Every business is now a data business. Managing with analytics starts with deciding strategic data needs. Then this data is used to improve business decisions and operations and to yield benefits and profits. This course describes the concepts and processes of sourcing & collecting data and turning data into insights. Managing and competing with analytics requires creating the technology and data infrastructure and building data competencies in the organization. In the meanwhile, data ethics and governance ensures that data does not become a liability. To this end, the course also reviews organizational and technological infrastructure, as well as data governance. Throughout the course, illustrations and case problems are provided for demonstrating how data strategy is executed in practice.
Data has become an essential strategic asset for many organizations in achieving competitive advantage and success. This course is designed to deliver a comprehensive introduction to visual analytics and business intelligence concepts and provide students with the knowledge and technical skills to support data-driven decision-making. Topics covered include data preparation and quality, dashboard implementation, and spatial analysis. The course uses state-of-the-art visual analytics software Tableau to provide hands-on experience. Students will work in groups to learn how to apply analytical techniques to sift through data and provide actionable business insights.
The objective of this course is to provide students with an introduction to the core concepts in data and information management. Enterprise data management systems are at the heart of modern business information systems. They facilitate information sharing across the organization and, therefore, support the notion that data is a corporate asset. Corporate data must be managed effectively to ensure the continued success of the organization. Data management, which focuses on data collection, storage, and retrieval, is a central activity for any organization. Topics covered include the principles of database design, modeling using the entity relationship model, the relational data model and relational database constraints, design techniques of relational database systems, and the Structured Query Language (SQL). In addition to developing database applications, the course helps the students understand how large-scale packaged systems such as business intelligence are highly dependent on the use of database systems.
Business analytics is the application of visual, statistical, and computational models and methods systematically, for developing new business insights and for improving performance. Business analytics projects and processes are empowered by data-analytic thinking and executed through data science: Data is collected, processed, modeled, and analyzed through descriptive, predictive, and prescriptive methods. Besides providing the definition, goals, and process of business analytics, this course presents a discussion of various technical topics of analytics. Topics covered include machine learning, predictive modeling, and model evaluation. Methodological foundations are supported by case study discussions and illustrated through experiential learning, where real-world datasets are analyzed with state-of-the-art analytical modeling & analysis software.
Analytics is vital for any organization, because every organization, regardless of its size or industry, has data that can be harnessed for beneﬁt and sustainable success. In this course, a multitude of real-world cases and data are discussed. A multitude of business functions, such as marketing and finance, and industry domains are covered. The KNIME software suite is used as the analytical modeling environment.
Text analytics is used to extract meaningful information and actionable insights from the text data, to improve decision making. For example, a company can assess positive and negative trends by monitoring how customers discuss products on social media and user-generated content websites. This course aims to be a primer for text analysis, at both conceptual and practical dimensions. After completing this course with success, students gain skills to independently collect, process, and analyze text data to uncover hidden patterns. Topics discussed in the course include: capturing textual data sets, stemming text documents, duplicate detection, cleaning data sets, document clustering, text classification, sentiment analysis, and topic modeling.
What do social networks, road networks, electric networks, protein networks, food webs, and the Internet all have in common? They are all networks, i.e. graphs. A graph is a structure that represents relations between entities, where entities are shown as nodes (vertices) and their relations are shown with links (edges). Graph analytics is the application of statistical and computational techniques for the analysis of graph data, for obtaining insights into the relations between the entities and the full graph. The course introduces the various types of graphs and the metrics, methods, and software tools for analyzing them. Throughout the course, graphs from a multitude of domains will be introduced and analyzed through constructing graph visualizations and computing graph metrics with state-of-the-art software.
Optimization problems are real-world problems we encounter in many areas such as manufacturing, transportation, financial planning, and scheduling. Optimization is an analytical technique for finding the best solution from within a set of solutions or solution space. A fundamental structure in optimization is to minimize an objective function under a set of constraints. This course introduces linear programming (LP), a modeling technique for optimization problems where the objective function and constraints are all linear. Practical modeling of LP problems with spreadsheet software is illustrated through a collection of illustrations and case studies. Other topics include what-If analysis for LP, binary integer programming, and mixed integer programming.
