Training Catalog

• Acquisition of the basics in image analysis
• Mastery of tools and techniques for AI-assisted analysis
• Hands-on practice and development of image pipelines using Generative AI

Half-Day 1:

Introduction to Imaging:

• Overview of imaging and computer vision
• Basic concepts in analysis
• Introduction to image analysis tools (ImageJ, Python, ICY, etc.)

Focus on Artificial Intelligence (AI):

• Understanding language models (LLMs) and their benchmarking for bio-image analysis
• Description of image analysis pipeline assisted by AI

Practical Sessions:

• Testing and comparing tools with basic image processing scripts
• Introduction to GenAI (ChatGPT, Claude, etc.) for integrating AI into image analysis

Half-Day 2:

Deep Dive into Generative AI Applications:

• Using GenAI in image pipelines: preprocessing and image processing
• Developing complex workflows with automation via macros and graphical interface generation

Focus on Prompt Engineering:

• Presentation of prompts applied to image analysis (practical exercises)
• Documentation and illustration of generated codes using tools like Roboflow

Advanced Practical Sessions:

• Application of image processing pipelines with GenAI
• Executing generated scripts in Python and Fiji
• Generating automated reports with GenAI

• Learn the inherent rules of scientific article writing
• Understand how Generative AI works and master Prompt Engineering
• Use AI for article writing: assistance with structure, reformulations, translations…

Half-Day 1: The Steps of Scientific Article Writing

• Objectives of a Scientific Article and Key Guidelines
• Writing an Abstract and a Graphical Abstract
• Introduction and Bibliography
• Results (Creating Figures and Tables)
• Discussion and Conclusion

Half-Day 2:

Introduction to Generative AI

• How Generative AI Works
• Prompt Engineering Applied to Article Writing
• Ethical Considerations Around AI

Application to Article Writing

• Overview of Various AI Tools for Different Article Sections
• Using AI for Other Applications Beyond Writing (Translation, Proofreading, Statistical Analysis, etc.)
• Practical Exercises and Real-World Use Cases of AI in Scientific Writing

• Understand the fundamentals of Generative AI and its applications in communication
• Use AI in communication strategy: defining target audiences, structuring messages, choosing formats…

Half-Day 1:

Introduction to Generative AI

• History
• How it works

Applications in Communication

Module 1: Transforming Your Knowledge into a Clear and Impactful Message

• Understand different types of scientific communication
• Define your target audience
• Structure a scientific message methodically
• Practical exercises

Module 2: AI as a Time-Saver in Scientific Communication

• Structuring prompts (role-play of AI) and creating different profiles
• Creating prompts based on objectives (and previously defined target audiences)
• Practical exercise

Half-Day 2:

Applications in Communication

Module 3: Building a Scientific Communication Strategy

• Choosing the right format
• Empowering texts, infographics, and visuals with AI
• Practical exercise

Module 4: Practical Case

• Create your first scientific publications, communication plan, and resource bank with the help of AI

• Deepen your mastery of R to perform advanced statistical analyses.
• Discover and leverage generative AI tools to automate common tasks in data analysis.
• Optimize analytical processes by integrating automated workflows.
• Structure complex workflows effectively and innovatively.
• Strengthen the understanding and application of advanced statistical concepts to real and simulated cases.

• Understand the fundamentals of generative AI and its applications.
• Master AI-assisted qualitative analysis techniques.
• Use AI for analyzing and presenting qualitative data.

• Discover the general principles of Deep Learning and Generative Artificial Intelligence, and benefit from the potential of Generative AI tools.
• Use advanced prompting techniques to meet business needs.
• Classify generative AI tools according to the media they implement (text-to-text, text-to-image, etc.), and select the right tool for a specific use case.
• Produce educational activities such as course notes, study guides, or chapter summaries to assist students in their learning.
• Design exams and quizzes based on the training content developed for students, saving time in course preparation.
• Generate scenarios or case studies for group projects, based on current topics or scientific advancements, to expand the possibilities of course facilitation (content, group workshops, etc.).
• Adapt your teaching and prepare your students for the Generative AI revolution.

Part 1: The Basics of AI and Prompt Engineering (1h30)

• Describe the general operating principle of a generative artificial intelligence
• Discover prompt engineering

Part 2: Uses of Prompt Engineering (1h30 + 30min practical work)

• Describe the main models of prompt engineering
• Share use cases in research and teaching
• Create several resources: summaries, articles, translations, presentations, etc.

