- +254 712 838 494
- tkamanu@uonbi.ac.ke
- Mon - Sat: 8:00 - 16:00
is a to analyse, synthesise, and quantify qualitative and open-ended data such as natural languages. Applications of NLP include speech recognition
Big data is defined and characterized by three v's
that is often implemented in specialized computing environments/clusters - possibly involving graphical processing unit (GPU) cores
finding the optimal solution among competing alternatives. Out of all the possible solutions/scenarios
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I am a lecturer at the Statistics and Computing Unit of School of Mathematics, University of Nairobi. My broad interests are in the application of Statistics and Mathematics to solutions of problems in nature. I believe that mathematical and statistical techniques are "most useful" when implemented through computation and simulation. Otherwise, the beauty of the techniques described in the subjects can only be appreciated by the seasoned mathematician or statistician rather than the general public!.
This site seeks therefore to demystify the use of mathematics in understanding real datasets in practice through the works of my students and computation group, the DASCLAB i.e., data analytics and scientific computing laboratory.
I am thankful to my students because through their work, which will be highlighted here, we illuminate the solutions to various problems in industry, with special interest to their significance in Kenya and Africa.
The general nature of real-life problems is that they are multidiciplinary and mutifaceted.
We collaborate with leading local organizations, industry experts, and technology companies to provide the best support and resources.
miRNAVISA is a web-based tool that allows customized interrogation and comparisons of miRNA families for hypotheses generation, and comparison of the per-species chromosomal distribution of miRNA genes in different families.
We have developed a web application for health informatics in Kenya. Data from the District Health Information Systems(DHIS) can complement ocassional demographic survey data thus ensuring continuous evaluation of the data accuracy, reduction of missingness, etc.
Traffic data from our roads can be used to analyse flow/flux, speed, accidents, road-use/misuse, and even tax compliance. Data-driven decisions help in policy formulation to enhance efficiency and mitigate deaths
Dragon microrna discovery (DMD) software for de-novo discovery of miRNAs from abitrary DNA/RNA sequences with high specificity and sensitivity. DMD is dedicated to the late VB!
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This course gives an introduction to the core skills regarding data analysis, mathematical and statistical models and the application of algorithms to make sense from data via analysis.
A time series is a set of data points indexed by time. Time series data is ubiqutous in many industries and sectors of the economy such as banking/finance, agriculture, mining, etc.
Computational Methods and Data Analytics III has an approach based on data concerning algorithms, fractals, probability, statistics, image processing, medicine (neurology, radiology and psychiatry), and economy. Combining theory and application for each field and different datasets.
The science and art of extracting meaning from seemingly incomprehensible data is the core of this course. Students develop confidence and skills to apply statistical principles to solve practical problems in industry and public service.
Albert Einstein
This is an introduction to Management Information Systems and System Design Life Cycle. Kindly peruse through the documents before the next class.
The notes have the following topics: Interpolation Interpolating polynomials Introduction to cubic splines Applications of Interpolation
Easily transform your Jupyter Notebook to PDF file There is an easy way to turn our Jupyter Notebooks into PDF files. Just with a simple setup, you can access your notebook as a PDF.
Is an organized collection of (one or more) related data file(s). The way the database organizes data depends on the type of database, called its data model, which, may be hierarchical, network and relational models.
The goal of our project was to determine whether Machine Learning and predictive analytics can improve the estimated time of arrival for a shipment.
