Building context and general idea

README.md

@@ -2,27 +2,35 @@
 
 ## Context
 
-This data analysis and visualition project was triggered by a [data-upskilling](https://benjamin-dubreu.systeme.io/programme-data-upskilling) training course I recently followed to acquaint myself with tools commonly used by data scientists. With this project, I used and combined all the different concepts I learned in the class.
+This data analysis and visualition project was triggered by a [data-upskilling](https://benjamin-dubreu.systeme.io/programme-data-upskilling) training course I recently followed to acquaint myself with tools commonly used by data scientists. With this project, I used and combined all the different concepts I learned in the class. The work was divided in four different routes:
 
+    1. Creating an API that generates data(*).
+    2. Querying that interface to extract the data required by the project.
+    3. Processing the data to address the client's needs.
+    4. Visualising the data in an online dynamic environment.
 
-**Note**: This project is not about the data illustrated in the final streamlit app. It is all about showcasing my capacity at creating a robust infrasture and data processing pipeline.
+(*) In a real life situation, this step would be skipped as the client would provide the data.
+
+**Skills involved**: Python, SQL, Bash, Git, Airflow, Object-oriented programming, Unit testing, API development, Data extraction, Data analysis, Data visualisation, streamlit cloud
+
+**Languages and packages used**: fastapi, pandas, datetime, numpy.random, unittest, uvicorn, requests, duckdb, streamlit, venv
 
 
 
 ## The general idea
 
-==test==
+The client owns several stores in major European cities that he/she opened in the last decade. The client needs to know how many people frequent the different stores and detect long-term trends. The data available are sensor data that count how many people enter a store per hour. A store can have several doors, and thefore, several streams of sensor data. With his/her initial request, the client wants an interface where he/she can easily navigate the sensor data from the different stores. In particular, he/she wants the data at four different resolutions:
+
+    - Hourly data at the sensor level.
+    - Hourly data at the store level.
+    - Daily data at the sensor level.
+    - Daily data at the store level.
+
 
 
+**Note**: This project is primarily about showcasing my capacity at creating a robust infrasture and data processing pipeline, and not so much about the data illustrated in the final app.
 
-This project will follow five different routes:
-1. Creating an API that generates data
-2. A python script to collect data
-3. A python script combining SQL techniques to transform the data.
-4. A visualisation App with streamlit
-5. An Airflow DAG to automatise the data update.
 
-Let's get started.
 
 ## The details