Data Analytics on user behaviour to improve member engagement

We are exploring the amazing world of Data Analytics, in this repository we can find a quick beginner friendly guide to R studio and R language. In the R notebook you will find a detailed process to begin with the R programming and RStudio environment, to understand the use of R in Data Analytics, we will start the journey with the open data provided by Google on an hypothetic company called Cyclistic, that has their user data base that include:

User IDs
Type of bycicles provided by the company
Dates of begining and end of travels
Locations of this travels
And the type of users

With this information we will walk through the steps of DATA ANALYSIS:

ASK
PREPARE
PROCESS
ANALYZE
SHARE
ACT

Using the R environment with the final objective to come up with a marketing strategy to engage the casual users to become an anual member depending on the findings of the Data Analysis.

So then… if you wish, embark with me on this journey of data analysis and learning a new programming language. :D
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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

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