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
Incubator for useful bioinformatics code, primarily in Python and R

Collection of useful code related to biological analysis. Much of this is discussed with examples at Blue collar bioinformatics. All code, images and

Brad Chapman 560 Jan 03, 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
๐Ÿงช Panel-Chemistry - exploratory data analysis and build powerful data and viz tools within the domain of Chemistry using Python and HoloViz Panel.

๐Ÿงช๐Ÿ“ˆ ๐Ÿ. The purpose of the panel-chemistry project is to make it really easy for you to do DATA ANALYSIS and build powerful DATA AND VIZ APPLICATIONS within the domain of Chemistry using using Python a

Marc Skov Madsen 97 Dec 08, 2022
This repository contains some analysis of possible nerdle answers

Nerdle Analysis https://nerdlegame.com/ This repository contains some analysis of possible nerdle answers. Here's a quick overview: nerdle.py contains

0 Dec 16, 2022
TextDescriptives - A Python library for calculating a large variety of statistics from text

A Python library for calculating a large variety of statistics from text(s) using spaCy v.3 pipeline components and extensions. TextDescriptives can be used to calculate several descriptive statistic

150 Dec 30, 2022
This repo contains a simple but effective tool made using python which can be used for quality control in statistical approach.

This repo contains a powerful tool made using python which is used to visualize, analyse and finally assess the quality of the product depending upon the given observations

SasiVatsal 8 Oct 18, 2022
Data Intelligence Applications - Online Product Advertising and Pricing with Context Generation

Data Intelligence Applications - Online Product Advertising and Pricing with Context Generation Overview Consider the scenario in which advertisement

Manuel Bressan 2 Nov 18, 2021
MeSH2Matrix - A set of Python codes for the generation of biomedical ontologies from the MeSH keywords of the PubMed scholarly publications

A set of Python codes for the generation of biomedical ontologies from the MeSH keywords of the PubMed scholarly publications

SisonkeBiotik 6 Nov 30, 2022
A neural-based binary analysis tool

A neural-based binary analysis tool Introduction This directory contains the demo of a neural-based binary analysis tool. We test the framework using

Facebook Research 208 Dec 22, 2022
pyhsmm MITpyhsmm - Bayesian inference in HSMMs and HMMs. MIT

Bayesian inference in HSMMs and HMMs This is a Python library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and expli

Matthew Johnson 527 Dec 04, 2022
Yet Another Workflow Parser for SecurityHub

YAWPS Yet Another Workflow Parser for SecurityHub "Screaming pepper" by Rum Bucolic Ape is licensed with CC BY-ND 2.0. To view a copy of this license,

myoung34 8 Dec 22, 2022
Sensitivity Analysis Library in Python (Numpy). Contains Sobol, Morris, Fractional Factorial and FAST methods.

Sensitivity Analysis Library (SALib) Python implementations of commonly used sensitivity analysis methods. Useful in systems modeling to calculate the

SALib 663 Jan 05, 2023
Two phase pipeline + StreamlitTwo phase pipeline + Streamlit

Two phase pipeline + Streamlit This is an example project that demonstrates how to create a pipeline that consists of two phases of execution. In betw

Rick Lamers 1 Nov 17, 2021
DataPrep โ€” The easiest way to prepare data in Python

DataPrep โ€” The easiest way to prepare data in Python

SFU Database Group 1.5k Dec 27, 2022
Using Data Science with Machine Learning techniques (ETL pipeline and ML pipeline) to classify received messages after disasters.

Using Data Science with Machine Learning techniques (ETL pipeline and ML pipeline) to classify received messages after disasters.

1 Feb 11, 2022
Python scripts aim to use a Random Forest machine learning algorithm to predict the water affinity of Metal-Organic Frameworks

The following Python scripts aim to use a Random Forest machine learning algorithm to predict the water affinity of Metal-Organic Frameworks (MOFs). The training set is extracted from the Cambridge S

1 Jan 09, 2022
Full automated data pipeline using docker images

Create postgres tables from CSV files This first section is only relate to creating tables from CSV files using postgres container alone. Just one of

1 Nov 21, 2021
General Assembly's 2015 Data Science course in Washington, DC

DAT8 Course Repository Course materials for General Assembly's Data Science course in Washington, DC (8/18/15 - 10/29/15). Instructor: Kevin Markham (

Kevin Markham 1.6k Jan 07, 2023
Spectacular AI SDK fuses data from cameras and IMU sensors and outputs an accurate 6-degree-of-freedom pose of a device.

Spectacular AI SDK examples Spectacular AI SDK fuses data from cameras and IMU sensors (accelerometer and gyroscope) and outputs an accurate 6-degree-

Spectacular AI 94 Jan 04, 2023
Get mutations in cluster by querying from LAPIS API

Cluster Mutation Script Get mutations appearing within user-defined clusters. Usage Clusters are defined in the clusters dict in main.py: clusters = {

neherlab 1 Oct 22, 2021