Provalis Research Trainer / Consultant

Gwin NYAKUENGAMA
TrainerGwin NYAKUENGAMA

LanguagePrimary: English
Secondary: French
LocationCanberra, Australia
Coverage AreaPrimary: Canberra, Australia and worldwide, remotely
Secondary: Australia, outside Canberra; New Zealand; Africa; UK; Europe (provided travel costs covered)
Training WorkshopsQDA Miner:
o Advanced
o Intermediate
o Basic

WordStat
o Advanced
o Intermediate
o Basic

Consulting servicesDr Gwin Nyakuengama founded DatAnalytics, an Australian small business that provides ethical and reliable data analytics advice and technical support services:
• Advanced statistical data analysis of text, time series, regressions, structural equation modelling, panel, survey, survival datasets using the following data analytical tools: QDA Miner and WordStat, SAS, Stata, R, RapidMiner and Tibco Spotfire.
• Supervised and unsupervised machine learning algorithms, including Naive Bayes; Generalized Linear Model (GLM), Logistic Regression, Deep Learning, Random Forest and Gradient Boosted Trees (XGBoost).
• Natural Language Processing (NLP) and Text Analytics (including Sentiment Analysis and Automatic Document Classification or Stylometry) using a variety of data tools.
• Reviewing of draft scientific reports, theses, journal papers and conference presentations.
• Independent statistical expert witness needs.
• Assisting M.Sc. and Ph.D. students at all stages of thesis work.



Services
Summary: WordStat
/ QDA Miner
Consultations
• Project specific, one-on-one consultations.
Workshops
• 2/3 day intensive training on general qualitative data analysis, using the Provalis Research tools
Training courses
• Customer designed training for your company on introductory, intermediate and advanced text analytics
Delivery of courses
• Courses will be delivered by Doctor Gwin Nyakuengama. Every possibility to team-up with other experts will be explored and announced prior to training events.
Delivery modes
• Face-to-face: Canberra, Australia. Other places and countries will be considered, provided travel costs are covered.
• Remote: Via Teachable and GoToTraining online applications
After training
• We seek customer feedback.
• We offer an after-service customer support.


ExperienceOver 20 years leading and teaching research methods, and undertaking data analytics in support of the Australian Federal Government signature policies on OHS, Education, VET and Employment.
Spearheading a major CSIRO flag-ship, industry significant 4-year project with international partners.
Leading, undertaking and teaching quantitative data analytics of text, time series, survey, survival, longitudinal data and forest genetics.
Heading qualitative data analytics of large, national Australian surveys used in public policy formulation and evaluation.
Authoring and reporting research results in various formats and genres; including scientific reports, peer-reviewed- and conference-papers, academic theses, parliamentary documents and media releases.
Providing training and customer support in quantitative data analysis (using Stata, SAS and mapping tools) and qualitative data analysis (using QDA/WordStat) in the Australian Federal Government and an Australian State Department.

EducationPh.D. from University of Melbourne, Australia
B.Sc. (with first class honours) from the National Australian University, Australia
Graduate Diploma in Public Sector Leadership from Griffith University, Australia
French diploma from CAVILAM de Vichy, France.

PublicationsAcademic Theses
J.G. Nyakuengama 1998: Thermomechanical pulping, wood science and quantitative genetics of Radiata pine D. Don. Ph.D. Thesis. Forestry Department. Melbourne University.
J.G. Nyakuengama 1991: The physical and chemical properties of a superior radiata pine (Pinus radiata D. Don) family compared with standard radiata pine family. B.Sc. Honours Thesis. Forestry Department. Australian National University.

Peer-reviewed Journal Articles
J.G. Nyakuengama; G.M. Downes; J. Ng 2003: Fibre properties changes in fertilized Pinus radiata. IAWA J. 24 (4), 2003: 397–409).
J.G. Nyakuengama; G.M. Downes; J. Ng 2002: Growth and wood density responses to later-age fertilizer application in Pinus radiata. IAWA J. 23(4): 431-448.
J.G. Nyakuengama; A.C. Matheson; R. Evans; D.J. Spencer; P. Vinden. 2000 b: Wood quality and quantitative genetics of Pinus radiata. Heartwood formation and moisture status. Appita J. 53(1): 30-35.
J.G. Nyakuengama; A.C. Matheson; R. Evans; D. Spencer; P. Vinden 2000 c: Effect of Age on Genetic Control of Pinus radiata Earlywood and Latewood Properties. Appita J. 53(2) 103:107.
J.G. Nyakuengama; R. Evans; A.C. Matheson; D.J. Spencer; P. Vinden. 1999 a: Wood quality and quantitative genetics of Pinus radiata. Fibre traits and wood density. Appita J. 52(5): 348-357.
J.G. Nyakuengama; A.C. Matheson; R. Evans; D.J. Spencer; P. Vinden. 1998 a: Wood quality and quantitative genetics of Pinus radiata. Cross-correlation between growth traits, wood microstructure traits and heartwood formation traits. Appita J. 51(1): 35-38.
J.G. Nyakuengama; A.C. Matheson; R. Evans; D.J. Spencer; P. Vinden. 1997 b: Wood quality and quantitative genetics of Pinus radiata. Time trends in the genetic control of wood microstructure traits and their interrelationships. Appita J. 50(6): 486-494.

Australian Federal Government Confidential Documents
J.G. Nyakuengama 2001-2017: Briefs, Internal Research Reports, Parliamentary Reports and Conference Proceedings.

Conference Article
J.G. Nyakuengama 2017: Stata: A key strategic statistical tool of choice in major impact evaluations of socioeconomic programs: https://www.stata.com/meeting/oceania17/slides/oceania17_Nyakuengama.pdf

Qualitative Data Analysis Blogs
J.G. Nyakuengama 2018 a: Stylometry – Authorship Attribution (Early British Fictionists)
https://dat-analytics.net/2018/12/02/stylometry-authorship-attribution-early-british-fictionists/
J.G. Nyakuengama 2018 b: Prototyping A WordStat – QDA Miner Automatic Document Classification Model For Product Review: https://dat-analytics.net/wp-content/uploads/2018/12/Prototyping-A-WordStat-QDA-Miner-Automatic-Document-Classification-Model-For-Product-Review.pdf
More informationLinkedIn
ResearchGate
Twitter

EmailDatAnalytics@iinet.com.au
Websitehttp://dat-analytics.net

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