The course helps students explore and learn about popular real-world topics using statistics as a tool. It discusses statistical application in population growth, economic developments, income distribution and environmental changes. Key statistical tools will be introduced through their applications in real world issues.
This course introduces students to decision making based on data in a business context. It covers basic concepts, sources and methods of data collection, tabular and graphic presentation of data, descriptive statistics (measures of location, dispersion, skewness and kurtosis), measures of association between variables, introduction to probability, random variables and probability distributions, sampling distributions, and statistical estimation including interval estimation.
This course introduces students to the fundamental concepts of statistics and trains them to apply the basic methods and techniques of statistical analysis in business and economics problems. It covers basic concepts, sources and methods of data collection, tabular and graphical presentation of data, descriptive statistics, introduction to probability and probability distributions, sampling distributions, statistical estimation, hypotheses testing, analysis of variance, chi-square test of independence, and correlation and regression analysis.
This course introduces the basic concepts and elementary applications of statistics that are widely utilized by psychologists. It covers data description, central tendency measures, variability indicators, and degrees of peakedness and asymmetry of data distributions. In addition, the normal distribution, standard scores, correlation and their applications in psychology and as well as hypothesis testing will be studied in this course. Statistical packages will be used throughout the course to work out psychological applications.
This course builds on the knowledge acquired in the Business Statistics I course. It introduces students to the basic methods and techniques of statistical inference and their applications in business and economics. Topics include inference involving one and two populations, analysis of variance, Chi-square tests, nonparametric tests, regression analysis, and time series analysis.
This course introduces students to events and sample space, probability, conditional probability, random variables, cumulative distribution function and probability density function, moments of random variables, common distribution functions, elementary introduction to statistics with emphasis on applications and model formulation, descriptive statistics, sampling and sampling distributions, inference, t tests, one and two factors analysis of variance, randomized complete block design, correlation and regression, and chi-square tests.
This course is an introduction to the principles and laws of probability. It gives the student a thorough understanding of the concepts of probability, conditional probability, random variables and probability distributions, moment generating functions, bivariate and marginal distribution functions, conditional distributions and expectations. While the primary focus of the course is on a mathematical development of the subject, it also includes a variety of illustrative examples and exercises that are oriented towards applications in social and physical sciences, and business.
This is an introductory course for students in biological sciences who have no formal background in statistics. It covers the basic statistical methods for describing and analyzing data arising in the biological sciences. The emphasis will be on the intuitive understanding of concepts rather than the underlying mathematical developments. Applications and data analysis will be based on the statistic package Minitab.
This course provides an introduction to exploratory data analysis through statistical programming. The data analytics process will begin with acquiring data from data sources, cleaning the data, and preparing it, through preprocessing, for statistical & computational analysis. The course will lay the foundation of fluency in handling, processing, and transforming data to a structure that enables its analysis. Topics covered in the course include the above steps of data preparation, as well as managing data frames, working with text data, exploratory graphs, visualizing clusters and distributional shapes.
This course introduces students to statistical graphics. It covers principles of graphical design, perceptual psychology, dimensionality reduction, statistical smoothing, trellis/lattice graphs, mosaic plots, 3D and dynamic graphics. Students will be trained to use appropriate statistical software libraries for graphics, reporting, and user interface.
This course introduces the basic concepts of statistical inference and their applications in psychology. It covers sampling distributions, point and interval estimation, statistical hypothesis testing, correlation, regression and prediction, analysis of variance and factorial ANOVA. Statistical packages will be used throughout the course to work out psychological applications.
The course starts by reviewing the basics of probability and counting, random variables and distributions, bivariate random variables. The course then covers the basic theories underlying statistical analysis techniques in point estimation, interval estimation, and hypothesis testing. Point estimation methods include methods of moments and maximum likelihood. It also elaborates the concepts of bias, variance, and mean-squared error of estimators. Confidence interval construction methods include likelihood-based intervals, inversion methods, intervals based on pivots, Bayesian credible and highest posterior density regions, and resampling based intervals. Various Markov Chain Monte Carlo (MCMC) computational techniques will be introduced. Hypothesis testing methods include classical and Bayesian approaches.
