Presciient’s courses in R and a range of data analytics disciplines are highly regarded by academics and industry leaders.
Here’s what some of our high-profile course graduates have said about their experiences.
I am a non-technical policy analyst in government and I wish I had taken this course years ago. The Introduction to R and data visualisation course was very well paced with accessible and practical content.
Eugene and Justin were terrific at tailoring content around the participants’ special interests.
I am interested in natural language data analytics—and this course in R has opened many doors for me, and solved all of the issues I had with Excel and other NLP programs.
The intro course provides the R suite of tools, intro to the environment, and access to a very supportive R community. Highly recommend attending if you are getting serious about unstructured data and written text analysis.
As a manager of analysts, I attended this course to deepen my understanding of the principles of predictive modelling in R, and I was absolutely satisfied with this course. Eugene’s explanation of the fundamentals and the theory behind the techniques was much clearer than any online resource I have come across. His knowledge of what he is teaching is first class and I would recommend Eugene to everyone who is interested in not just learning a technique, but developing true understanding.
Eugene’s courses are not your standard technical courses where you learn how to put data into a model and get a result. The real life experiences – warts and all – he brings to the instruction mean that attendees walk away with a better understanding of the real life challenges of analytics as well as the technical know-how. We routinely send our team members on these courses to help them get the capabilities that really help our clients get better insights from their data.
Eugene’s Data Science course really opened my eyes to how accessible the latest machine learning tools are. I won’t need to spend months learning a hard-core new discipline, and I can already think of ways to use this at work.
I’m very comfortable providing a personal recommendation for Eugene Dubossarsky and his data analytics course for forensic and security purposes. Foremost, I have directly observed Eugene’s status as a leader among the Australian data-science community. I have seen this includes extensive connections and work with highly skilled forensics professionals within major Australian organisations.
In addition to deep expertise, Eugene’s course is pedagogically sound and useful to ‘clients’ (e.g. managers, stakeholders) and to specialists in forensic work. My decade teaching helps me appreciate the systematic gradation of materials. This progression takes participants from demonstration and experiential engagement with simple techniques and fundamentals (e.g. pros/cons of rule-based detection) to explaining the distinguishing basis of state-of-the-art analytic techniques readily usable and accessible through open sources. I readily followed the course, yet my professional background and expertise do not include IT, maths, or computer science. But in advising on reform of operating strategies I can now communicate effectively with forensic specialists and form evaluative judgments on methods and findings.
The value of this course to an organisation is determined much, much more by the quality of its plans to exploit the knowledge attained, than whether the course is rated subjectively ‘good’ or ‘outstanding’. I suggest keying into Eugene’s depth of knowledge and potential indirect access to other national and international experts is the biggest opportunity provided by this and his other courses.
The Introduction to R course provided clear and logical assistance to getting up and running with R. More than that, the real value was in providing guidance on the myriad of online resources and introducing me to a network of passionate and helpful R users. Eugene is a knowledgeable and approachable teacher. I wouldn’t hesitate in recommending the course. I feel that I am now fully on the road to applying R and using data to improve efficiency across my organisation.
Thank you very much for the information I gathered at the Predictive Modeling course I attended recently. As a beginner in R, I thought that it might be a bit overwhelming. But I was wrong! Eugene did a fantastic job at explaining the concepts and all practical work was engaging and easy to follow. Entertaining, informative and most importantly relevant – it has already proven valuable in my work.
I have been trying to convert my Stata programming skills to R, however, there have been many times where I just wanted to sit down with someone and have them explain the fundamentals of programming in R. Sure, a number of books and websites have helped me become familiar with R, however, I still didn’t feel ready to translate all of my familiar Stata commands to R (e.g. I am comfortable plotting graphics using ggplot2, however, revert back to Stata for data manipulation). I knew that a more effective way to learn and feel confident would be to sit down with someone and have them explain how they use R, how they clean data, how they plot graphics, etc. I knew that once I felt comfortable with cleaning my data in R, analysis would be less of an issue—I’m happy to research the specifics on my own.
Thank you, Eugene for advancing my R skills. I especially appreciate the time spent explaining the fundamentals of data manipulation—i.e. the code one needs to know before running any basic or sophisticated analysis. The pace of the workshop was perfect.
