In [0]: import numpy as np import pandas as pd import matplotlib.pyplot as plt plt.style.use(‘seaborn’) import scipy.stats as ss Instructions Group Project¶ • Make groups of 4 or 5 students (maximum of 2 students of the same nationality in each group) • Use a publicly available real-world data set from the web: ▪ kaggle.com/datasets ▪ Other […]
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PowerPoint Presentation SQL I R & G Chapter 5 1 SQL Roots Developed @IBM Research in the 1970s System R project Vs. Berkeley’s Quel language (Ingres project) Commercialized/Popularized in the 1980s “Intergalactic Dataspeak” IBM beaten to market by a startup called Oracle Slide Deck Title SQL’s Persistence Over 40 years old! Questioned repeatedly 90’s: Object-Oriented
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Quantitative Platial Analysis: methods for handling and representing platial heterogeneity and linking varying concepts of place Part 1: Machine learning / data mining, inference vs prediction GEOG5917 Big Data and Consumer Analytics Lex Comber Professor of Spatial Data Analytics School of Geography University of Leeds a.comber@leeds.ac.uk Pre-amble Last week Introduced models and modelling Regression models
Jupyter Notebooks¶ Jupyter is a nod to 3 languages: Julia, Python, and R. Source @jakevdp. This document that you’re currently reading is a “Jupyter Notebook”. It’s like a text document, but you can run code on it! It can also display inline graphs, In [1]: from utils import plot_sine %matplotlib inline plot_sine()  Pull data from
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Mining Frequent Patterns Without Candidate Generation * MD-MIS 637-Fall 2020 * Deriving Rules From Data Deriving Rules from Data Data Analytics & Machine Learning Algorithms Recursive Partitioning: C4.5 and CART Algorithms Overview MD-MIS 637-Fall 2020 * MD-MIS 637-Fall 2020 * Deriving Rules From Data Machine Learning Algorithms (ML): derive rules from the data, create rules
PowerPoint Presentation Revision Topics 5-12 1 Week 5 – Risk Management and Assessment WHAT YOU NEED TO KNOW Understand the importance of the two models The COSO Risk Management ERM (Quantifies Risk) AS/NZS ISO 31000:2009 (Qualitative Approach) 2 COSO Enterprise Risk Management (ERM) 3 Understand what each of the 8 aspects of the model involves
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PowerPoint Presentation Parallel Query Processing R&G Chapters 22.1-22.4, A little history Relational revolution declarative set-oriented primitives 1970’s Parallel relational database systems on commodity hardware 1980’s Big Data: MapReduce, Spark, etc. scaling to thousands of machines and beyond 2005-2015 2 Why Parallelism? Scan 100TB At 0.5 GB/sec (see lec 4): ~200,000 sec = ~2.31 days Why
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MET CS 689 B1 Designing and Implementing a Data Warehouse Mary E Letourneau MET CS 689 B1 Designing and Implementing a Data Warehouse Mary E Letourneau Dimensional Data Modeling March 25, 2020 1 Module 2 Dimensional Data Modeling Modeling Fundamentals Dimensions and Facts Schemas Slowly Changing Dimensions Time and Bitemporality Including Big Data 2 MET
PowerPoint Presentation Distributed Transactions with Two-Phase Commit R&G – Chapter 20 Distributed vs. Parallel? Earlier we discussed Parallel DBMSs Shared-memory Shared-disk Shared-nothing Distributed is basically shared-nothing parallel Perhaps with a slower network What’s Special About Distributed Computing? Parallel computation No shared memory/disk Unreliable Networks Delay, reordering, loss of packets Unsynchronized clocks Impossible to have perfect
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FIT5148 – Big data management and processing¶ Activity: Parallel Join¶ In this activity, we will learn and build different parallel algorithms for join queries. This practice will help you understand how parallel processing of a join operation can significantly improve the serial join operation which is considered to be one of the most expensive operations
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