This course prepares students to plan and implement surveys, and to analyze survey data. Topics include survey planning and formatting, guidelines to develop questionnaires, data collection methods, various sampling methods (simple random, cluster, systematics, and multiple stages), and methods to maximize response rates and minimize survey errors. The course also covers survey weights for unequal probability sampling, non-response and post-stratification, and standard error estimation for complex samples. Appropriate practices for protecting data privacy and sensitivity in survey research will also be discussed.
This course helps students select the appropriate design for an experiment and analyze its results using statistical packages. It includes complete randomized designs, ANOVA, multiple comparisons, residual analysis, factorial experiments, ANCOVA, randomized block designs, Latin squares.
This course introduces students to regression analysis, ridge and robust regression, non-parametric regression and Lasso, and General Linear Models (GLIMs). The emphasis of the course is on practical data analysis and interpretation. Real-world examples and data are analyzed throughout the course using the statistical software R.
This course provides a foundation in statistical theory. It covers methods of estimation and properties of estimators with a focus on likelihood-based approaches, interval estimation, tests of hypotheses with a focus on likelihood ratio tests, and theory of linear models. The course illustrates the theoretical concepts and methods through the derivation of some common confidence intervals and tests for means, variances, and proportions.
This course introduces students to the principles and techniques of data mining and statistical machine learning, including artificial neural networks. It covers various statistical machine learning techniques, such as data exploration and visualization, supervised and unsupervised machine learning techniques, e.g., classification, regression, cluster analysis, principal component analysis, and ensemble methods for machine learning e.g., boosting and random forests. The course also includes the cross-validation techniques. The emphasis is on the practical implementations and the discovery of patterns and insights from data.
This course introduces students to the methodology and applications of multivariate statistical analysis. It covers multivariate analysis of variance and regression, canonical correlations, principal components, factor analysis, discrimination, classification, and cluster analysis. The emphasis is on practical implementations and applications to the various disciplines and sciences.
This course trains students in selecting and constructing appropriate time series models, estimating their parameters and forecasting with the constructed models. Topics include time series regression, classical decomposition, exponential smoothing, autocorrelation and partial autocorrelation functions, stationary and homogeneous time series; autoregressive, moving average, ARMA and ARIMA models, seasonal models, Box-Jenkins methodology and business applications.
The course develops an understanding of survey research methodologies and data collection methods from scientific and practical perspectives. It emphasizes training students on alternative sample designs used to produce statistical inferences to solve real-life problems. In addition to discussing survey methods and design, it covers: simple, stratified, systematic and cluster sampling, ratio and regression estimates, errors in sample surveys and case studies.
This course is an introduction to topics in categorical data analysis. It is an applied course emphasizing the modeling and analysis of categorical data using mainly the R statistical software. Both descriptive and inferential methods are discussed. The covered topics include measures of association, tests of goodness-of-fit, tests of independence, exact tests, logit and probit models, and discriminant analysis.
The aim of this course is to introduce students to the Bayesian statistical modeling and inference and to the related computational strategies and algorithms. The course starts with the logic behind Bayesian data analysis, including the mathematical formalization of updating beliefs under uncertainty, followed by the treatment of simple models, such as those based on normal and binomial distributions. Concepts of conjugate and non informative priors are illustrated, for single and multi-parameters models. Basic treatment of hierarchical models and linear regression models are also covered. Bayesian computational methods such as the Gibbs sampler and Metropolis-Hastings algorithms are briefly presented, with an emphasis on their implementation and use on simple cases.
The course introduces students to common computational techniques needed in statistics. It covers, in particular, data manipulation and cleaning techniques, sampling, simulation, resampling, maximum likelihood estimation and elementary Bayesian analysis. The introduced techniques are demonstrated using R software. The parallel computing aspects and cloud computing implementations are discussed with practical examples.
This course covers topics in statistics and data analytics that broaden the students understanding of statistical theory and methods, which are not covered in the other courses offered in the Bachelor of Science in Statistics and Data Analytics program.
