This project is the implementation template for HW 0 and HW 1 for both the programming and non-programming tracks

Overview

S22-W4111-HW-1-0:
W4111 - Intro to Databases HW0 and HW1

Introduction

This project is the implementation template for HW 0 and HW 1 for both the programming and non-programming tracks.

HW 0 - All Students

You have completed the first step, which is cloning the project template.

Note: You are Columbia students. You should be able to install SW and follow instructions.

MySQL:

  • Download the installation files for MySQL Community Server..

    • Make sure you download for the correct operating system.
    • If you are on Mac make sure you choose the correct architecture. ARM is for Apple silicon. x86 is for other Apple systems.
    • On Windows, you can download and use the MSI.
  • Follow the installation instructions for MySQL. There are official instructions and many online tutorials.

  • Remember your root user ID and password, that you set during installation. Also, choose "Legacy Authentication" when prompted.

    • If you forget your root user or password, you are on your own. The TAs and I will not fix any problems due to forgetting the information.
    • Also, if you say something like, "It did not prompt me for a user ID and password when I instaled ... ..," we will laugh. We will say something like, ""Sure. 20 million MySQL installations asked for the information, but it decide to not to ask you."
    • If you tell us that you are sure that you are entering the correct user ID and password we will laugh. We will say something like, "Which is more likely. That a DATABASE forgot something or" you did?"
  • You only need to install the server. All other SW packages are optional.

Anaconda:

  • I strongly recommend uninstalling any existing version of Anaconda. If you choose not to uninstall previous versions, you may hit issues. You are on your own if you hit issues due to conflicting versions of Anaconda during the semester.

  • Download the most recent version of Ananconda..

  • Follow the installation instructions. Choose "Install for me" when prompted. If you hit a problem and I find your Anaconda installation in the wrong directory, you are on your own. If you say something like, "But, it did not give me that option," you can guess what will happen.

DataGrip:

  • Download DataGrip. Make sure you choose the correct OS and silicon.

  • Follow the installation instructions.

  • Apply for a student license.

  • When you receive confirmation of your student license, set the license information in DataGrip.

HW0: Non-Programming

Step 1: Initial Files

  1. Create a folder in the project of the form _src, where is your UNI I created an example, which is dff9_src.

  2. Create a file in the directory _HW0.

  3. Copy the Jupyter notebook file from dff9_src/dff9_HW0.ipynb into the directory you created and replace dff9 with your UNI.

  4. Do the same for dff9_HW0.py

Step 2: Jupter Notebook

  • Start Anaconda.

  • Open Jupyter Notebook in Anaconda.

  • Navigate to the directory where you cloned the repository, and then go into the folder you created.

  • Open the notebook (the file ending in .ipynb).

  • The remaining steps in HW0: Non-Programming are in the notebook that you opened.

HW 0: Programming

  • Complete the steps for HW0: Non-Programming.

  • The programming track is not "harder" than non-programming. The initial set up is a little more work, however.

  • Download and install PyCharm. Download and install the professional edition.

  • Follow the instructions to set the license key using the JetBrains account you used to get the DataGrip licenses.

  • Start PyCharm, navigate to and open the project that you cloned from GitHub.

  • Follow the instructions for creating a new virtual Conda environment for the project.

  • Select the root folder in the project, right click and add a new Python Package named _web_src. My example is dff9_web_src.

  • Copy the files from dff9_web_src into the package you created.

  • Follow the instructions for adding a package to your virtual environment. You should add the package flask.

  • Right click on your file application.py that you copied and select run. You will see a console window open and this will show a URL. Copy on the URL.

  • Open a browser. Paste the URL and append '/health'. My URL looks like http://172.20.1.14:5000/health. Yours may be a little different.

