methodology requires gathering relevant data from the specified documents and compiling databases in order to analyze the material and arrive at a more complete understanding on how African American families cope with having an autistic child, those receiving versus those not receiving professional counseling. We hope to shed light on the following questions through our research: Paradigm Study Design This research will utilize both quantitative and qualitative data collection tools, but is rooted
Words: 877 - Pages: 4
10 questions to help you make sense of qualitative research How to use this appraisal tool Three broad issues need to be considered when appraising the report of a qualitative research: • • • Are the results of the review valid? What are the results? Will the results help locally? The 10 questions on the following pages are designed to help you think about these issues systematically. The first two questions are screening questions and can be answered quickly. If the answer to both is “yes”
Words: 969 - Pages: 4
Building Data Science Teams The Skills, Tools, and Perspectives Behind Great Data Science Groups DJ Patil THE SIMPLEST WAY TO BRING MapReduce and SQL www.asterdata.com Optimized in One Database Appliance Everyone knows data is the new black. The Aster MapReduce Analytics Portfolio enables customers to quickly make use of their data for actionable insights, analysis and product innovation. - Jonathan Goldman, Director of Analytics, Teradata Aster (and former Principal Data Scientist
Words: 8024 - Pages: 33
Qualitative Data Analysis 1. Data reduction Data reduction is a term that applies to the business practice of accumulating, analyzing and ultimately transforming massive amounts of data into a series of summarized reports. The idea behind the data reduction process is to provide a complete though somewhat simplified format that can be utilized with relative ease in business settings. Several different approaches to the process may be used, with the selection of data reduction techniques and
Words: 312 - Pages: 2
Briefly describe th emajor differences between data mining and statistics. a. Statistics is user driven, while data mining is data driven. b. In statistics, there exist underlying theory about certain relationships in data. While in data mining, there is often no pre-existing theory. c. In statistics, users use statistical methods to testify the hypothesis among data. While in data mining, users often use different techniques to examine data and uncover unknown relationships. 2.
Words: 316 - Pages: 2
FINAL REPORT DATA MINING Reported by: Nguyen Bao An – M9839920 Date: 99/06/16 Outline In this report I present my study in the Data mining course. It includes my two proposed approaches in the field of clustering, my learn lessons in class and my comment on this class. The report’s outline is as following: Part I: Proposed approaches 1. Introduction and backgrounds 2. Related works and motivation 3. Proposed approaches 4. Evaluation method
Words: 5913 - Pages: 24
analysing the data you brought with you from the field and writing your research report. The report should contain feasible and useful recommendations, based on the findings of your study concerning how to solve the problem investigated. As in the first workshop there will be presentations, group work sessions and a few plenaries. In this workshop, however, group work will take up most of the time. The presentations will be concentrated in the first week, which will be devoted to data analysis. The second
Words: 1046 - Pages: 5
describes the plan on how the study will be carried out in order to obtain the required data. This chapter looks at the nature and design of the study concentrating on the various categories of the study design. The chapter will look at research procedure, data collection technique, sampling, population and the location of the study, limitations of the study as well as ethical considerations. Data analysis and presentation will also be included in this chapter. 3.2 NATURE OF THE STUDY The study
Words: 2096 - Pages: 9
guideline that I will be following for each article that includes identifying and examining the data collection and data analysis methodologies used in each study. The names of the articles are The Experience of Patients Undergoing Awake Craniotomy and The Effects of Crossed Leg Blood Pressure Measurement. The references will also be reviewed to determine validity and relationship to the new study. Data Collection Quantitative Study: The operational and conceptual definition is congruent. The key
Words: 1627 - Pages: 7
6 $37,750,000 20X5 $37,750,000/6 = $6,291,667 Exponential Smoothing: Given The last forecasting (LF) = $6,300,000, the last data (LD) = $6,750,000, a = 0.95 I have used the 0.95 alpha because I believe the new forecast will be based on the last data. NF = LF + a (LD – LF) NF= $6,300,000 + 0.95 ($6,750,000 - $6,300,000) NF = $6,300,000 + 0.95 ($450,000) NF = $6,300,000 + $427,500 NF = $6,727,500 20X5 = $6,727,500 Time Series Regression:
Words: 453 - Pages: 2
