Big data courses with Presciient
Learn from an analytics leader with Dr Eugene Dubossarsky’s highly regarded courses in data science, machine learning and big data.
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Click on the city and date links below for further details including pricing, venue and course registration.
For any enquiries, please email enquiries@presciient.com, or call +61 4 1457 3322 (Australia and international) or 0800-424282 (New Zealand, toll-free).
Predictive analytics, machine learning, data science and AI
Our leading course has transformed the machine-learning and data-science practice of the many managers, sponsors, key stakeholders, entrepreneurs and beginning data-science practitioners who have attended it. This course is an intuitive, hands-on introduction to data science and machine learning. The training focuses on central concepts and key skills, leaving the trainee with a deep understanding of the foundations of data science and even some of the more advanced tools used in the field. The course does not involve coding, or require any coding knowledge or experience. This course is also advertised as “Introduction to Data Science” and “Introduction to Machine Learning”.
Advanced masterclass 2: Random forests
Explore the many unique applications and extensions of the randomForest package, many of which are implemented in R, so you can easily solve problems not susceptible to other methods, including deep learning.
Learn about the random forest algorithm and a wide range of its applications in areas including fraud, risk and outlier detection, along with associated packages and extensions that enable visualisations and other features. You’ll also learn how to address the common challenge of dealing with small data sets and classes.
Forecasting and trend analytics
This course is an intuitive introduction to forecasting and analysis of time-series data. We will review a range of standard forecasting methods, including ARIMA and exponential smoothing, along with standard means of measuring forecast error and benchmarking with naive forecasts, and standard pre-processing/de-trending methods such as differencing and missing value imputation. Other topics will include trend/seasonality/noise decomposition, autocorrelation, visualisation of time series, and forecasting with uncertainty.
Introduction to data science
Our leading course has transformed the machine-learning and data-science practice of the many managers, sponsors, key stakeholders, entrepreneurs and beginning data-science practitioners who have attended it. This course is an intuitive, hands-on introduction to data science and machine learning. The training focuses on central concepts and key skills, leaving the trainee with a deep understanding of the foundations of data science and even some of the more advanced tools used in the field. The course does not involve coding, or require any coding knowledge or experience. This course is also advertised as “Predictive Analytics, Machine Learning, Data Science and AI” and “Introduction to Machine Learning”.
Advanced R
This class builds on “Introduction to R” by providing students with powerful, modern R tools including pipes, the tidyverse, and many other packages that make coding for data analysis easier, more intuitive and more readable. The course will also provide a deeper view of functional programming in R, which also allows cleaner and more powerful coding, as well as R Markdown, R Notebooks, and the shiny package for interactive documentation, browser-based dashboards and GUIs for R code.
Fraud and anomaly detection
This course presents statistical, computational and machine-learning techniques for predictive detection of fraud and security breaches. These methods are shown in the context of use cases for their application, and include the extraction of business rules and a framework for the interoperation of human, rule-based, predictive and outlier-detection methods.
Methods presented include predictive tools that do not rely on explicit fraud labels, as well as a range of outlier-detection techniques including unsupervised learning methods, notably the powerful random-forest algorithm, which can be used for all supervised and unsupervised applications, as well as cluster analysis, visualisation and fraud detection based on Benford’s law. The course will also cover the analysis and visualisation of social-network data.
A basic knowledge of R and predictive analytics is advantageous.
Introduction to Python for data analysis
Python is a high-level, general-purpose language used by a thriving community of millions. Data-science teams often use it in their production environments and analysis pipelines, and it’s the tool of choice for elite data-mining competition winners and deep-learning innovations. This course provides a foundation for using Python in exploratory data analysis and visualisation, and as a stepping stone to machine learning.
Advanced machine learning masterclass
This course is for experienced machine-learning practitioners who want to take their skills to the next level by using R to hone their abilities as predictive modellers. Trainees will learn essential techniques for real machine-learning model development, helping them to build more accurate models. In the masterclass, participants will work to deploy, test, and improve their models. This course is also advertised as “Kaggle Boot Camp”.