  • Hit enter. You should see a health message. Take a screenshot of the browser window and add the file to the directory. My example is ""

Owner
Donald F. Ferguson
Senior Technical Fellow, Chief SW Architect, Ansys, Inc. Adjunct Professor, Dept. of Computer Science, Columbia University. CTO and Co-Founder, Seeka.TV
Donald F. Ferguson
Bigdata Simulation Library Of Dream By Sandman Books

BIGDATA SIMULATION LIBRARY OF DREAM BY SANDMAN BOOKS ================= Solution Architecture Description In the realm of Dreaming, its ruler SANDMAN,

Maycon Cypriano 3 Jun 30, 2022
This is a python script to navigate and extract the FSD50K dataset

FSD50K navigator This is a script I use to navigate the sound dataset from FSK50K.

sweemeng 2 Nov 23, 2021
Using approximate bayesian posteriors in deep nets for active learning

Bayesian Active Learning (BaaL) BaaL is an active learning library developed at ElementAI. This repository contains techniques and reusable components

ElementAI 687 Dec 25, 2022
Describing statistical models in Python using symbolic formulas

Patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design mat

Python for Data 866 Dec 16, 2022
Statistical & Probabilistic Analysis of Store Sales, University Survey, & Manufacturing data

Statistical_Modelling Statistical & Probabilistic Analysis of Store Sales, University Survey, & Manufacturing data Statistical Methods for Decision Ma

Avnika Mehta 1 Jan 27, 2022
Manage large and heterogeneous data spaces on the file system.

signac - simple data management The signac framework helps users manage and scale file-based workflows, facilitating data reuse, sharing, and reproduc

Glotzer Group 109 Dec 14, 2022
Create HTML profiling reports from pandas DataFrame objects

Pandas Profiling Documentation | Slack | Stack Overflow Generates profile reports from a pandas DataFrame. The pandas df.describe() function is great

10k Jan 01, 2023
MidTerm Project for the Data Analysis FT Bootcamp, Adam Tycner and Florent ZAHOUI

MidTerm Project for the Data Analysis FT Bootcamp, Adam Tycner and Florent ZAHOUI Hallo

Florent Zahoui 1 Feb 07, 2022
Implementation in Python of the reliability measures such as Omega.

OmegaPy Summary Simple implementation in Python of the reliability measures: Omega Total, Omega Hierarchical and Omega Hierarchical Total. Name Link O

Rafael Valero Fernández 2 Apr 27, 2022
Flenser is a simple, minimal, automated exploratory data analysis tool.

Flenser Have you ever been handed a dataset you've never seen before? Flenser is a simple, minimal, automated exploratory data analysis tool. It runs

John McCambridge 79 Sep 20, 2022
A lightweight, hub-and-spoke dashboard for multi-account Data Science projects

A lightweight, hub-and-spoke dashboard for cross-account Data Science Projects Introduction Modern Data Science environments often involve many indepe

AWS Samples 3 Oct 30, 2021
apricot implements submodular optimization for the purpose of selecting subsets of massive data sets to train machine learning models quickly.

Please consider citing the manuscript if you use apricot in your academic work! You can find more thorough documentation here. apricot implements subm

Jacob Schreiber 457 Dec 20, 2022
AWS Glue ETL Code Samples

AWS Glue ETL Code Samples This repository has samples that demonstrate various aspects of the new AWS Glue service, as well as various AWS Glue utilit

AWS Samples 1.2k Jan 03, 2023
Fast, flexible and easy to use probabilistic modelling in Python.

Please consider citing the JMLR-MLOSS Manuscript if you've used pomegranate in your academic work! pomegranate is a package for building probabilistic

Jacob Schreiber 3k Jan 02, 2023
This cosmetics generator allows you to generate the new Fortnite cosmetics, Search pak and search cosmetics!

COSMETICS GENERATOR This cosmetics generator allows you to generate the new Fortnite cosmetics, Search pak and search cosmetics! Remember to put the l

ᴅᴊʟᴏʀ3xᴢᴏ 11 Dec 13, 2022
songplays datamart provide details about the musical taste of our customers and can help us to improve our recomendation system

Songplays User activity datamart The following document describes the model used to build the songplays datamart table and the respective ETL process.

Leandro Kellermann de Oliveira 1 Jul 13, 2021
Datashredder is a simple data corruption engine written in python. You can corrupt anything text, images and video.

Datashredder is a simple data corruption engine written in python. You can corrupt anything text, images and video. You can chose the cha

2 Jul 22, 2022
Nobel Data Analysis

Nobel_Data_Analysis This project is for analyzing a set of data about people who have won the Nobel Prize in different fields and different countries

Mohammed Hassan El Sayed 1 Jan 24, 2022