Part 3: Going Further with Generative AIs – Real-world Cases & Tools (3h00 + 30min practical work)

• Experiment with creative uses of AI to generate content, brainstorm, argue
• Discover generative AI tools to simplify tasks (Chatbase, Custom GPT, Brancher.ai, Scispace, etc.). Example: create GPTs to automate the writing of references in the correct format
• Discuss challenging questions: ethics, copyright, security…

Part 1: Basics of AI and Prompt Engineering (1h30)

• Describe the general working principle of a generative artificial intelligence
• Discover prompt engineering

Part 2: Uses of Prompt Engineering (1h30 + 30min practical work)

• Describe the main models of prompt engineering
• Share use cases in the fields of research and education
• Create multiple resources: summaries, articles, translations, presentations, etc.

Part 3: Going Further with Generative AI – Case Studies & Tools (3h00 + 30min practical work)

• Experiment with creative uses of AI to generate content, brainstorm, and argue
• Discover generative AI tools to make life easier (Chatbase, Custom GPT, Brancher.ai, Scispace, etc.). Example: create a GPT to automate the formatting of references
• Discuss the challenges and upheavals of generative AI for the sector: ethics, copyrights, security…

• Understand Generative AI, its functioning, and its limitations
• Explore the applications of Generative AI and use cases for scientists
• Define a plan for integrating Generative AI into your team/laboratory and prioritize the first actions

Sequence 1:

• What is AI?
• Predictive AI vs. Generative AI
• Acceleration of AI development
• Overview of Generative AI
• LLMs, RAG, and agents
• Adoption and generational challenges
• Risks of AI
• Ethical & Legal considerations

Sequence 2:

• AI in a Laboratory

  • A lever for innovation & competitiveness
  • Use cases in laboratories
  • Choosing the right tools & providers

Starting the Integration

• What are the different ‘AI projects’?
• What are the areas for transformation?

• Discover the fundamentals of R and generative AI tools.
• Learn the basics of descriptive statistical analysis and apply them to real-world datasets.
• Learn how to automate common tasks in R using generative AI.
• Master the creation of simple visualizations and data presentation.
• Get introduced to the concepts of simple regressions and hypothesis testing.
• Generate workflows and automated reports with R Markdown.

Half-day 1: Introduction to Generative AI and Its Application in Statistical Analysis

1. Introduction to Generative AI

   • Definition and fundamental principles
   • How generative AI models work
   • Strengths and limitations of generative AI in data processing

2. How Generative AI Can Simplify Learning Statistical Tools

3. Practical Use Case for Beginners

   • Generating fake data examples to illustrate statistical concepts
   • Offering simplified explanations for basic concepts (mean, standard deviation, etc.)
   • Structuring a step-by-step approach for a simple statistical analysis

Half-day 2: First Steps with R

1. Installation and Getting Started with RStudio Interface

2. First Scripts: Introduction to Key Object Types in R

3. Introduction to Essential Packages: dplyr, tidyr, ggplot2

4. Exploring a Simple Dataset

Half-day 3: Effectively Visualizing and Analyzing Your Data

1. Descriptive Analysis: Means, medians, standard deviations, frequencies.

2. Creating Visualizations: Histograms, boxplots, bar charts.

3. Case Study: Analyzing and visualizing a real dataset.

4. Introduction to Automation: Using AI to generate simple visualization scripts.

Half-day 4: Introduction to Regression and Hypothesis Testing

1. Simple Linear Regressions: Concepts and implementation.

2. Introduction to Hypothesis Testing

3. Practical Workshop with Simulated or Real Datasets

Half-day 5: Automating Analysis with R and Generative AI

1. Prompt Engineering for Automating Analysis with R

   • Writing simple prompts to generate R code.
   • Automating key steps: data loading, cleaning, analysis, and visualization.

2. Practical Example: Creating an Automated Workflow with R and Generative AI

   • Loading a data file.
   • Summarizing data and producing simple visualizations.
   • Generating an automated report (HTML or PDF) using R Markdown.