Introduction to machine learning
Data science, predictive modelling and big data skills are of vital and growing importance in commercial, government, and not-for-profit contexts, particularly for managers and those in risk, customer and IT functions. Learn the fundamentals of predictive modelling, including coverage of generalised linear models, support vector machines, decision trees, and tree boosting machines. This course also covers a range of other key data mining tools, including cluster analysis. This course is also advertised as “Predictive Analytics, Machine Learning, Data Science and AI” and “Introduction to Data Science”.
Kaggle boot camp: Feature engineering and advanced algorithms
This course is for experienced machine-learning practitioners who want to take their skills to the next level by using R to hone their abilities as predictive modellers. Trainees will learn essential techniques for real machine-learning model development, helping them to build more accurate models. In the masterclass, participants will work to deploy, test, and improve their models. Note: This is an alternative title for the course “Advanced Machine Learning Masterclass”.
Introduction to R and data visualisation
R is the world’s most popular data mining and statistics package. It’s also free, and easy to use, with a range of intuitive graphical interfaces. This two-day course will introduce you to the R programming language, teaching you to create functions and customise code so you can manipulate data and begin to use R self-sufficiently in your work.
Text and language analytics
Text analytics is a crucial skill set in nearly all contexts where data science has an impact, whether that be customer analytics, fraud detection, automation or fintech. In this course, you will learn a toolbox of skills and techniques, starting from effective data preparation and stretching right through to advanced modelling with deep-learning and neural-network approaches such as word2vec.
Quantum computing
This is an introduction to the exciting new field of quantum computing, including programming actual quantum computers in the cloud. Quantum computing promises to revolutionise cryptography, machine learning, cyber security, weather forecasting and a host of other mathematical and high-performance computing fields. A practical component will include writing quantum programs and executing them on simulators as well as on actual quantum computers in the cloud.
Training for all R courses will be conducted with Microsoft R Open, the Enhanced Distribution of R. “Predictive Analytics, Machine Learning and Data Science for Big Data” and related courses, along with all advanced courses, will also include use of Azure ML, Microsoft's interactive machine learning platform in the cloud. Some Python training may be demonstrated using Microsoft Azure Notebooks or on the Microsoft Data Science Virtual Machine.
Predictive analytics, machine learning, data science and AI
Our leading course has transformed the machine-learning and data-science practice of the many managers, sponsors, key stakeholders, entrepreneurs and beginning data-science practitioners who have attended it. This course is an intuitive, hands-on introduction to data science and machine learning. The training focuses on central concepts and key skills, leaving the trainee with a deep understanding of the foundations of data science and even some of the more advanced tools used in the field. The course does not involve coding, or require any coding knowledge or experience. This course is also advertised as “Introduction to Data Science” and “Introduction to Machine Learning”.
Advanced machine learning masterclass
This course is for experienced machine-learning practitioners who want to take their skills to the next level by using R to hone their abilities as predictive modellers. Trainees will learn essential techniques for real machine-learning model development, helping them to build more accurate models. In the masterclass, participants will work to deploy, test, and improve their models. This course is also advertised as “Kaggle Boot Camp”.
Advanced masterclass 2: Random forests
Explore the many unique applications and extensions of the randomForest package, many of which are implemented in R, so you can easily solve problems not susceptible to other methods, including deep learning.
Learn about the random forest algorithm and a wide range of its applications in areas including fraud, risk and outlier detection, along with associated packages and extensions that enable visualisations and other features. You’ll also learn how to address the common challenge of dealing with small data sets and classes.
Introduction to machine learning
Data science, predictive modelling and big data skills are of vital and growing importance in commercial, government, and not-for-profit contexts, particularly for managers and those in risk, customer and IT functions. Learn the fundamentals of predictive modelling, including coverage of generalised linear models, support vector machines, decision trees, and tree boosting machines. This course also covers a range of other key data mining tools, including cluster analysis. This course is also advertised as “Predictive Analytics, Machine Learning, Data Science and AI” and “Introduction to Data Science”.