I attended the Predictive Analytics course presented by Eugene Dubossarsky from Presciient in March of 2013 in North Sydney. I am primarily a computer scientist, and have a broad but very shallow knowledge of the area of machine learning and analytics. The course gave me a very good starting point to start gaining a deep knowledge of the topic. The tooling presented gives an excellent place to start learning and is useful beyond the class setting. I think the key value of the course is that it was presented by a domain expert who is passionate about the topic and growing the maturity of the field and so was very open with the sorts of insights that you don’t read in a text book. This included the high level concepts within analytics, models of thinking about analytic problems and key lessons from his career implementing predictive analytics. I therefore left the course knowing what I don’t know, and knowing where to start, which is more than I expected. I would recommend it to any computer scientist.
Last week I attended two of Presciient’s courses with Eugene Dubossarsky: “Predictive Analytics, Machine Learning and Data Science for Big Data”, and “Advanced Machine Learning Masterclass”.
I’m amazed by how much information I had in four days! Having known Eugene before, I had high expectations, but the course was even better. The first was an introductory course where he presents the main theory without using advanced math or R. The goal is to understand principles, theory, and strategies. It’s highly suitable for people starting in the field, managers, or people who work close to data scientists.
The second course is much more advanced, more practical, and has more theory. The theory is presented with the proper motivation, which helps us to understand it. We left the course having experienced an analysis end to end.
The courses are also full of tips from Eugene’s experience. Different ways to look at the data, technical tricks, advice to engage people… Pure gold. I highly recommend the courses. However, I suggest you don’t blink too much during the classes, you may lose really good information if you do. 🙂
Eugene is an excellent communicator with excellent presenting skills. He was able to break down and explain advanced statistical concepts and modelling that were very confusing and poorly explained in university courses, in a very clear and easy-to-understand manner. Eugene also effectively explains statistical concepts in a real world, engaging and relatable way and his conceptualisation of statistical concepts is clear and memorable. He is able to engage individuals of all levels of statistical backgrounds to ensure that every student is able to maintain an understanding of the material as the course progresses.
Eugene is very open to participation and active learning, where he encourages feedback and student input to tailor the course to his pupils’ needs. He is very knowledgeable and able to highlight advanced statistical analyses that are best practice and produce superior results in terms of modelling reliability and accuracy, while also highlighting effective modelling practices that produce consistent results and can be successfully utilised by pupils with a non-statistical or less advanced statistical background.
Eugene also identifies common struggles or obstacles faced in practically applied statistical modelling for those possessing a university background, and breaks this down into simple overarching ideas and approaches for those who do not. He clearly identifies and explains methods for addressing each of these that can be applied by all individuals. Moreover, Eugene is very personable and engaging and he maintains an interest in the ongoing analyses and statistical applications of his pupils. He provides continued support through one-on-one or small group meetups with pupils that wish to discuss any challenges they face in their work as they move forward, to ensure his pupils can approach statistical problems with confidence and highly developed expertise. Eugene’s passion and knowledge shines through his courses and his teaching as he addresses all the areas of the statistical practices in his course.
Eugene belongs to a rare breed of folks who can do, and can also teach. Eugene has an outstanding ability to distil complicated concepts into bits of essence. Through Eugene’s course, I got to learn about and better understand how to work with data, industry practices, and myself. The course was delivered in a very engaging way. I had a great learning experience.
Having studied stats at Uni I was surprised how far the field has progressed in the last few years, particularly in the area of big data. The great thing about Eugene’s course is I left with a sense that I was up to date with the latest big data modelling concepts but more importantly could also deploy them with some confidence using R. Eugene also made it clear he was available to answer questions after the course, so you are not left hanging. I would absolutely recommend this!
Eugene’s fraud and anomaly detection course is extremely valuable for anyone wishing to learn more about fraud detection using analytical techniques. Eugene’s ability to cater and tailor the course for all levels of experience is fantastic and much appreciated.
I have been very fortunate to be on the R & Data Visualisation course read by Dr. Eugene Dubossarky.
I was thinking of doing computational finance with R—data analysis, statistical modeling, and data visualization for large financial datasets, e.g. quantifying market risk measures—without the heavy lifting in Excel. And I was looking for the most effective introduction to programming in R.
The course exceeded all my expectations, given the breadth and quality of the information provided in Dr. Dubossarky’s presentation. The pace and structure of the course made learning intuitive and comfortable; providing cross-references between different programming languages and R showed the language capability in a familiar way; the elegance and power of R, its ability to facilitate rapid data analysis and visualisation were demonstrated in a number of real-life examples—encouraging us to integrate the course materials into our day-to-day tasks, and continue learning.
What I found also invaluable was his recommendations for numerous online resources, as well as offering his post-course support. All in all, this was the best start I was hoping for. I'd be happy to recommend this course for any corporate environment either in transition or thinking of switching to R.