• Describe how a Generative AI (GenAI) works in order to optimize its use in daily tasks
• Apply prompt engineering methods to use GenAI tools effectively
• Identify use cases within your professional environment

Half-Day Session:

Introduction to AIs:

• History and functioning of AI

Prompt Engineering:

• Methods and applications

Professional Use Cases:

• Hands-on practice with real-world applications

• Understanding the contribution of NVivo for analyzing qualitative data
• Using the NVivo interface
• Importing and organizing data in an NVivo project
• Coding data
• Understanding the most advanced features of the software (queries, visualizations, intercoder reliability…)

Day 1

1. Introduction to NVivo
2. Discovering the NVivo user interface
3. Creating and importing data in NVivo*
4. Organizing data through memo links and annotations*
5. Organizing data using file classifications*
6. Organizing data using cases and case classifications*
7. Coding data (coding, uncoding, coding stripes, organizing codes hierarchically, codebook)*

Day 2

1. Coding relations and sentiments
2. Working with non-textual data (pdfs, audio, video, pictures, surveys)
3. Team work
4. Visuals (exploration tools and maps)
5. Queries (word frequency, text search, coding, matrixes and crosstabs, coding comparison)

* Users can use their own data during these sequences.

• Acquisition of the basics in image analysis
• Mastery of tools and techniques for AI-assisted analysis
• Hands-on practice and development of image pipelines using Generative AI

Half-Day 1:

Introduction to Imaging:

• Overview of imaging and computer vision
• Basic concepts in analysis
• Introduction to image analysis tools (ImageJ, Python, ICY, etc.)

Focus on Artificial Intelligence (AI):

• Understanding language models (LLMs) and their benchmarking for bio-image analysis
• Description of image analysis pipeline assisted by AI

Practical Sessions:

• Testing and comparing tools with basic image processing scripts
• Introduction to GenAI (ChatGPT, Claude, etc.) for integrating AI into image analysis

Half-Day 2:

Deep Dive into Generative AI Applications:

• Using GenAI in image pipelines: preprocessing and image processing
• Developing complex workflows with automation via macros and graphical interface generation

Focus on Prompt Engineering:

• Presentation of prompts applied to image analysis (practical exercises)
• Documentation and illustration of generated codes using tools like Roboflow

Advanced Practical Sessions:

• Application of image processing pipelines with GenAI
• Executing generated scripts in Python and Fiji
• Generating automated reports with GenAI

• Mastery of the basic functions in order to be autonomous with Stata on the following subjects: Descriptive statistics, graphs and first estimates.

4 modules of 3.5 hours each

Stata Deb1 : Meeting the software 

• Presentation of the software environment
• How to set up a Stata session to be efficient
• The general syntax of a Stata command
• How to use the help to become autonomous
• Commented example of a Stata session to understand its possibilities
• Importing data, describing them and visualizing them: a first approach
• A first exercise

Stata Deb2: Working with your data 

• Exploring a data file: the if, by and in conditional
• Manipulating variables: creation, recoding, labels and many other tricks
• Handling data: sorting, deleting, merging, changing format and producing aggregated data
• An exercise to test yourself

Stata Deb3: Descriptive Statistics, Tables and Charts 

• Descriptive statistics
• Synthetic statistical tables
• Univariate analysis
• An introduction to analysis of variance
• Graphs with Stata
• A synthetic exercise

Stata Deb4: An introduction to regression

• Linear regression: estimation, post-estimation, diagnostics and tests
• Logistic regression: estimation, post-estimation, diagnostics and tests
• Discovering programming: loops
• Synthetic exercise (continued)

Learn how to create and analyse Factorial Design of Experiments with Design Expert.

Please contact us if you would prefer to have this training course held in English.

• Discover the R language and software and learn the first basics of this language

R1: First steps in R

• Introduction
• Entering a command in the console
• Writing a clean, structured and commented script
• Create, modify, view and delete an object
• Manipulating different data types and data structures
• The R objects: vectors, factors, arrays, lists, data frames, functions

R2: Import, control and export data arrays

• View and edit working directory
• Import data contained in a .csv file
• Check the types of its variables and modify them if needed
• Categorical variables: factors
• Controlling for missing data
• Exporting a data table to a .csv file

R3: Numerical valuation of data

• Manipulating your dataset (selecting variables, rows …)
• Numerical valuation: getting to know the dataset, summarizing and quantifying the information
• Descriptive statistics, counts, pivot tables
• Data aggregation (statistics by group of observations)

R4: Graphical valuation of data

• Creating basic graphs: histogram, scatterplot, box plot, bar chart, pie chart
• Changing the various basic chart options (color, title, point and line type, size, …)
• Add elements to a chart (points, lines, segments, legends, …)
• Save a graph

• Understand the fundamentals of generative AI and its applications.
• Master AI-assisted qualitative analysis techniques.
• Use AI for analyzing and presenting qualitative data.

