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Lab 1: Development Environment Setup & Web Architecture Fundamentals (with fixes)

Overview

In this lab, you will set up a complete MERN (MongoDB, Express, React, Node.js) development environment and demonstrate your understanding of web architecture fundamentals. By the end of this lab, you should have multiple tools running and understand the core concepts that underpin modern web development.

MERN is a client–server architecture that uses layered architecture on the backend, middleware pipelines for cross-cutting concerns, MVC-style separation for request handling, and component-based architecture on the frontend.

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Quantum Qiskit Setup

Step 0 Download Github Desktop and Clone this or your Team Repository

Step 1 Install Anaconda

Anaconda is a Python.

Step 2 Set packages.

Create a file requirements.txt file in your working directory.

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Prompt Driven Process for Python-based Data Science using CRISP-DM Framework

RPI - 2024

Framework: https://app.myeducator.com/reader/web/1421a/2/qk5s5/

Resources: https://colab.research.google.com

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def generate_lag(dframe, by, target, periods):
    #print("Generating laged values for", target)
    for num in range(1, periods+1):
        dframe[target+'_lag'+str(num)]=dframe.groupby(by)[target].shift(num)
        dframe[target+'_lag'+str(num)].fillna(0, inplace=True)
    return dframe
    

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# This will loop through a set of data files, creating samples of each file. 


import os, sys, importlib, glob
import numpy as np
from pathlib import Path
import pandas as pd 
sys.path.append(os.path.join(Path.cwd().parent, 'modules'))
import mongoparser as mp
importlib.reload(mp) 

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Final Exam - Intoduction to Machine Learning Applications.

You are not allowed to collaborate with anyone on part 1 or 2. Other than that it is "open internet".

Part 1.

Part 1 consists of 20 multiple choice questions taken on the LMS. Once you begin you will have 1 hour to finish the exam.

Part 2

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Final Exam - Intoduction to Machine Learning Applications.

Part 2.

You are not allowed to collaborate with anyone on part 2.

This is the starter notebook.

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It is important for you to spend time understanding the mistakes from the homework.

For the midterm, please add the following code to a cell to run and debug any errors:

import os
files = "https://www.dropbox.com/s/fa7sp3r1enbk8xy/midterm.zip"
!pip install git+https://github.com/data-8/Gofer-Grader && wget $files && unzip -o midterm.zip
from client.api.notebook import Notebook

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