Make decisions based on statistical data. Perform a significance test to verify assumptions based on measured data 6. Analyze the reliability of interconnected systems 5. Perform simple performance analysis of network or switch models 4. Perform simple inference of system parameters from measured data 3. Perform basic analysis of data using statistical methods 2. When a student completes this course, he/she should be able to: 1. HOMEWORK ASSIGNMENTS: There will be w eekly assignments midterm in Week 6 class (open book open note) one take-home final. Week 1: Data displaying distributions describing distributions density curves and normal distributions the R language for statistical computing and graphics Week 2: Relationships scatterplots correlation least-squares regression data analysis for two-way tables Week 3: Producing data random numbers random experiments basic combinatorics classical probability Week 4: Probability space conditional probability & independence Bayes rule Week 5: Random variables distributions probability mass function uniform, geometric and Poisson distributions probability density exponential and normal distributions Week 6: Midterm expectation variance joint distribution correlation law of large numbers Week 7: The sampling distribution of a sample mean sampling distributions for counts and proportions Week 8: Introduction to inference estimating with confidence tests of significance power and inference as a decision Week 9: Inference for distributions Week 10: Inference for regression. REQUIRED TEXT: Moore, McCabe & Craig, “Introduction to the Practice of Statistics”, 8th Edition, Freeman, 2014.ĬOURSE GOALS: To provide basic understanding of probabilistic and statistical methods and the knowledge of the application of such methods to the evaluation and analysis of communication systems, information technology systems and networks, as well as application to other business models based on statistical data, including inference and hypothesis testing. It includes fundamental topics as well as applications: data analysis and representation probability models conditional probability and independence reliability of systems and networks binomial, Poisson, and geometric distributions data relationships correlation inference with confidence significance tests network simulation and analysis performance analysis of systems and networks. Data Analytics Basics: Statistics + Coding + Business Thinking To be a fully featured data professional, you have to be good at all three I don’t think I have to explain why Statistics is important. The purpose of the course is to introduce the statistical methods that are critical in the performance analysis and selection of information systems and networks. Inside Our Program CollapseInside Our Program SubmenuĭescriptionsMSIT 431: Introduction to Statistics & Data Analysis VIEW ALL COURSE TIMES AND SESSIONS Description.Full-time Option CollapseFull-time Option Submenu.Part-time Option CollapsePart-time Option Submenu.Study Options CollapseStudy Options Submenu.Whether you’re learning through a degree program, professional certificate, or on your own, these are some essential skills you’ll likely need to get hired. Advisory Boards CollapseAdvisory Boards Submenu Getting a job in data analysis typically requires having a set of specific technical skills.
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