• Master more complex machine learning models, in particular ensemble methods based on bagging and boosting techniques, to use and optimize penalty models (lasso and elasticnet), to understand the bootstrap resampling technique for estimation and cross-validation, to know how to implement collaborative filtering techniques.
• At the end of this training, the participant will have a global vision of the different multivariate modeling techniques.

Day 1

• Advanced data mining:
  o DBSCAN, unsupervised data clustering algorithm
  o Manifold Learning

DAY 2

• Gaussian Mixture Modelling (GMM)
• Optimizing penalty models with Lasso and elasticnet (regression, PLS)
• Support Vector Machine (SVM)

DAY 3

• Random Forest and Gradient Boosting Machines
• Bootstraping estimation and cross-validation
• Collaborative filtering and recommendation system

• Understand the basics of machine learning and machine learning on structured data, apply standard dimension reduction and clustering methods, know how to implement a regression model by controlling overfitting and validating model predictions, understand the basics of text mining.
• At the end of this training, the participant will be able to determine the type of techniques to apply based on the questions asked and perform elaborate pre-processing to implement predictive models.

DAY 1

• Introduction to unsupervised methods:
  • Principal component analysis (PCA)
  • Automatic classification (k-means)
  • Association rules (apriori, eclat)
• Introduction to supervised methods:
  • Linear and logistic regression models with regularization (ridge regression)
  • Decision trees (regression and classification)

DAY 2

• Standard methods for implementing predictive models:
  • Feature engineering: learning to reduce the complexity of a problem,
  • Variable selection,
  • Cross-validation,
  • Calibration of a predictive model
  • Text mining and web scraping

• Understand the role of NVivo in the qualitative analysis process.
• Understand and master the NVivo environment.
  • Source management
  • Case management
  • Source coding
  • Crossing matrices
  • Documenting your analysis; memos, annotations and links to.

First half-day

1. Qualitative analysis with NVivo and getting started with the software (1h)

• Reminder of the basic principles of qualitative analysis
• The place of Computer-Assisted Qualitative Data Analysis Software in the research process
• NVIVO’s interface and perspective

2. Preparing a project (1h)

• Preparation of the data, organisation of the software and data importation (textual data in word and pdf – working with images)
• The NVivo mind map: brainstorming as a starting point for coding
• Practical exercises

3. Deductive and inductive coding (2h)

• Practical exercises on textual and image databases
• The different coding logics.
• The relations

Module 1: The special case of audio and video data and transcription (2h)

• The different types of transcription
• Exchange between participants on their good practices
• Tools for efficient transcription
• Demonstration of NVivo transcription
• Importing transcriptions made outside NVIVO

Module 2: Automatic queries (2h)

• Automatic coding of emotions and themes
• Automatic coding based on document structure – working with semi-structured and structured interviews

Module 3: Working with surveys and data tables (2h)

• Importing Excel files

Module 4: Documenting your analyses, your work + Cross-referencing matrices (2h)

• Memos and annotations
• See-also links
• Matrix coding query to explore the links between ideas

Exchange with participants about their NVivo practices and refresh their knowledge

From participant data:
Mastering the NVivo environment.
– Data management
– Case management
– Source coding
– Queries and matrices
– Documenting your analysis; memos, annotations and see-also links.
– Visualisation and export
– Importing and using data from social networks and from internet
– Collaborative work and encoder comparison.

First half-day: Review of the basic principles of qualitative analysis with NVivo (3h)

Exchange with participants on their practices and needs + Refresher course if necessary
The aim of this module is to standardise knowledge of the software and qualitative analysis.
Each participant will be able to present his or her research and questions in preparation for the training

Module 1: Generating and using cases through queries (2h)

• Cases, classifications, attributes and attribute values – what differences in NVivo?
• Importing classifications sheets from Excel
• Organisation of cases and nesting of the different levels

Module 2: Automatic queries and collaborative work (2h)

• Automatic coding of emotions and themes
• Automatic coding based on document structure – working with semi-structured and structured interviews
• Working with several people on an NVivo project
• Encoder comparison

Module 3: Queries: Deepening the understanding of your corpus and its coding (2h)

• Frequency queries and word clouds
• Word search, synapsis and hierarchical top-down classification.
• Matrix coding: exploratory approaches and word overlap
• Crosstab: cross-referencing nodes and features
• Coding queries: answering a research question in a few clicks

Module 4: Documenting analysis, work and visualisations (2h)

• Memos and annotations
• See-also links
• Framework matrices
• Comparison diagrams, clusters, project and concept maps.

