The journey of Data science
The objective of this repository is presenting the journey that a Data Science project includes. Having as a main objective finding and understanding the main factors that influence on the success of a space launch.
In this path we will go through the step-by-step that a Data scientist can walk to find using different tools like Python, SQL, SciKit Learn among others. The steps of this journey can be resumed as follows:
Web Scrapping or API connection and data extraction.
Data wrangling using Pandas, Numpy.
Exploratory Data Analysis using SQL(MySQL), Data Visualization(Matplotlib).
Showcase of the process using Dash and/or Folium, creating an app that includes the EDA.
Finally there will be a machine learning excercise that presents the possibilities of understanding and predicting the influence of different factors on the success of a space shuttle launch.
All of this journey, as previously said, will embark us on the steps required to get to a machine learning model to predict how successful a space shuttle launch is, evaluating different factors like: Payload weight, type of rocket launcher, location of the launch. Finally presenting interesting infornation of how this factors influence on the success rate of the launches.
The archives are named on the order of the step-by-step requirements of the journey, where you can find the complete guide for each step.
Finally on the PDF file you can find the complete presentation that I have carefully curated to make the process more digestible and accessible. If you have time and want to learn more about my learning process, be my guest, and dive deeply into my journey! :D
https://www.twine.net/signin…The journey of Data science
The objective of this repository is presenting the journey that a Data Science project includes. Having as a main objective finding and understanding the main factors that influence on the success of a space launch.
In this path we will go through the step-by-step that a Data scientist can walk to find using different tools like Python, SQL, SciKit Learn among others. The steps of this journey can be resumed as follows:
Web Scrapping or API connection and data extraction.
Data wrangling using Pandas, Numpy.
Exploratory Data Analysis using SQL(MySQL), Data Visualization(Matplotlib).
Showcase of the process using Dash and/or Folium, creating an app that includes the EDA.
Finally there will be a machine learning excercise that presents the possibilities of understanding and predicting the influence of different factors on the success of a space shuttle launch.
All of this journey, as previously said, will embark us on the steps required to get to a machine learning model to predict how successful a space shuttle launch is, evaluating different factors like: Payload weight, type of rocket launcher, location of the launch. Finally presenting interesting infornation of how this factors influence on the success rate of the launches.
The archives are named on the order of the step-by-step requirements of the journey, where you can find the complete guide for each step.
Finally on the PDF file you can find the complete presentation that I have carefully curated to make the process more digestible and accessible. If you have time and want to learn more about my learning process, be my guest, and dive deeply into my journey! :D
https://www.twine.net/signinWWWWWWWW…