CIS 545 Assessment Answers US

The goal of this module is to provide the student with a wide variety of the most pertinent subjects that relate to modern analytical techniques and are the cornerstones of the developing area of big data analytics. The theoretical and practical distinctions between conventional datasets and big datasets are explained to learners. An overview of the initial data gathering will be investigated for various data sources. An important component of the module curriculum is a thorough foundation in analytical statistics. Learners are required to use statistical analysis concepts to solve issues and guide decisions. This is accomplished by learners when they gain knowledge and understanding of statistical analytics techniques and concepts and apply those techniques and principles to common real-world situations.

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Topic covered under the CIS 545

The important topics of this unit are briefly explained below:

• Overview of Big Data Analysis: Different types of data sources and data collection, including streaming, night before going to bed data, suitable time scales, time framerate drops, granularity, and dimensions, as well as obtaining data from a big data system.
• Experiential data analysis includes error detection, assumption verification, exploratory pattern classification that is appropriate, relationships among explanatory variables, and evaluation of the strength and direction of links between explanatory and situational factors.
• The history of statistics, their definitions, and their various applications including condensing, comparing, predicting, estimating, and testing hypotheses all play a key role in data analytics.
• Hypothesis testing includes Type I and Type II errors, the critical region, the significance level, test methodologies, and the formulation of the null and alternative hypotheses.
• Statistical analysis: goodness of fit, regression, correlation, measuring the relationship between two variables, balanced scorecard, coefficient of determination, exploratory research, and combinations
• Graphing and plotting: frequency distributions, histograms, 2D and 3D frequency distributions, graphical representations of correlation and regression data, contour plots, and 3D plots, as well as programs such as R, iPhython, and Tableau.

Learning Outcomes of CIS 545 course

After completing the course, students will be able to:

• Gather massive amounts of data from various sources.
• Critically evaluate current Big Data datasets and implementations, taking usability and practicability criteria into account.
• Recognize and demonstrate the use of statistics in the analysis of large datasets.
• For data with different structures and content, choose and use the appropriate statistical measurements and analysis methodologies, and then show summary statistics.
• Understand statistical data analytics thoroughly and be able to apply it to large data sets.
• Develop and display new facts and insights from massive datasets using sophisticated statistical analytical techniques, test hypotheses, and perform hypothesis testing.

Universities in USA offering CIS 545 Course:

The top universities in USA offering the related courses are listed below:

• Carnegie Mellon University
• Stanford University
• Georgia Institute of Technology
• University of Washington
• Columbia University
• New York University
• University of Minnesota- Twin Cities
• Northwestern University
• North Carolina State University
• Cornell University
• University of Texas, Austin
• University of Massachusetts, Amherst
• University of California, San Diego
• University of Pennsylvania
• University of Southern California
• University of Chicago
• University of Colorado, Boulder
• University of Texas, Dallas
• Rutgers university
• University of Maryland, College Park

Career after the CIS 545 Course:

Our CIS 545 assignment help experts say that after you complete the course with top-notch grades, you can easily acquire the following positions:

• Big Data Engineer
• Data Architect
• Data Security Analyst
• Database Manager
• Business Intelligence Analyst
• Data Scientist
• Data Modeler
• Database Developer
• Database Administrator
• Data Analyst

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