Module 5: Working with web data (2h)

• Importing data from Twitter, Facebook and YouTube
• Importing data from websites
• Social network analysis with NVivo

Module 6: Literature review with NVivo (2h)

• Importing scientific documents from (Endnote, Zotéro, Refworks, Mendeley or Citavi)
• Thematic coding of scientific documents

• Deepen the tools to represent and manipulate complex data, effectively use the pandas library, import data sources (CSV, JSON, XML, SQL), perform a simple or multiple linear regression model with the statmodels library, perfect your knowledge of matplotlib and know how to use seaborn or plotly..
• At the end of this training, the participant should be able to import or even merge structured or unstructured data sources, apply advanced processing on quantitative and qualitative data and build elaborate static or dynamic graphs.

DAY 1

• Advanced Data Processing:
  • The numpy library :
    • advanced functions (views, slices)
    • the interface with scipy
  • The pandas library:
    • The import of external data sources,
    • The aggregation of data,
    • The reshaping,
    • Indexing,
    • The merging of data sources
  • The statmodels library:
    • Single and multiple regression,
    • The testing of regression coefficients,
    • The diagnosis of the model,
    • Point and interval prediction
• The processing of strings, regex
• Date processing and time series management

DAY 2

• The generators, itertools, lazy evaluation
• The database interface (SQL, NoSQL)
• The Seaborn package:Advanced graphing features (trellis graphs, statistical distributions, heatmap)
• Interactive graphics with the Bokeh and Plotly packages.

• Understand how data is represented, know how to manipulate simple data structures, master the basics of the numpy and scipy libraries for numerical computation and basic statistical functions, learn the basics of graphical visualization with matplotlib.
• At the end of this training, the participant should be able to write simple analysis scripts working either with artificial data or with data sources that do not require major pre-processing. He/she will know how to implement the main statistical tests for the comparison of two samples and perform basic exploratory graphs.

Day 1

• The working environment: Python 2 and 3.x
• The presentation of the different consoles and debugging in Python:
  • Anaconda
  • Jupyter
  • Spyder
• Data types: lists, dictionaries
• Control structures
• The functions, methods and packages

DAY 2

• Data handling and cleaning:
  • numpy: Basic objects and manipulation of 2-dimensional arrays (array and numeric calculation functions, random number generators)
  • scipy: Basic functionality (scientific functions and basic statistical tests)
  • The probability distributions and univariate statistics.
    • matplotlib: basic features: scatterplot, box plot, histogram
  • Simple scripting

Learn more about the tools used to represent and manipulate complex data, discover the dplyr and data.table packages to optimise data processing, import data sources (CSV, JSON, XML, SQL), create a simple or multiple linear regression model using the {stats} package, improve your knowledge of graphs and know how to use ggplot2 or plotly.

DAY 1

• Importing external data sources with the{base}, {foreign} and {haven} packages

• The tools for optimizing data processing, {data.table} and {dplyr}:
  • Advanced data frame manipulation,
  • Data aggregation,
  • The reshaping,
  • Indexing,
  • The merging of data sources

• The realization of a simple or multiple linear regression model with the {stats} package:
  • The simple and multiple regression,
  • The testing of regression coefficients,
  • The diagnosis of the model,
  • Point and interval prediction

DAY 2

• The processing of strings, regex
• The processing of dates and time series management
• The functional approach and lazy evaluation
• The database interface (SQL, NoSQL)
• The advanced graphical features with the {ggplot2} package:
  • Trellis charts,
  • Statistical distributions,
  • Data presentation with heatmap
• Building elaborate interactive static or dynamic graphs with the {ggvis} and {plotly} packages

• Understand how data is represented, know how to manipulate simple data structures, master basic functions in the {base} and {stats} packages for numerical computation and basic statistical functions, learn the basics of graphical visualization with the basic graphics package {graphics}.
• At the end of this training, the participant should be able to write simple analysis scripts working with either artificial data or data sources that do not require major pre-processing. He/she will know how to implement the main statistical tests for comparing two samples and perform basic exploratory graphs.

DAY 1

• The work environment:
  • Introduction to R 3.x
  • Introduction to RStudio
• The different basic data types (vector, list, data frame), control structures, simple functions
• The basic control structures of R
• The important functions and packages for data manipulation

DAY 2

• The basic features:
  • Simple numeric functions,
  • The random number generators
• The probability distributions and univariate statistics
• Scientific functions {stats} and basic statistical tests
  • Elementary statistical graphical management: scatterplot, box plot, histogram)
• The writing of simple programming scripts with R

Discover all the possibilities offered by PRISM to get the graph you want, automated plotting of fit curves included.

