At the end, the participant must be able to understand and use analytical methods that allow any organization to find opportunities for greater margins and profitability through analytical techniques.
More specifically, it is intended that students can:

  • Understand the difference between unsupervised and supervised machine learning methods to improve the future decisions of any given company.
  • Learn what can I contribute to my business and where can it can be applied to improve both financial and operational efficiency
  • Understand the different techniques of Machine Learning to find business opportunities in companies.
  • Develop several application cases so that through benchmarking, the participants can then find opportunities in the future.

Duration

5 sessions, one per day, 25 hours in total (1 ECTS credit)

Methodology and Assessment

Through an active learning hands-on approach, we will carry out a different set of activities to understand and solve different organizational problems’ using supervised and non-supervised machine learning methods.

What does the course consist of?


When
: July 1 - 5, 2019
Where: University of Deusto, Bilbao Campus
Language: English
Duration: 5 days
Credits: 1 ECTS
Registration: Registration will open on May 8th 

Course content

  • The paradigm of Big Data: what it is and what new features it brings to companies
  • From the description of the past, to the prediction of the future to prescribe the best decision and action
  • Types of data that affect the decision making of companies
  • Knowledge discovery techniques
    • Descriptive techniques
      • Cluster analysis
      • Association rules
      • Factorial analysis
    • Predictive techniques
      • Classification
      • Regression
    • Optimization models
    • Text mining and semantic text analysis
    • Social Networks Analysis (SNA)
  • SMART methodology
    • S → Data Strategy.
    • M → Metrics and data indicators.
    • A → Analytical methods to discover opportunities and knowledge.
    • R → Reporting or visualization of results.
    • T → Transformation or organizational decision making.
  • Analytical models: statistics and artificial intelligence (differences and applications to the company)
    • Processing data of the functional areas of the company for the improvement of the Annual Management Plan: some examples
    • Making marketing decisions to get more market or more profitability
    • Customer personalization: right message, right channel, right time and right person
    • Financial decision making for the optimization of the cost structure
    • Propensity models to predict the success, failure or departure of employees of my company (People Analytics)
    • Identifying financial efficiency frameworks in the procurement process of my organization.
    • Optimizing prices in the omnichannel era
  • Design and development of a KPI dashboard using libraries and dashboard tools
     

Managing and processing large volumes of data, or “Big Data”, and gaining meaningful insights is a significant challenge facing the future of many companies. As a consequence, many business are demanding data analytics competencies for the job positions that are opening. This has a significant impact on a wide range of domains, including health care, marketing, human resources, digital economy, finance, etc.

Despite considerable progress in high performance, storage capacity, and computation power, challenges remain in identifying, clustering, classifying, and interpreting a large spectrum of information. That’s why it is necessary to acquire skills and knowledge in the fields of analytics, machine learning, and high-performance computing.
Thus, the goal of this workshop is to train professionals capable of completing cycles of data analysis (extraction, management, processing and visualization) to offer business analytics services to organizations, companies and individuals. Accordingly, participants will learn to master the main technologies of analysis and processing of large volumes of data, as well as other tools to enhance the value of the analyzed data and thus allow organizations to make more informed decisions.

Target group

People who want to acquire a solid base of data analysis and processing technologies to enable organizations to make more informed decisions. It is not necessary to have a computer science profile to participate in the workshop, although minimum analytical bases are desirable. We usually have a heterogeneous group in terms of academic profiles, a diversity that creates an enriching environment for learning full of experiences, problems and shared solutions.

Course aim

At the end, the participant must be able to understand and use analytical methods that allow any organization to find opportunities for greater margins and profitability through analytical techniques.
More specifically, it is intended that students can:

  • Understand the difference between unsupervised and supervised machine learning methods to improve the future decisions of any given company.
  • Learn what can I contribute to my business and where can it can be applied to improve both financial and operational efficiency
  • Understand the different techniques of Machine Learning to find business opportunities in companies.
  • Develop several application cases so that through benchmarking, the participants can then find opportunities in the future.

Duration

5 sessions, one per day, 25 hours in total (1 ECTS credit)

Methodology and Assessment

Through an active learning hands-on approach, we will carry out a different set of activities to understand and solve different organizational problems’ using supervised and non-supervised machine learning methods.