This capstone course uses the case teaching approach to enable students to synthesize and deepen their knowledge of statistical methods and theories and data analytics techniques learned in earlier courses and apply this knowledge for business analytics. Students work individually and in groups to analyze a variety of business analytics problems, including issues posed by big data drawn from real-world problems. Covered topics include techniques for handling, cleaning, extracting, organizing, and processing real world data from business and industry, as well as data ethics and quality. Students apply their statistical modelling and computational skills to develop comprehensive solutions to data-driven problems. Through a multitude of case studies drawn from the real-world, students advance their skills, exposure, and experience in diverse applications of analytics for business.
The bridging course in statistics aims to give students with no statistical background a good knowledge of descriptive statistics and probability and probability distributions. These topics, covered in most introductory statistics courses, are a pre-requisite knowledge for the course STAT 609 (Decision Techniques and Data Analysis).
This course is dedicated to graduate students from College of Science. It introduces the students to the basic statistical procedures commonly used in the analysis of scientific and environmental problems. These statistical applications complement and reinforce scientific and environmental concepts and methods, particularly in practical, development and assessment models, and interpretation of data and results. It includes numerical and graphical description of data, techniques for significance evaluation and relationships.
The course provides a structured approach for describing, analyzing, and finalizing decisions involving uncertainty. It introduces various decision analysis techniques and principles of designing decision support systems for carrying out sensitivity analysis. It also presents key probability and statistical techniques used in modeling and analyzing business data and providing empirical evidence for action recommendation. Topics include decision analysis techniques, descriptive and inferential statistics, one-way and two-way analysis of variance, modelling using regression analysis, times series regression, exponential smoothing and forecasting.
This courses provides students with an understanding of the required steps in planning experiments; principles of experimental design; application of some designs in product development systems and evaluation factorial design; linear programming, CRD, RCD, LS, regression and correlation: and inspection of mean differences.
This course focuses on design of experiments, optimum selection of input for experiments, and the analysis of results. Full factorial as well as fractional factorial designs, response surface designs, complete randomized designs, ANOVA, multiple regression, normal probability plot, importance of analyzing interactions, signal to noise ratios, confidence intervals, and variance reduction analysis are covered in this course. Statistical analysis software such as SPSS and Minitab will be used.
This course provides students with an understanding of mathematical models for evaluating resource management strategies. It covers stochastic and deterministic simulation for optimization, System control structures and team modeling approach.
This course prepares MBA students to design and conduct research to address and solve business challenges. It provides an empirical basis for the analysis and action recommendations for the solution of business problems or for the achievement of business objectives. MBA students will learn to frame, plan, and conduct research projects as well as developing and fine-tuning forecasting models. Students will apply key statistical techniques used in modeling and analyzing research findings and business data.
This course provides students with an understanding of computer-based methods in geographical analysis. It focuses on bivariate and multivariate regression, discriminant analysis, factor analysis, and analysis of spatial and temporal data.
The course provides an in-depth study of regression and analysis of variance models. Topics include multiple regression and model building, multiple and partial correlation, analysis of residuals, analysis of variance, multivariate analysis of variance, generalized linear model, and various applications of statistical modeling. Computer software packages such as SAS, SPSS, or R will be used to carry out the data analysis. This course is designed for doctoral students to get familiar with statistical modeling for their research projects. The emphasis of the course is on the applications and fine-tuning of statistical modeling techniques.
The course provides an in-depth study of advanced statistical methods used in quantitative research. It covers important modeling and analysis tools which include Analysis of Variance (ANOVA), Analysis of Covariance (ANCOVA), multivariate analysis of variance (MANOVA), multiple regression including interactions, logistic regression, discriminant analysis, factor analysis, and structural equation modeling. The course emphasizes applications using a comprehensive statistical packages such as SPSS, MINITAB, SAS or R.
This course focuses on the design and analysis of experiments in applied sciences. It covers ANOVA, full and fractional factorial designs, blocked designs, response surface designs, robust designs, experimentation and modeling strategies, and parameter design optimization. Statistical analysis software such as Minitab and R will be used.
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