Please contact us if you would like to have this training course held in English.

• Improve your knowledge of the software and discover advanced functions of Stata
  • Declare data in different formats
  • Advanced estimation
  • Programming.

4 modules of 3.5 hours each

Stata Av1 : Advanced data management

• Treatment of alphanumeric variables
• Merging and aggregating data
• Processing of date variables and time series operators
• Advanced management of graphs

Stata Av2 : Programming with Stata

• Local and global macros
• Loops, sums and counters, temporary variables
• Programming to reproduce
• Application examples

Stata Av3: Estimation methods, tests and predictions

• Linear regression with categorical variables and interactions
• Presenting results in a synthetic table and exporting them
• Specification tests and diagnostics
• Extension to other estimation methods

Stata Av4: Introduction to panel data estimation

• Structure and visualize data
• Fixed effects model
• Random effects models
• Some extensions

• Learn to account for different sources of endogeneity in a regression model

3.5 hour module

1. The different sources of endogeneity and the consequences for the properties of estimators
2. Estimation methods
3. Synthetic ordering that allows for these different sources to be considered in a single model
4. Practical exercise

• How to master Lasso (least absolute shrinkage and selection operator) methods with Stata for prediction and/or inference on causal parameters. This regression method (typically used in high-dimensional problems) consists of penalizing the absolute size of the regression coefficients.

Please contact us if you would like to have this training course held in English.

Processing and Analyzing Time Series with Stata.

Please contact us if you would like to have this training course held in English & for the whole program.

Know how to process time series with R.

Please contact us if you would prefer to have this training course held in English.

• Become familiar with three main components of Stata: data management, data analysis, and data visualization.
• Upon completion of the course, you will be able to use Stata efficiently for data management, basic analyses and graphics.
• You will be able to create reproducible analysis, for better collaborative works and simplified follow-up analyses

Fundamentals of Using Stata (1h30 – Day1)

• Keeping organized
• Knowing how Stata treats data
• Using dialog boxes efficiently
• Using the Command window
• Saving time and effort while working
• A sample Stata Session
• Getting Help

Basic Data Management in Stata (3h30 – Day1 and Day2)

• Reading Data in Stata
  o Using and Saving Stata data files
  o Reading in datasets of various standard formats, such as those from spreadsheets or databases
• Labeling data, variables and values and setting up encoded variables
• Creating and Recoding variables in an efficient fashion
• Generating statistics within groups, and working across variables

Intermediate Data Management in Stata (2h – Day2)

• Combining datasets by adding observations and by adding variables
• Reshaping data from wide to long
• Reshaping data from long to wide
• Collapsing data across observations

Workflow (1h30 – Day3)

• Using menus and the Command window to work quickly
• Setting up Stata for your profile
• Keeping complete records of what is done inside Stata: saving dofile
• Creating reproducible analyses, which are completely documented
• Finding, installing, and removing community-contributed extensions to Stata
• Customizing how Stata starts up and where it looks for files

Analysis (3h30 – Day3 and Day 4)

• Using basic statistical commands
• Reusing results of Stata commands
• Using common postestimation commands
• Working with interactions and factor variables

Graphics (2h – Day 4)

• Introduction to graphics
• Overview of graph two-way plots
• Building up complex graphs
• Using the Graph Editor

Addressing the Agile mindset and concepts.

1. Why move to Agile ?
2. Introduction to Agile
3. The vocabulary of Agile
4. The principles of Scrum

Understand the Design Thinking process in order to use it to innovate and solve complex problems.

1. Discover: discover the challenge
2. Define: define precisely the problem to be solved
3. Develop: imagine the most relevant solution
4. Deliver: build the solution and collect feedbacks according to an iterative approach

• Understand Generative AI, its functioning, and its limitations
• Explore the applications of Generative AI and use cases for scientists
• Define a plan for integrating Generative AI into your team/laboratory and prioritize the first actions

Sequence 1:

• What is AI?
• Predictive AI vs. Generative AI
• Acceleration of AI development
• Overview of Generative AI
• LLMs, RAG, and agents
• Adoption and generational challenges
• Risks of AI
• Ethical & Legal considerations

Sequence 2:

• AI in a Laboratory

  • A lever for innovation & competitiveness
  • Use cases in laboratories
  • Choosing the right tools & providers

Starting the Integration

• What are the different ‘AI projects’?
• What are the areas for transformation?

