STAT318/462 — Data Mining Dr G ́abor Erd ́elyi University of Canterbury, Christchurch, Course developed by Dr B. Robertson. Some of the figures in this presentation are taken from “An Introduction to Statistical Learning, with applications in R” (Springer, 2013) with permission from the authors: G. James, D. Witten, T. Hastie and R. Tibshirani. G. […]
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UNIVERSITY OF WARWICK September Examinations 2021/22 Applications of Data Science Time Allowed: 2 Hours Answer ALL FOUR questions in Section A (10 marks each) and TWO questions from THREE in Section B (30 marks each). Approved pocket calculators are allowed. Copyright By PowCoder代写 加微信 powcoder Read carefully the instructions on the answer book provided and
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COMP90051 Statistical Machine Learning Project 2 Description1 (v3 updated 2021-09-19) Due date: 4:00pm Friday, 8th October 2021 Weight: 25%; forming combined hurdle with Proj1 Copyright statement: All the materials of this project—including this specification and code skeleton—are copyright of the University of Melbourne. These documents are licensed for the sole purpose of your assessment in
Coursework – EMATM0051 Large Scale Data Engineering [Data Science] Version: 12.11.2021 v2.0 Changes: 12.11.2021 v2.0 – Initial version for 2021-22 unit Summary This coursework is divided into two parts: Part 1: A written task (only) related to the knowledge gained in the AWS Academy Cloud Foundations course (weeks 1-7). Part 2: A combined practical and
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TERM ASSIGNMENT WINTER 2022 SDDS Winter – 2022 School of Software Design and Data Science Contents Copyright By PowCoder代写 加微信 powcoder Assignment #1 …………………………………………………………………………………………………………………………………. 2 Introduction …………………………………………………………………………………………………………………………………….. 2 Preparation ……………………………………………………………………………………………………………………………………… 2 Milestone – 1 (Code: weight 2.5%) …………………………………………………………………………………………………….. 2 Development Suggestions………………………………………………………………………………………………………………3 Specifications ……………………………………………………………………………………………………………………………….. 3 Core Module……………………………………………………………………………………………………………………………..3 Functions ……………………………………………………………………………………………………………………………… 4 A1-MS1: Sample Output…………………………………………………………………………………………………………………8 Reflection (Weight: 2.5%)……………………………………………………………………………………………………………..10
Where are we now? XML Template (2011) [10.8.2011–6:17pm] [1–18] K:/IVI/IVI 415994.3d (IVI) [PREPRINTER stage] Kandel et al. 3 Figure 1. The iterative process of wrangling and analysis. One or more initial data sets may be used and new versions may come later. The wrangling and analysis phases overlap. While wrangling tools tend to be separated
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What is the subject about? “DATA WRANGLING” “data” — information we can process “wrangling” — round up, herd, or take charge of What does Data Wrangling entail? Mapping Formatting Data integration Aggregation Publishing Structuring Organising Data Wrangling Data enrichment Visualisation Converting Storage Why is it important? Who is a data wrangler? Step forward the data
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Where are we now? XML Template (2011) [10.8.2011–6:17pm] [1–18] K:/IVI/IVI 415994.3d (IVI) [PREPRINTER stage] Kandel et al. 3 Figure 1. The iterative process of wrangling and analysis. One or more initial data sets may be used and new versions may come later. The wrangling and analysis phases overlap. While wrangling tools tend to be separated
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COMP3430 / COMP8430 Data wrangling Lecture 7: Data transformation, aggregation and reduction (Lecturer: ) Lecture outline Data transformation Attribute/feature construction ● – – – ● ● ● Data aggregation Data reduction Summary 2 Data transformation ● – ● – – ● – Generalisation Using concept hierarchy Normalisation Scale data to fall within a small (specified)
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COMP3430 / COMP8430 Data wrangling Lecture 2: The data wrangling process and understanding data (Lecturer: ) Lecture outline ¡ñ The data wrangling process / pipeline / tasks ¡ñ Understanding data: sources, types, and formats ¡ñ Example data wrangling tools and resources The data mining / analytics process Typically up to 90% of time and effort