Services

⭓ Exploratory analysis

Exploratory data analysis is used to analyse and investigate raw data sets and understand its key characteristics, often by using non-systematic methods. It helps determine how best to manipulate raw data to get the answers you need, to discover patterns, spot anomalies, check assumptions and dig deep into what needs to be analysed. It is used to see which data-points can reveal a better understanding of data set variables and the relationships between them. It can also help determine if the statistical techniques you are considering for data analysis are appropriate.

⭓ Descriptive analysis

This is the actual data analysis that you would be reporting in your documentation. This type of analysis uses systematic statistical techniques to describe various characteristics of data set. These techniques mostly include constructing tables, charts & graphs and summarizing data characteristics textually.

⭓ Inferential analysis

Inferential analysis involves objectively and quantitatively summarizing the data, determining which data patterns are significant, and making inferential statements about data sets. Inferential analysis uses tools necessary to identify critical factors and to what degree specific inferences can be generalized to the world view as a whole. Using inferential analysis, a researcher can make more broad and stronger claims about data sets than allowed by descriptive and exploratory analyses. Areas like hypothesis testing fall into this category.

⭓ Predictive analysis

Predictive analysis uses mathematical modeling tools to construct mathematical equations that can in-turn be used to generate predictions about an unknown fact, characteristic, or event. It’s about taking the data that you know exists and make predictions about somebody [or something] not yet in that data set. Areas like structured equation modeling (SEM) and factor analysis (EFA & CFA) fall into this category.

⭓ Analysis writing

Reporting your analysis is a huge part of research documentation. It should make bulk of your research documentation. This is what the majority of your research viewers want to see; not your introduction, or abstract but your analysis, findings and results. These must be presented in a fashion that is understandable by your readers. They are reading your report because they want to know what exactly you have found out that is new. Presenting your data and findings in the best way is exactly how you answer their questions.

⭓ Analysis automation

Scientific researchers typically do their data analysis manually in spreadsheets (Excel), whereas professional data analysts typically leverage the many powerful automation tools available in Excel and R to automate various analysis tasks. Researchers, however have to update their spreadsheets and repeat the same analysis and calculations numerous times before finalizing their reporting document. Analysis automation can reduce this boring time consuming repetition, reduce calculation errors & mistakes while saving on both time & stress. Above all it reduces your dependence on the data analyst.

⭓ Research methodology advice

Overall validity and reliability of your research quality depends on the methodology you have used. Areas like research type, data collection process, data analysis process, resources, materials & tools used and their rationale fall into this category.

⭓ Other Services

Synopsis, research proposals, questionnaires, surveys, Hypothesis framing and testing, Excel & R problem solving etc.

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