• Understand high-throughput genomic sequencing
• Be able to choose the appropriate technology for your project
• Know the bioinformatics tools used
• Know the possible statistical analyses

Please contact us if you would like to have this training course held in English.

Discover all the possibilities offered by PRISM to get the graph you want, automated plotting of fit curves included.

Please contact us if you would like to have this training course held in English.

• Acquisition of the basics in image analysis
• Mastery of tools and techniques for AI-assisted analysis
• Hands-on practice and development of image pipelines using Generative AI

Half-Day 1:

Introduction to Imaging:

• Overview of imaging and computer vision
• Basic concepts in analysis
• Introduction to image analysis tools (ImageJ, Python, ICY, etc.)

Focus on Artificial Intelligence (AI):

• Understanding language models (LLMs) and their benchmarking for bio-image analysis
• Description of image analysis pipeline assisted by AI

Practical Sessions:

• Testing and comparing tools with basic image processing scripts
• Introduction to GenAI (ChatGPT, Claude, etc.) for integrating AI into image analysis

Half-Day 2:

Deep Dive into Generative AI Applications:

• Using GenAI in image pipelines: preprocessing and image processing
• Developing complex workflows with automation via macros and graphical interface generation

Focus on Prompt Engineering:

• Presentation of prompts applied to image analysis (practical exercises)
• Documentation and illustration of generated codes using tools like Roboflow

Advanced Practical Sessions:

• Application of image processing pipelines with GenAI
• Executing generated scripts in Python and Fiji
• Generating automated reports with GenAI

• Understand the fundamentals of Generative AI and its applications in communication
• Use AI in communication strategy: defining target audiences, structuring messages, choosing formats…

Half-Day 1:

Introduction to Generative AI

• History
• How it works

Applications in Communication

Module 1: Transforming Your Knowledge into a Clear and Impactful Message

• Understand different types of scientific communication
• Define your target audience
• Structure a scientific message methodically
• Practical exercises

Module 2: AI as a Time-Saver in Scientific Communication

• Structuring prompts (role-play of AI) and creating different profiles
• Creating prompts based on objectives (and previously defined target audiences)
• Practical exercise

Half-Day 2:

Applications in Communication

Module 3: Building a Scientific Communication Strategy

• Choosing the right format
• Empowering texts, infographics, and visuals with AI
• Practical exercise

Module 4: Practical Case

• Create your first scientific publications, communication plan, and resource bank with the help of AI

Knowing how to manipulate data and extract information from it with R.

Please contact us if you would like to have this training course held in English.

Understand the data science ecosystem and know the tools related to the realization of a data science project.

DAY 1

• The unix environment, interacting with a shell, open source tools (sed, awk, grep, jq, csvkit, etc.), R and Python, SQL and NoSQL
• Revision control and collaborative work with Git
• The methodology for managing a data science project
• Software engineering fundamentals and best practices

DAY 2

• Information gathering and processing (experimental designs and clinical trials, surveys and polls, web data, open data)
• Distributed architecture and database, map-reduce, big data, Apache Spark

• Deepen your mastery of R to perform advanced statistical analyses.
• Discover and leverage generative AI tools to automate common tasks in data analysis.
• Optimize analytical processes by integrating automated workflows.
• Structure complex workflows effectively and innovatively.
• Strengthen the understanding and application of advanced statistical concepts to real and simulated cases.

• Discover the R language and software and learn the first basics of this language

R1: First steps in R

• Introduction
• Entering a command in the console
• Writing a clean, structured and commented script
• Create, modify, view and delete an object
• Manipulating different data types and data structures
• The R objects: vectors, factors, arrays, lists, data frames, functions

R2: Import, control and export data arrays

• View and edit working directory
• Import data contained in a .csv file
• Check the types of its variables and modify them if needed
• Categorical variables: factors
• Controlling for missing data
• Exporting a data table to a .csv file

R3: Numerical valuation of data

• Manipulating your dataset (selecting variables, rows …)
• Numerical valuation: getting to know the dataset, summarizing and quantifying the information
• Descriptive statistics, counts, pivot tables
• Data aggregation (statistics by group of observations)

