This repository holds all files and my codes written (excluding ones that needed to be forked from professors Github repo and modified) to complete the Data Science Specialization from Johns Hopkins University.
This readme file is intended to be a portal to each sub-folder dedicated to the 9 courses (there are 10 courses, but the first didn't need any files to be completed).
Most of these Github sub-folders contain a readme to explain what's going on, if not then the work wasn't fitted to be communicated, so there's nothing such interesting to see.
In order of appearance, linked to the data science workflow:
-
Course: Getting and Cleaning Data, n°3
- Github: Learning_ETL_EDA. Needed to be a stand-alone repo for evaluation.
- Topics: Cleaning data, features engineering -
Course: Reproducible Research, n°5
- Github: Reproducible Research
- Topics: Exploratory Data Analysis, Dataviz -
Course: Statistical Inference, n°6
- Github: Statistical Inference
- Topics: Statistics, Inference, Dataviz, Data Analysis -
Course: Regression Models, n°7
- Github: Regression Models
- Topics: Exploratory Data Analysis, Dataviz, Modeling, Regressions, Data Analysis -
Course: Practical Machine Learning, n°8
- Github: Practical Machine Learning
- Topics: Modeling, Machine Learning -
Course: Data Products, n°9
- Github: Data Products
- Project overview slides deck
- Final product web app - Topics: Cleaning data, GEOJson data, Maps, Dataviz, Web products -
Course: Data Science Capstone, n°10
- Github: Data Science Capstone
- Exploratory Data Analysis slides deck
- Algorithm and methodology project overview slides deck
- Proof of concept web app
- Topics: Exploratory Data Analysis, Natural Language Processing & Text Mining, Dataviz, Web products