Course Title

Data Analysis

Course Number

10676333

Instructor Name

Mohammed Dwikat

Contact Information

[email protected]

Semester(s) and academic year(s)

Second Semester 2020
Summer Semester 2019

Compulsory / Elective

Elective

This course uses a broad brush to introduce the fascinating science of statistical analysis to students from many diverse fields. Examples and applications from different fields will be used to enhance the relevancy of the basic methods of descriptive , inferential statistics and correlation that form the core learning in this course

In this course aims to introduce students to data processing & analysis  in both directions:

1. Data Processing: data types, data measures, testing normality, reliability analysis, correlations, handling outliers and missing values, filtering data, ranking and weighting data. Splitting data, Transforming data, converting data measures, computing new features and recoding.
2. Descriptive Statistics: Introduce students to several statistical techniques for summarizing and describing data. This includes a broad range of techniques for exploring and summarizing data, as well as investigating and testing underlying relationships. Testing Linearity, Normality and Homoscedasticity.
3. Inferential Statistics: Introduce students to several statistical techniques and discuss situations in which to use each technique, the assumptions made by each method, how to set up the analysis as well as how to interpret the results. This includes a broad range of techniques for exploring and summarizing data, as One sample t test, Independent samples t test, paired sample t test, ANOVA….in addition to Mann-Whitney, Wilcoxon signed Rank test, Kruskale Wallis test, Friedman Test, Kappa Measures of agreement, McNemar Test. Also student will learn nonparametric test methods as binomial, chi square goodness of fit, Chochran test in addition to other measures and methods as Risk (odds Ratio), Linear, Logistic and ordinal Regression. Using control charts, Data Visualization tools to represent data in graphical mode. Finally student should be able to write executive summary for the analysis

At the end of this course all students are expected to demonstrate the ability to:

1. Use Data Files, Structuring Files, Data Measure Types
2. Be able to do Data preparation as Transformation, creating new features, filtering weighting and ranking cases.
3. Apply Descriptive statistics and inferential statistics
4. Detect outliers, extreme values, missing values, clean the data
5. Understand the inferential statistics , Null hypothesis, Data Analysis methods
6. Use predictive and descriptive methods
7. Analyze data relationships using correlation

Writ executive summary for any analysis project

SPSS SURVIVAL MANUAL,A step by step guide to Data Analysis using SPSS, 4th edition, Julie Pallant.Copyright © Julie Pallant 2011

Microsoft Excel , IBM SPSS 21