Automated Machine Learning for Business

2021 ◽  
Author(s):  
Kai R. Larsen ◽  
Daniel S. Becker
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Graduate Program ◽  
School Curriculum ◽  
High School Curriculum ◽  
Stakeholder Groups ◽  
New Development ◽  
Automated Machine Learning ◽  
Undergraduate Programs

In Automated Machine Learning for Business, we teach the machine learning process using a new development in data science: automated machine learning. AutoML, when implemented properly, makes machine learning accessible to most people because it removes the need for years of experience in the most arcane aspects of data science, such as the math, statistics, and computer science skills required to become a top contender in traditional machine learning. Anyone trained in the use of AutoML can use it to test their ideas and support the quality of those ideas during presentations to management and stakeholder groups. Because the requisite investment is one semester-long undergraduate course rather than a year in a graduate program, these tools will likely become a core component of undergraduate programs, and over time, even the high school curriculum.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Data science skills: Building partnership for efficient school curriculum delivery in Africa

2020 ◽  
Vol 36 ◽  
pp. 49-62
Author(s):  
Nureni Olawale Adeboye ◽  
Peter Osuolale Popoola ◽  
Oluwatobi Nurudeen Ogunnusi
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Open Data ◽  
School Curriculum ◽  
Global Perspective ◽  
Special Focus ◽  
African Countries ◽  
Tertiary Institutions ◽  
Data Description ◽  
Curriculum Delivery

Data science is a concept to unify statistics, data analysis, machine learning and their related methods in order to analyze actual phenomena with data to provide better understanding. This article focused its investigation on acquisition of data science skills in building partnership for efficient school curriculum delivery in Africa, especially in the area of teaching statistics courses at the beginners’ level in tertiary institutions. Illustrations were made using Big data of selected 18 African countries sourced from United Nations Educational, Scientific and Cultural Organization (UNESCO) with special focus on some macro-economic variables that drives economic policy. Data description techniques were adopted in the analysis of the sourced open data with the aid of R analytics software for data science, as improvement on the traditional methods of data description for learning and thus open a new charter of education curriculum delivery in African schools. Though, the collaboration is not without its own challenges, its prospects in creating self-driven learning culture among students of tertiary institutions has greatly enhanced the quality of teaching, advancing students skills in machine learning, improved understanding of the role of data in global perspective and being able to critique claims based on data.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Prediction Accuracy ◽  
Data Science ◽  
State Of The Art ◽  
Hybrid Models ◽  
The Other ◽  
Learning Models ◽  
Comprehensive Review

Abstract This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models. Application domains include stock market, marketing, E-commerce, corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on advancement of hybrid models as more than 51% of the reviewed articles applied hybrid model. On the other hand, it is found that based on the RMSE accuracy metric, hybrid models had higher prediction accuracy than other algorithms. While it is expected the trends go toward the advancements of deep learning models.

scholarly journals Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Prediction Accuracy ◽  
Data Science ◽  
State Of The Art ◽  
Hybrid Models ◽  
The Other ◽  
Learning Models ◽  
Comprehensive Review

This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models. Application domains include stock market, marketing, E-commerce, corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on advancement of hybrid models as more than 51% of the reviewed articles applied hybrid model. On the other hand, it is found that based on the RMSE accuracy metric, hybrid models had higher prediction accuracy than other algorithms. While it is expected the trends go toward the advancements of deep learning models.

scholarly journals Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

scholarly journals Self-Service Data Science in Healthcare with Automated Machine Learning

2020 ◽  
Vol 10 (9) ◽  
pp. 2992
Author(s):  
Richard Ooms ◽  
Marco Spruit
Keyword(s):  
Machine Learning ◽  
User Interfaces ◽  
Web Application ◽  
Data Science ◽  
Design Science ◽  
Science Research ◽  
Research Approach ◽  
Automated Machine Learning ◽  
Self Service ◽  
Service Data

(1) Background: This work investigates whether and how researcher-physicians can be supported in their knowledge discovery process by employing Automated Machine Learning (AutoML). (2) Methods: We take a design science research approach and select the Tree-based Pipeline Optimization Tool (TPOT) as the AutoML method based on a benchmark test and requirements from researcher-physicians. We then integrate TPOT into two artefacts: a web application and a notebook. We evaluate these artefacts with researcher-physicians to examine which approach suits researcher-physicians best. Both artefacts have a similar workflow, but different user interfaces because of a conflict in requirements. (3) Results: Artefact A, a web application, was perceived as better for uploading a dataset and comparing results. Artefact B, a Jupyter notebook, was perceived as better regarding the workflow and being in control of model construction. (4) Conclusions: Thus, a hybrid artefact would be best for researcher-physicians. However, both artefacts missed model explainability and an explanation of variable importance for their created models. Hence, deployment of AutoML technologies in healthcare remains currently limited to the exploratory data analysis phase.

scholarly journals Understanding quality of analytics trade-offs in an end-to-end machine learning-based classification system for building information modeling

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Minjung Ryu ◽  
Hong-Linh Truong ◽  
Matti Kannala
Keyword(s):  
Machine Learning ◽  
Classification System ◽  
Data Science ◽  
Building Information Modeling ◽  
Information Modeling ◽  
Time Cost ◽  
Trade Offs ◽  
Building Information ◽  
End To End

AbstractOptimizing quality trade-offs in an end-to-end big data science process is challenging, as not only do we need to deal with different types of software components, but also the domain knowledge has to be incorporated along the process. This paper focuses on methods for tackling quality trade-offs in a common data science process for classifying Building Information Modeling (BIM) elements, an important task in the architecture, engineering, and construction industry. Due to the diversity and richness of building elements, machine learning (ML) techniques have been increasingly investigated for classification tasks. However, ML-based classification faces many issues, w.r.t. vast amount of data with heterogeneous data quality, diverse underlying computing configurations, and complex integration with industrial BIM tools, in an end-to-end BIM data analysis. In this paper, we develop an end-to-end ML classification system in which quality of analytics is considered as the first-class feature across different phases, from data collection, feature processing, training to ML model serving. We present our method for studying the quality of analytics trade-offs and carry out experiments with BIM data extracted from Solibri to demonstrate the automation of several tasks in the end-to-end ML classification. Our results have demonstrated that the quality of data, data extraction techniques, and computing configurations must be carefully designed when applying ML classifications for BIM in order to balance constraints of time, cost, and prediction accuracy. Our quality of analytics methods presents generic steps and considerations for dealing with such designs, given the time, cost, and accuracy trade-offs required in specific contexts. Thus, the methods could be applied to the design of end-to-end BIM classification systems using other ML techniques and cloud services.

scholarly journals Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

scholarly journals Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

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