Week 9: Relational Database Service Alibaba Cloud- DSA 441 Cloud Computing

Jan 4, 20220 likes222 views

This document is a presentation on Relational Database Service (RDS) given by Dr. Ferdin Joe John Joseph from the Faculty of Information Technology at the Thai-Nichi Institute of Technology in Bangkok. The presentation introduces RDS as part of Alibaba Cloud's portfolio and provides a demonstration of its features and capabilities. It concludes by announcing that next week's topic will be on cloud security.

DSA 441 – Cloud Computing
Week 7: Relational Database Service
Asst. Prof. Dr. Ferdin Joe John Joseph
Faculty of Information Technology
Thai-Nichi Institute of Technology, Bangkok

Relational Database Service
Demonstration

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Next Week
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• Demonstration
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This document discusses a lecture on object storage services from Alibaba Cloud. It provides an overview of Alibaba Cloud's portfolio and a demonstration of the Object Storage Service (OSS). The presentation was given by Dr. Ferdin Joe John Joseph from the Faculty of Information Technology at the Thai-Nichi Institute of Technology in Bangkok. Next week's lecture will cover relational database services and include another demonstration.

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This document discusses different types of linear regression models including simple, multiple, and polynomial linear regression. It provides code examples for implementing linear regression using scikit-learn and statsmodels. Key steps covered include importing packages, providing data, creating and fitting regression models, obtaining results like coefficients and metrics, making predictions on new data, and visualizing models. Types of linear regression covered are simple linear regression with one variable, multiple linear regression with two or more variables, and polynomial regression with higher degree polynomials.

Week 8: Programming for Data AnalysisFerdin Joe John Joseph PhD

This document discusses feature engineering techniques for data analysis. It covers feature selection, construction, and engineering. Specific techniques discussed include feature imputation, handling outliers, binning, log transforms, one-hot encoding, grouping, splitting, scaling, and extracting date features. The document provides examples and explanations of these techniques to transform raw data into more useful features for machine learning models.

Programming for Data Analysis: Week 4Ferdin Joe John Joseph PhD

This document discusses permutations, combinations, and binomial coefficients. It provides examples of calculating permutations and combinations, as well as properties like Pascal's identity. It also discusses combinations with repetition and solving combinatorics problems. The document is a lesson plan on programming for data analysis that covers permutations, combinations, and a laboratory session.

Programming for Data Analysis: Week 3Ferdin Joe John Joseph PhD

This document summarizes a lesson on data analysis techniques in pandas including pivoting, binning, and replacing/renaming data. It discusses pivoting tables in pandas, binning continuous numeric data into buckets for analysis using cut and qcut, and naming the bins. Methods of equal frequency and equal width binning are also covered. The lesson concludes with an example of creating a pivot table and visualizing binning of data using cut, qcut, and named bins.

Week2: Programming for Data AnalysisFerdin Joe John Joseph PhD

This document discusses merging and concatenating data in pandas. It describes how to use the concat() function to combine data from multiple sources like CSV, JSON, and XML files into a single dataframe. The syntax of concat() is explained and examples are provided of merging different arrays and dataframes, setting axes, performing inner and outer joins, and appending rows. Methods like append() are also covered as alternatives to concat() for combining two dataframes.

Deep learning - IntroductionFerdin Joe John Joseph PhD

This document discusses deep learning and convolutional neural networks (CNNs). It defines deep learning as a type of machine learning inspired by the human brain that uses artificial neural networks to learn representations of data. CNNs are described as a type of neural network commonly used for image and video recognition. The document outlines the basic architecture of CNNs including convolutional layers, pooling layers, and fully connected layers. It also discusses common CNN architectures like LeNet-5, AlexNet, VGG16 and VGG19.

Data wrangling week 10Ferdin Joe John Joseph PhD

This document provides an overview of outlier detection techniques. It begins by defining outliers as data points that are considerably different from the majority of the data set. Several approaches to outlier detection are then discussed, including statistical-based methods using distributions and depth, deviation-based methods like sequential exceptions and OLAP data cubes, and distance-based methods using indexes and nearest neighbors. Specific algorithms are explained for each approach.

Data wrangling week 9Ferdin Joe John Joseph PhD

This document discusses data cleaning and handling dirty data. It introduces common data issues like missing values, typos, mixed formats, replicated entries, outliers, and violations of business rules. It then focuses on handling missing values and describes several techniques: listwise deletion, recovering values, educated guessing, average imputation, common-point imputation, regression substitution, and multiple imputation. The document also briefly mentions addressing typos, mixed formats, and replicated entries before assigning an activity to use pandas on CSV files to perform data cleaning.

Data Wrangling Week 7Ferdin Joe John Joseph PhD

This document summarizes a lesson on the NumPy library in Python. It introduces NumPy, which is a library for scientific computing that allows managing n-dimensional arrays. NumPy can be used for mathematical functions like Fourier transforms and linear algebra. It is also the foundation for data science in Python. The lesson demonstrates how to import NumPy, create different types of arrays from 0 to 3 dimensions, access and slice array elements, and perform operations like addition on arrays. Exercises include matrix multiplication and generating Pascal's triangle using NumPy.

Deep Learning and CNN ArchitecturesFerdin Joe John Joseph PhD

This document discusses deep learning and convolutional neural networks. It defines artificial intelligence, machine learning, and deep learning, explaining that deep learning is a type of machine learning inspired by the human brain's neural networks. The document then describes commonly used CNN layers like convolution, pooling, padding, and fully connected layers. It provides examples of classic CNN architectures, including LeNet-5, AlexNet, and VGGNet, detailing their layer configurations and explaining how they were influential early models in deep learning.

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Process Mining and Data Science in the Financial IndustryProcess mining Evangelist

Lalit Wangikar, a partner at CKM Advisors, is an experienced strategic consultant and analytics expert. He started looking for data driven ways of conducting process discovery workshops. When he read about process mining the first time around, about 2 years ago, the first feeling was: “I wish I knew of this while doing the last several projects!". Interviews are subject to all the whims human recollection is subject to: specifically, recency, simplification and self preservation. Interview-based process discovery, therefore, leaves out a lot of “outliers” that usually end up being one of the biggest opportunity area. Process mining, in contrast, provides an unbiased, fact-based, and a very comprehensive understanding of actual process execution.

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Zig Websoftware creates process management software for housing associations. Their workflow solution is used by the housing associations to, for instance, manage the process of finding and on-boarding a new tenant once the old tenant has moved out of an apartment. Paul Kooij shows how they could help their customer WoonFriesland to improve the housing allocation process by analyzing the data from Zig's platform. Every day that a rental property is vacant costs the housing association money. But why does it take so long to find new tenants? For WoonFriesland this was a black box. Paul explains how he used process mining to uncover hidden opportunities to reduce the vacancy time by 4,000 days within just the first six months.

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1. DSA 441 – Cloud Computing Week 7: Relational Database Service Asst. Prof. Dr. Ferdin Joe John Joseph Faculty of Information Technology Thai-Nichi Institute of Technology, Bangkok

2. Relational Database Service Demonstration

3. Alibaba Cloud Portfolio

4. Relational Database Service (RDS)

5. Faculty of Information Technology, Thai-Nichi Institute of Technology 5

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28. Faculty of Information Technology, Thai-Nichi Institute of Technology 28

29. Demonstration Faculty of Information Technology, Thai-Nichi Institute of Technology 29

30. Next Week • Cloud Security • Demonstration Faculty of Information Technology, Thai-Nichi Institute of Technology 30

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