Data Science for Chemists

IPL Summer School, CPE Lyon

Introduction

John Samuel
CPE Lyon

Year: 2024-2025
Email: john.samuel@cpe.fr

Associate Professor (Enseignant-Chercheur), Conception Logicielle et Big Data, CPE Lyon
Research Interests and Themes: Knowledge representation, semantic web, web services, data integration, data warehousing, distributed systems, geographic information systems
Cours : Programming in C, Algorithms in C, Data Mining and Machine Learning, Artificial Intelligence and Deep Learning, Operating Systems and Concurrent Programming, Web Languages
Ph.D Thesis : Integration of data from web services

Goals

Introduction to Data Science
Fundamentals of Programming
Data Analysis and visualization
Data Mining
Machine Learning
Big Data

Course Structure:

Classes: 8h
Programming sessions: 10h

Course:

Operating system: Linux
Programming Language: Python
Editor: Jupyter

Class:

Interactive
Questions: Every 20-30 mins
Written final Exam: 60% to grades

Programming Session:

Grades: 40%
4 exercises
Online submission

Class	Dates
Class 1	17^th June
Class 2	20^th June
Class 3	24^th June
Class 4	27^th June
Class 5	1^st July
Class 6	4^th July

Programming Session	Dates
Session 1	17^th June
Session 2	20^th June
Session 3	24^th June
Session 4	27^th June
Session 5	1^st July
Session 6	4^th July

Written Exam: 60%

Exam: Online on E-campus
Duration: 2 heures
Total: 20 points
Open book Exam
Types of allowed documents: All documents (books, notes, etc.) allowed
Calculating devices : not allowed
Internet usage : not allowed
Additional submission link : Available for personal files

You will receive a detailed email before the exam.

Evaluation of practical works

The practical works will be evaluated.
The submission deadline is specified on e-campus.

Programming Sessions

In-depth exploration of Jupyter, an interactive platform widely used for data analysis. Creation of interactive notebooks, integration of code and visualizations for interactive data analysis.
Practical experience with open datasets, providing a concrete understanding of issues related to data processing. Application of advanced techniques to extract meaningful insights from heterogeneous data.

Programming Sessions

Each lab session consists of several exercises. Each exercise is accompanied by a difficulty level indication :

★: Easy
★★: Medium
★★★: Difficult

Checklist

Before submitting your project, please ensure that you adhere to the following checklist:

✅ Full names (first and last names) of the team are correctly included in the file CONTRIBUTORS.md.
✅ The README.md file is filled out comprehensively and adheres to the provided instructions.
✅ You have adhered to file names as specified in each lab exercise.
✅ Your code is accompanied by appropriate comments to explain logic and functionality.
✅ Your code can be executed without generating errors, and ideally without generating warnings.
⚠ No Python files (*.py)

Programming Sessions

You can check https://github.com/johnsamuelwrites/SummerSchool-DS4C online or clone it to your machine using the terminal with the following commands:


                	            $ git clone https://github.com/johnsamuelwrites/SummerSchool-DS4C

           $ cd SummerSchool-DS4C ; ls

And for the latest modifications:


                	            $ git pull

Programming Sessions: Jupyter Notebooks

When creating a new notebook (tab "New"), you will have the option to choose from several kernels.
Make sure to select the "Python 3" or "Python 3+" kernel for this lab session.

Programming Sessions: Jupyter Notebooks

Structured: Divide the code into cells, making it easier to understand, modify, and debug step by step.
Interactivity: Execute Python code directly in the notebook, enabling interactive exploration of data.
Advantages - Comprehensive Communication: Combine explanations, visualizations, and code in a single document, facilitating understanding and sharing with others.

Programming Sessions: Jupyter Notebooks

Explore the use of the scikit-learn library for implementing machine learning tasks using built-in datasets.

Programming Sessions: Jupyter Notebooks and Visualization

Programming Sessions: Jupyter Notebooks and Visualization

Programming Sessions: Wikidata (Open Data)

Data Science for Chemists

IPL Summer School, CPE Lyon

Introduction

John SAMUEL

Data Science for Chemists

Goals

Data Science for Chemists

Course Structure:

Programming Environment

Course:

Class:

Programming Session:

Data Science for Chemists

Data Science for Chemists

Data Science for Chemists

Written Exam: 60%

Data Science for Chemists

Evaluation of practical works

Data Science for Chemists

Programming Sessions

Data Science for Chemists

Programming Sessions

Data Science for Chemists

Checklist

Data Science for Chemists

Programming Sessions

Data Science for Chemists

Programming Sessions: Jupyter Notebooks

Data Science for Chemists

Programming Sessions: Jupyter Notebooks

Data Science for Chemists

Programming Sessions: Jupyter Notebooks

Data Science for Chemists

Programming Sessions: Jupyter Notebooks and Visualization

Data Mining: Notebooks Jupyter

Programming Sessions: Jupyter Notebooks and Visualization

Data Science for Chemists

Programming Sessions: Wikidata (Open Data)

References

Websites

Colours

Images