Data Science for Chemists

IPL Summer School, CPE Lyon

1. Introduction to Data Science

John Samuel
CPE Lyon

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

Goals: Introduction to Data Science

• History of Data Science and computing
• Computer Architecture and Systems
• Major phases of data analysis
• Algorithms for data acquisition and process control
• Applications: sustainable cities, energy transitions

Numeration system

Numeration System

• Used to represent and manipulate quantities.
• Contributed to the collection and organization of data.

Examples of Ancient Systems

• Babylonian System: Base 60, used for astronomical calculations.
• Maya Numeration: Base 20, with a system of dots and bars.
• Roman System: Alphanumeric symbols to represent quantities.

Numeration System

Applications

• Historical Recording: Use of these systems to record important data.
• Astronomical Calculations: Adaptation for specific needs like astronomical calculations.

Transition to Modern Systems

• Numerical Evolution: Transition to binary and decimal systems.
• Cultural Heritage: Lasting impact on modern numeration.

Typewriter

• Invention: Developed in the 19th century for typing and documentation.
• Manual Processing: Limitations related to speed and capacity.

Electronic Typewriter

• Digital Transition: Integration of electronic components into typewriters.
• Increased Speed and Efficiency: Improvements in information processing.

Partial Automation: Reduction of manual tasks in data entry.

Blaise Pascal's Calculating Machine

• 17th Century Invention: Six-digit mechanical calculator.
• Scientific Use: Contribution to solving complex mathematical problems.

Charles Babbage's Difference Engine

• 19th Century Design: Mechanical machine for automating calculations.
• Precursor to Computers: Influence on the development of modern computers.

Automation of Calculations: Reduction in the time required to perform complex calculations.

Scientific Advances: Facilitation of scientific research through more efficient calculation tools.

ENIAC (1947-1955)

• Pioneer of the Computer Age: First large-scale electronic computer.
• Complex Calculations: Used for scientific and military calculations.

IBM PC 5150 (1983)

• Era of Personal Computers: Launch of the first IBM PC accessible to the general public.
• Computer Revolution: Popularization of home computing and software.

Democratization of Computing: Transition to widespread accessibility and use of computers.

Predecessors of Current Technologies: Foundation of modern computer systems.

Floppy Disks (8-inch, 5.25-inch, and 3.5-inch)

• Development: Introduction of different floppy disk sizes in the 1970s and 1980s.
• Removable Storage: Convenient storage medium for data transfer.

Hard Disk Drive

• Development: Introduction of the first hard disk drives in the 1950s.
• Permanent Storage: Use of magnetic disks to permanently store data.
• Increasing Capacity: Evolution towards hard drives offering increasingly larger storage capacities.

Portable and Massive Storage: Floppy disks for portable data and massive permanent storage.

Growth of Storage Capacities

• Early Computing: Modest storage capacities, often measured in kilobytes.
• 2000s: Significant expansion with the advent of multi-gigabyte hard drives.
• Contemporary Era: Terabytes and petabytes become the norm.

Emerging Storage Technologies

• Flash Storage: Introduction of flash memory for fast performance.
• Cloud Storage: Use of cloud services for virtually infinite capacity.
• Future Innovations: Anticipation of new advances in quantum storage, etc.

Systems

Distributed Computing

The following projects have utilized the processing power of personal computers for various purposes:

• Genome@home: Studying genomes and proteins.
• Folding@home: Simulating protein folding in various temperature and pressure configurations.
• SETI@home: Searching for non-terrestrial intelligent life.
• LHC@Home: Simulating particle collisions in the LHC particle accelerator.

Lifecycle of Data

1. Data
2. Knowledge
3. Insights
4. Actions

From Data to Knowledge

1. Data acquisition
2. Data Extraction
3. Data Cleaning
4. Data Transformation
5. Data analysis modeling
6. Data Storage
7. Analysis
8. Visualisation

Data Acquistion

ETL (Extraction Transformation and Loading)

1. Data Extraction
2. Data Cleaning
3. Data Transformation
4. Loading data to information stores

Data Analysis

Data Visualization

Data Visualization

Sampling Techniques

• Time-Based Sampling: Regular collection of data at fixed intervals.
• Event-Based Sampling: Triggered by specific events, such as a reaction reaching a certain stage.
• Batch Sampling: Collecting data at the end of predefined stages in a process.

Signal Processing Techniques

• Spectral Analysis: Analyzing signals in the frequency domain to detect and interpret chemical spectra.

Chemometric Methods

• Principal Component Analysis (PCA): Dimensionality reduction technique for analyzing chemical data and identifying patterns.
• Partial Least Squares (PLS): Regression technique used when predicting one set of variables from another, particularly in spectroscopic analysis.

Data Fusion and Integration

• Multivariate Data Fusion: Integrating data from multiple sources (e.g., spectroscopic, chromatographic) to enhance understanding of complex chemical systems.
• Feature Selection: Identifying relevant features from complex datasets to focus on key variables influencing chemical processes.

Chemical Reaction Control

• Proportional-Integral-Derivative (PID) Control: Adjusting process parameters based on real-time feedback to maintain optimal reaction conditions. It aims to maintain a desired setpoint (target value) by continuously adjusting a control variable (such as temperature, pressure, flow rate) based on the difference between the setpoint and the measured process variable.
• Advanced Process Control (APC): Using models to predict reaction behavior and optimize control strategies. It encompasses a range of sophisticated algorithms and methodologies that leverage mathematical models, real-time data, and optimization techniques to enhance process efficiency, stability, and product quality.

Quality Control and Monitoring

• Statistical Process Control (SPC): Monitoring and controlling chemical processes to ensure they operate within specified limits.
• Real-Time Monitoring Algorithms: Detecting deviations from expected process behavior and triggering corrective actions.

Modeling and Simulation

• Reaction Kinetics Modeling: Developing mathematical models to describe the rates of chemical reactions.
• Process Simulation: Using software tools to simulate chemical processes and optimize parameters before implementation.

Machine Learning

• Predictive Modeling: Using machine learning algorithms to predict chemical properties or reaction outcomes based on historical data.
• Anomaly Detection: Identifying unusual patterns or outliers in chemical processes that may indicate process abnormalities.

Importance

• Data Interpretation: Algorithms should provide interpretable results that chemists can use to make informed decisions about experiments and processes.
• Automation and Integration: Algorithms should integrate with laboratory automation systems to streamline data acquisition and process control.
• Safety and Compliance: Ensuring algorithms comply with safety regulations and guidelines for handling hazardous chemicals and processes.

Sustainable Cities

1. Urban Planning and Design: Spatial analysis, predictive modeling, simulation, urban digital twins.
2. Energy Efficiency and Management: Smart grids, building energy management, renewable energy integration.
3. Transportation and Mobility: Traffic management, public transport optimization, urban mobility applications.
4. Waste Management and Recycling: Smart waste collection, waste tracking.
5. Water Management: Smart water systems, water conservation.
6. Environmental Monitoring and Air Quality: Sensor networks, early warning systems.
7. Community Engagement and Decision Support: Data-driven systems, citizen feedback systems.

Energy Transition

1. Renewable Energy Integration and Optimization
2. Smart Grids and Energy Management
3. Energy Efficiency and Conservation
4. Policy Making and Decision Support
5. Consumer Engagement and Behavior Change
6. Financial and Investment Decisions

Colors

• Color Tool - Material Design

Images

• Wikimedia Commons