Forecasting and trend analytics
This course is an intuitive introduction to forecasting and analysis of time-series data. We will review a range of standard forecasting methods, including ARIMA and exponential smoothing, along with standard means of measuring forecast error and benchmarking with naive forecasts, and standard pre-processing/de-trending methods such as differencing and missing value imputation. Other topics will include trend/seasonality/noise decomposition, autocorrelation, visualisation of time series, and forecasting with uncertainty.
Kaggle boot camp: Feature engineering and advanced algorithms
This course is for experienced machine-learning practitioners who want to take their skills to the next level by using R to hone their abilities as predictive modellers. Trainees will learn essential techniques for real machine-learning model development, helping them to build more accurate models. In the masterclass, participants will work to deploy, test, and improve their models. Note: This is an alternative title for the course “Advanced Machine Learning Masterclass”.
Introduction to data science
Our leading course has transformed the machine-learning and data-science practice of the many managers, sponsors, key stakeholders, entrepreneurs and beginning data-science practitioners who have attended it. This course is an intuitive, hands-on introduction to data science and machine learning. The training focuses on central concepts and key skills, leaving the trainee with a deep understanding of the foundations of data science and even some of the more advanced tools used in the field. The course does not involve coding, or require any coding knowledge or experience. This course is also advertised as “Predictive Analytics, Machine Learning, Data Science and AI” and “Introduction to Machine Learning”.
Introduction to R and data visualisation
R is the world’s most popular data mining and statistics package. It’s also free, and easy to use, with a range of intuitive graphical interfaces. This two-day course will introduce you to the R programming language, teaching you to create functions and customise code so you can manipulate data and begin to use R self-sufficiently in your work.
Advanced R
This class builds on “Introduction to R” by providing students with powerful, modern R tools including pipes, the tidyverse, and many other packages that make coding for data analysis easier, more intuitive and more readable. The course will also provide a deeper view of functional programming in R, which also allows cleaner and more powerful coding, as well as R Markdown, R Notebooks, and the shiny package for interactive documentation, browser-based dashboards and GUIs for R code.
Text and language analytics
Text analytics is a crucial skill set in nearly all contexts where data science has an impact, whether that be customer analytics, fraud detection, automation or fintech. In this course, you will learn a toolbox of skills and techniques, starting from effective data preparation and stretching right through to advanced modelling with deep-learning and neural-network approaches such as word2vec.
Fraud and anomaly detection
This course presents statistical, computational and machine-learning techniques for predictive detection of fraud and security breaches. These methods are shown in the context of use cases for their application, and include the extraction of business rules and a framework for the interoperation of human, rule-based, predictive and outlier-detection methods.
Methods presented include predictive tools that do not rely on explicit fraud labels, as well as a range of outlier-detection techniques including unsupervised learning methods, notably the powerful random-forest algorithm, which can be used for all supervised and unsupervised applications, as well as cluster analysis, visualisation and fraud detection based on Benford’s law. The course will also cover the analysis and visualisation of social-network data.
A basic knowledge of R and predictive analytics is advantageous.
Introduction to Python for data analysis
Python is a high-level, general-purpose language used by a thriving community of millions. Data-science teams often use it in their production environments and analysis pipelines, and it’s the tool of choice for elite data-mining competition winners and deep-learning innovations. This course provides a foundation for using Python in exploratory data analysis and visualisation, and as a stepping stone to machine learning.
Quantum computing
This is an introduction to the exciting new field of quantum computing, including programming actual quantum computers in the cloud. Quantum computing promises to revolutionise cryptography, machine learning, cyber security, weather forecasting and a host of other mathematical and high-performance computing fields. A practical component will include writing quantum programs and executing them on simulators as well as on actual quantum computers in the cloud.
Training for all R courses will be conducted with Microsoft R Open, the Enhanced Distribution of R. “Predictive Analytics, Machine Learning and Data Science for Big Data” and related courses, along with all advanced courses, will also include use of Azure ML, Microsoft's interactive machine learning platform in the cloud. Some Python training may be demonstrated using Microsoft Azure Notebooks or on the Microsoft Data Science Virtual Machine.