- Data mining: breaking down large raw databases into manageable chunks of information that can be usable. We also identify anomalies in groups of data and assess the dependencies between different data groups to come up with correlations between them. Data mining is used for determining behavioral patterns in areas ranging from consumer behavior and voter choices to health research areas involving patient data in many clinical trials.
- Text analytics: Text analytics is used to develop predictive models involving processing huge chunks of unstructured texts to develop algorithms. It includes linguistic analysis, pattern recognition in textual data, and filtering out noise from signals.
- Data visualization: It involves laying out data in a visual format for a better assessment. It helps make complex data understandable. Examples include bar charts, histograms, graphs, and pie charts
- Organizational intelligence: It involves transforming data into actionable insights for an organization. These results are used for making strategies and involve using visual tools such as heat maps, pivot tables, and mapping techniques.
Analysis and Training
Our social responsibility drives our training objective to enhance access to world-class data analysis skills and open access tools to the less reached groups. It is always our excitement and joy whenever our course participants present publication, thesis, or presentation-ready analysis on the last day of our courses. We perform capacity building activities through presentations in workshops, seminars and teaching of analytics especially using open-access programming languages R, Python and Structured Query Language (SQL). We aim to enhance access to world-class data analysis skills and open access tools to the less reached groups.
World-Class Quality Research Design and Analysis Mentoring
A team of analysts brings to you more than 15 years in teaching, academic supervision and research work together to ensure world-class data analysis for small reports to large academic projects. We provide swift, high-quality, and interactive statistical design and analysis mentoring for your research while unearthing the expert in you! We provide scientific oversight and guidance for the statistical aspects of quantitative and qualitative research studies and grant applications including but not limited to: study design, evaluating the statistical properties of alternative study design and analysis options, sample size/power estimation and justification, preparing Statistical Analysis Plans (SAP), conducting statistical analyses and interpretation as stipulated in SAP, and the presentation and dissemination of study findings in thesis, dissertations, conferences and journal manuscripts. We mentor you to handle the statistical aspects of your research with care and great precision. Contact us now for a free initial mentoring consultation by clicking on "Contact". We offer a custom-made statistical mentoring.
Leveraging Big Data Analytics to Increase Productivity Through Evidence-Backed Decisions
Technology use in every sector is increasingly generating huge amounts of information that can yield valuable insights. Such data needs to be supplemented with its analysis for obtaining decision-making insights. Data analytics helps businesses, governments and industries make sense of the vast volumes of information for further growth and development. Investing in analytics is the difference between successful and failing organizations in the present and the years to come. Data analytics refers to all the processes and tools required to process a set of data and interpret important insights from them. Analytics tools can either qualitative such as quality of life surveys in the medical field, voter behaviour and choices in political processes or quantitative such as statistical tools or software. Data analytics assists extract, and bifurcate useful data from unnecessary information and analyse them to come up with patterns and numerical data that can help in making a profitable change while helping predict customer, voter or system trends and behaviours. This increases productivity through evidence-backed decisions.
Aspects we are involved in
Data analytics is an essential asset for organizations to obtain a competitive advantage. It helps in:
- Prediction and knowledge discovery capabilities,
- Development of targeted content which helps to determine which segment will respond best to the campaign. It saves resources utilized in making conversions and improves the overall efficiency of the marketing or campaign efforts.
- Operational efficiency: Data analytics can also help organizations identify other potential opportunities to streamline operations or maximize their impact. It helps identify potential problems, eliminating the process of waiting for them to occur and then taking action on the same. This allows organizations to see which operations have yielded the best overall results under various conditions and identify which operational areas are error-prone and which ones need to be improved.