{"id":11449,"date":"2018-06-04T16:14:07","date_gmt":"2018-06-04T16:14:07","guid":{"rendered":"https:\/\/websiteghana.com\/?p=11449"},"modified":"2018-06-04T16:14:07","modified_gmt":"2018-06-04T16:14:07","slug":"data-analyst-ghana-work-projects","status":"publish","type":"post","link":"https:\/\/websiteghana.com\/blog\/data-analyst-ghana-work-projects\/","title":{"rendered":"Get a Data Analyst in Ghana to Work on Your Projects"},"content":{"rendered":"

Research data analyst<\/strong> is a group of professionals with the interest in academic research<\/strong> or project work<\/strong> analysis<\/strong> in Ghana<\/strong> and beyond.\u00a0 We render statistical services<\/strong> to students in the various institutions (universities)<\/strong> who are writing on their project works<\/strong>.<\/p>\n

Our services are specified; our focus is on Data analysis<\/strong>, Methods<\/strong>, Questionnaire construction<\/strong>, and Data collection<\/strong>.\u00a0 We look forward to working with all students writing their thesis in Ph.D., Masters, and Degrees.<\/p>\n

If you are looking for a working partner whom you can trust in terms of efficiency, accuracy, timeliness, reliable, Excellency, professional and data-oriented look no further, we are a group of smart professionals; we give to you what you want, how you want it and when you need it.<\/p>\n

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Academic project work thesis analysis, Ghana<\/figcaption><\/figure>\n

Our flexible terms of services give you the opportunity to understand whatever we do in relation to your thesis analysis<\/strong>; we discuss with you what we do and make sure you understand all aspects of our statistical findings so as to foster your knowledge on your findings and assumptions. We make you the boss and the owner of all aspect of your thesis<\/strong> write-ups.<\/p>\n

Very importantly we work with you until your thesis<\/strong> is accepted or approved by your department. Terms and conditions apply.<\/p>\n

What The Data Analyst Do<\/h2>\n

The Data analyst<\/strong> is involved in the process of systematically applying statistical<\/strong> and\/or logical techniques to describe and illustrate, condense and recap, and evaluate data<\/strong>. According to Shamoo and Resnik (2003) various analytic procedures<\/strong> \u201cprovide a way of drawing inductive inferences from data <\/strong>and distinguishing the signal (the phenomenon of interest) from the noise (statistical<\/strong> fluctuations) present in the data<\/strong>\u201d.<\/p>\n

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Ph.D., Masters and Degree thesis analysis<\/figcaption><\/figure>\n

While data analysis<\/strong> in qualitative research<\/strong> can include statistical procedures<\/strong>, many times analysis<\/strong> becomes an ongoing iterative process where data<\/strong> is continuously collected and almost simultaneously. Indeed, researchers generally analyze for patterns in observations through the entire data collection<\/strong> phase (Savenye, Robinson, 2004). The form of the analysis is determined by the specific qualitative approach<\/strong> taken (field study, ethnography content analysis, oral history, biography,\u00a0unobtrusive<\/strong>\u00a0research) and the form of the data (field notes, documents, audiotape, videotape).<\/p>\n

Importance of the Data Analyst<\/h2>\n

An essential component of ensuring data integrity<\/strong> is the accurate and appropriate analysis<\/strong> of research findings<\/strong>. Improper statistical analyses<\/strong> distort scientific findings<\/strong>, mislead casual readers (Shepard, 2002), and may negatively influence the public perception of research<\/strong>. Integrity issues are just as relevant to the analysis<\/strong> of non-statistical data as well.<\/p>\n

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Research thesis analysis<\/figcaption><\/figure>\n

Why You Need The Data Analyst<\/h2>\n

There are a number of issues that researchers <\/strong>should be cognizant of with respect to data analysis<\/strong>. These include:<\/p>\n

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  1. Having the necessary skills to analyze<\/strong><\/li>\n
  2. Concurrently selecting data collection<\/strong> methods and appropriate analysis<\/strong><\/li>\n
  3. Drawing unbiased inference<\/li>\n
  4. Inappropriate subgroup analysis<\/strong><\/li>\n
  5. Following acceptable norms for disciplines<\/li>\n
  6. Determining\u00a0statistical significance<\/strong><\/li>\n
  7. Lack of clearly defined and objective\u00a0outcome measurements<\/strong><\/li>\n
  8. Providing honest and accurate analysis<\/li>\n
  9. The manner of presenting data<\/li>\n
  10. Environmental\/contextual issues<\/li>\n
  11. Data recording method<\/li>\n<\/ol>\n