Introduction to information system Logistic Regression Bowei Chen, Deema Hafeth and Jingmin Huang School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science 2016 – 2017 Workshop Today’s Objectives • Study the slides in Part I, including: – Implementation of logistic regression in R – Interpretation of results of logistic regression in R • Do […]
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CMP3036M Data Science Page 1 of 3 University of Lincoln School of Computer Science 2016 – 2017 Assessment Item 1 of 2 Briefing Document Title: CMP3036M Data Science Indicative Weighting: 50% Learning Outcomes On successful completion of this component a student will have demonstrated competence in the following areas: x LO1 Critically apply fundamental concepts
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Introduction to information system Handling and Processing Strings/Texts in R Bowei Chen, Deema Hafeth and Jingmin Huang School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science 2016 – 2017 Workshop Today’s Objectives • Study the following slides: – Regular expression functions • Do the exercises 1-4 • Do the additional exercise 1-2 References •
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Introduction to information system Exploratory Graphs and Base Plotting System in R Bowei Chen School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science 2016 – 2017 Workshop Today’s Objectives • Study the following slides: – Part I: Exploratory Graphs (If you are familiar with statistical graphical representations, please skip Part I and jump to
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Microsoft Learning Experiences CMP3036M/CMP9063M Data Science 2016 – 2017 Semester B Week 03 Workshop Energy Efficiency Prediction This lab is based on the Microsoft course material for MS Azure, more specifically Lab3A, Lab3B and Lab3C. In this exercise, you will use a dataset of metrics for buildings. Specifically, you will try to identify data fields
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Introduction to information system Data I/O in R Bowei Chen School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science 2016 – 2017 Workshop Today’s Objectives • Study the following slides for Data I/O in R • Do the exercises 1-8 (exercise 5-8 are important!!!) • Do the additional exercises (which can help you understand
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CMS052 Abstract Data Types & Dynamic Data Structures Assessment 1 School of Computer Science CMP3036M Data Science Page 1 of 2 CMP3036M Data Science Assessment 2 of 2 Criterion Grid 2016 – 2017 Learning Outcome Criterion Pass 2:2 2:1 1st LO1 Critically apply fundamental concepts and techniques in data science. LO2 Utilise state- of-the-art tools
trees_workshop(2) Data Science Workshop: Week 7¶ This week we will learn something about trees and forests for regression and classification. We will start with regression trees. Please download all files from blackboard before starting the notebook. Also, execute each code cell in the correct order. Please read over the whole notebook. It contains several excercises
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CS6907-13 Big Data and Analytics CS6907-13 Big Data and Analytics CS3907-80/CS444-10 Big Data and Analytics Class project #3 Text Analytics in R 1. Data Set: acq The problem is to process a large set of documents (50) to understand how text analytics works. The function acq in the R package tm references a corpus of
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Introduction to information system Inference Bowei Chen School of Computer Science University of Lincoln CMP3036M/CMP9063M Data Science • Population and Sample – Basic Concepts – Sample Mean, Median, Mode • Sampling Distribution – Central Limit Theorem (CLT) – Z-Score • Unbiased Estimator • Several Topics For Your Direct Study – Chi-Square Distribution – t Distribution
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