our services
Customised AI-Powered Tools
Reach Out To Us Today
No matter your business’s industry, TutAnalytics can create AI-powered tools tailored to your specific needs!
Data-Driven solutions
Consultancies and Data-Driven Solutions
Workflow Enhancement and Consultations
Basics of Medical Statistics using SPSS
Unlock the power of medical statistics with our dynamic and interactive consultancies designed for healthcare professionals and researchers eager to enhance their analytical skills.
This doesn’t assume any background in statistics or AI
It offers a comprehensive introduction to the fundamentals of medical statistics, and an overview of the descriptive, inferential, and basic statistical models, seamlessly integrated with hands-on applications using SPSS, one of the most widely used statistical software packages in the field.
Basics of Medical Statistics using R
Unlock the power of medical statistics with our dynamic and interactive consultancies designed for healthcare professionals and researchers eager to enhance their analytical skills.
This doesn’t assume any background in statistics and/or R programming. It starts covering the basics from scratch!
It offers an introduction to the fundamentals of medical statistics, seamlessly integrated with R, one of the most advanced and widely used statistical software. With top-tier outstanding Medical Statisticians and R developers, you will master the future of medical statistics applications!
Introduction to R Programming
This covers employing basics and fundamental concepts of R. It is suitable to up-skill a wide range of partners e.g., statisticians, biologists, clinicians and postgraduate students in quantitative and qualitative fields.
The assumes no prior programming skills.
The consultancies are delivered by top-tier outstanding R developers who will help you to excel the skills of R programming!
Data Visualisation
Covering cutting-edge techniques of data visualisation!
This utilises the key aspects of data visualisation using R, with applications of powerful R tools such as ggplot2.
This includes methods of interactive plots and dynamic visualisations.
Dynamic reporting using Rmarkdown!
This will enable you quickly produce reports, documents, and web contents of your analysis results, seamlessly sharing them with collaborators and/or clients without complex software.
Easy Formatting – Convert Your Analysis Reporting to Various Formats – Embed Elements, e.g., links, images, and even code snippets in your reports, enhancing your content with minimal effort – Lightweight and Portable – Reproducible Analyses!
Simplify the process of creating and sharing dynamic, well-structured reports!
Development of Dashboards & Web-Application!
Achieving an exceptional integration of modern interactive data visualisation and outstanding user experiences through dashboards, providing real-time insights and analytics.
This will equip you with the tools required for aiding in quick decision-making!
Supervised Machine Learning (Classification)
This projects utlise the statistical learning techniques for classification (supervised learning) and their various approaches with applications in R!
We analyse high-throughput real as well as toy example data sets and compute estimates of the misclassification rate, of the area under the receiver-operating characteristic (ROC) and of other relevant assessment measurements.
This is beneficial for a wide range of partners e.g., statisticians, data scientists, bioinformaticians, engineers and researchers in different fields.
Unsupervised Machine Learning (Clustering)
Utilising statistical learning techniques for clustering (unsupervised learning) and their various approaches with applications!
This training introduces basic multivariate concepts, such as notions of multivariate analysis, e.g., covariance, correlation and Euclidean distance. It will also covers an introduction to the similarity measures, k-means clustering, its visualising solutions and hierarchical clustering with various linkage methods, e.g., agglomerative hierarchical clustering and additive trees.
This training is beneficial for a wide range of participants e.g., statisticians, data scientists, bioinformaticians, engineers and postgraduate students in different fields.