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Course 10: Applied Data Science Capstone

Spacex Falcon Heavy dual landing

What you'll learn

  • Demonstrate proficiency in data science and machine learning techniques using a real-world data set and prepare a report for stakeholders
  • Apply your skills to perform data collection, data wrangling, exploratory data analysis, data visualization model development, and model evaluation
  • Write Python code to create machine learning models including support vector machines, decision tree classifiers, and k-nearest neighbors
  • Evaluate the results of machine learning models for predictive analysis, compare their strengths and weaknesses and identify the optimal model

Skills you'll gain: Data Science, Data Analysis, CRISP-DM, Methodology, Data Mining

W1: Project Introduction

Objectives:

  • Use data science methodologies to define and formulate a real-world business problem.
  • Use your data analysis tools to load a dataset, clean it, and find out interesting insights from it

Problem/Issue to solve:

  • In this capstone, we will predict if the Falcon 9 first stage will LAND successfully.
  • SpaceX advertises Falcon 9 rocket launches on its website, with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is because SpaceX can reuse the first stage.
  • Therefore if we can determine if the first stage will land, we can determine the cost of a launch.
  • This information can be used if an alternate company wants to bid against SpaceX for a rocket launch.

Solution Steps:

  1. Course Introduction & Understanding:
# Project Scenario and Overview
# Obtain an IBM Cloud Feature Code : Ungraded External Tool
# IBM Watson Account creation and Watson Studio
# Getting started with GitHub
# Publishing Notebook to Github
# IBM Watson Setup and Project Creation
# Adding a Notebook to the Project
  1. Data Collection Overview:
# Lab: Complete the Data Collection API Lab
# Data Collection with Web Scraping
# Lab: Complete the Data Collection with Web Scraping lab 
# Quiz
  1. Data Wrangling Overview

EDA & Training Labels:

  • find patterns in the data
  • datermine the LABEL for training supervised models or the OUTCOME
- True Ocean          => means the mission outcome was successfully landed to a specific region of the ocean while 
- False Ocean         => means the mission outcome was unsuccessfully landed to a specific region of the ocean. 
- True RTLS           => means the mission outcome was successfully landed to a ground pad 
- False RTLS          => means the mission outcome was unsuccessfully landed to a ground pad.
- True ASDS           => means the mission outcome was successfully landed on a drone ship 
- False ASDS          => means the mission outcome was unsuccessfully landed on a drone ship
- None ASDS/None None => these represent a failure to land

Lab: Data Wrangling

Quiz

W2: EXploratory Data Analysis (EDA)

  • In this module, you will collect data on the Falcon 9 first-stage landings.
  • You will use a RESTful API and web scraping.
  • You will also convert the data into a dataframe and then perform some data wrangling.

Exploratory Data Analysis Overview:

Hands-on Lab: Complete the EDA with SQL
- (Optional)Hands-on Lab: Complete the EDA with SQL
- Ungraded External Tool•. Duration: 1 hour1h
- Check Points: Exploratory Analysis Using SQL
- Practice Quiz•3 questions
- Exploratory Data Analysis using SQL
- Quiz•5 questions
- •Grade: --

- Hands on Lab: Complete the EDA with Visualization lab
- (Optional)EDA with Visualization Lab
- Ungraded External Tool•. Duration: 1 hour1h
- Check Points: Complete the EDA with Visualization
- Practice Quiz•3 questions
- Exploratory Data Analysis for Data Visualization
- Quiz•3 questions
- •Grade:

Lab: Data Wrangling

Quiz

W3: Interactive and Visual Analytics and Dashboard

  • In this module, you will build a dashboard to analyze launch records interactively with Plotly Dash.

  • You will then build an interactive map to analyze the launch site proximity with Folium.

  • Interactive Visual Analytics and Dashboards:

- Hands on Lab: Complete the Data Visualization with Folium
- (Optional)Hands-on Lab: Interactive Visual Analytics with Folium lab
- Ungraded External Tool•. Duration: 1 hour1h
- Hands-on Lab: Build an Interactivce Dashboard with Ploty Dash
- Ungraded External Tool•. Duration: 1 hour1h
- Check Points: Interactive Visual Analytics and Dashboard
- Practice Quiz•7 questions
- Graded Quiz: Interactive Visual Analytics and Dashboard
- Quiz•5 questions

Lab: Data Wrangling

Quiz

W4: Predictive Analysis (Classification)

  • In this module, you will use machine learning to determine if the first stage of Falcon 9 will land successfully.
  • You will split your data into training data and test data to find the best Hyperparameter for:
    • SVM
    • Classification Trees
    • Logistic Regression
    • Then find the method that performs best using test data.

Objectives:

  • Split the data into training testing data.

  • Train different classification models.

  • Hyperparameter grid search.

  • Use your machine learning skills to build a predictive model to help a business function more efficiently.

  • Predictive Analysis Overview:

- Hands on Lab: Complete the Machine Learning Prediction lab
- (Optional)Hands-on Lab: Complete the Machine Learning Prediction lab
- Ungraded External Tool
- Check Points: Predictive Analysis
- Practice Quiz•4 questions
- Graded Quiz: Predictive Analysisis

Lab: Data Wrangling

Quiz

W5: Present Your Data Driven Insights

  • Submission Overview and Instructions

Lab: Data Wrangling

Quiz