R4: Graphical valuation of data

• Creating basic graphs: histogram, scatterplot, box plot, bar chart, pie chart
• Changing the various basic chart options (color, title, point and line type, size, …)
• Add elements to a chart (points, lines, segments, legends, …)
• Save a graph

• Understand Generative AI, its functioning, and its limitations
• Explore the applications of Generative AI and use cases for scientists
• Define a plan for integrating Generative AI into your team/laboratory and prioritize the first actions

Sequence 1:

• What is AI?
• Predictive AI vs. Generative AI
• Acceleration of AI development
• Overview of Generative AI
• LLMs, RAG, and agents
• Adoption and generational challenges
• Risks of AI
• Ethical & Legal considerations

Sequence 2:

• AI in a Laboratory

  • A lever for innovation & competitiveness
  • Use cases in laboratories
  • Choosing the right tools & providers

Starting the Integration

• What are the different ‘AI projects’?
• What are the areas for transformation?

• Discover the fundamentals of R and generative AI tools.
• Learn the basics of descriptive statistical analysis and apply them to real-world datasets.
• Learn how to automate common tasks in R using generative AI.
• Master the creation of simple visualizations and data presentation.
• Get introduced to the concepts of simple regressions and hypothesis testing.
• Generate workflows and automated reports with R Markdown.

Half-day 1: Introduction to Generative AI and Its Application in Statistical Analysis

1. Introduction to Generative AI

   • Definition and fundamental principles
   • How generative AI models work
   • Strengths and limitations of generative AI in data processing

2. How Generative AI Can Simplify Learning Statistical Tools

3. Practical Use Case for Beginners

   • Generating fake data examples to illustrate statistical concepts
   • Offering simplified explanations for basic concepts (mean, standard deviation, etc.)
   • Structuring a step-by-step approach for a simple statistical analysis

Half-day 2: First Steps with R

1. Installation and Getting Started with RStudio Interface

2. First Scripts: Introduction to Key Object Types in R

3. Introduction to Essential Packages: dplyr, tidyr, ggplot2

4. Exploring a Simple Dataset

Half-day 3: Effectively Visualizing and Analyzing Your Data

1. Descriptive Analysis: Means, medians, standard deviations, frequencies.

2. Creating Visualizations: Histograms, boxplots, bar charts.

3. Case Study: Analyzing and visualizing a real dataset.

4. Introduction to Automation: Using AI to generate simple visualization scripts.

Half-day 4: Introduction to Regression and Hypothesis Testing

1. Simple Linear Regressions: Concepts and implementation.

2. Introduction to Hypothesis Testing

3. Practical Workshop with Simulated or Real Datasets

Half-day 5: Automating Analysis with R and Generative AI

1. Prompt Engineering for Automating Analysis with R

   • Writing simple prompts to generate R code.
   • Automating key steps: data loading, cleaning, analysis, and visualization.

2. Practical Example: Creating an Automated Workflow with R and Generative AI

   • Loading a data file.
   • Summarizing data and producing simple visualizations.
   • Generating an automated report (HTML or PDF) using R Markdown.

• Master more complex machine learning models, in particular ensemble methods based on bagging and boosting techniques, to use and optimize penalty models (lasso and elasticnet), to understand the bootstrap resampling technique for estimation and cross-validation, to know how to implement collaborative filtering techniques.
• At the end of this training, the participant will have a global vision of the different multivariate modeling techniques.

Day 1

• Advanced data mining:
  o DBSCAN, unsupervised data clustering algorithm
  o Manifold Learning

DAY 2

• Gaussian Mixture Modelling (GMM)
• Optimizing penalty models with Lasso and elasticnet (regression, PLS)
• Support Vector Machine (SVM)

DAY 3

• Random Forest and Gradient Boosting Machines
• Bootstraping estimation and cross-validation
• Collaborative filtering and recommendation system

• Understand the basics of machine learning and machine learning on structured data, apply standard dimension reduction and clustering methods, know how to implement a regression model by controlling overfitting and validating model predictions, understand the basics of text mining.
• At the end of this training, the participant will be able to determine the type of techniques to apply based on the questions asked and perform elaborate pre-processing to implement predictive models.

DAY 1

• Introduction to unsupervised methods:
  • Principal component analysis (PCA)
  • Automatic classification (k-means)
  • Association rules (apriori, eclat)
• Introduction to supervised methods:
  • Linear and logistic regression models with regularization (ridge regression)
  • Decision trees (regression and classification)

DAY 2

• Standard methods for implementing predictive models:
  • Feature engineering: learning to reduce the complexity of a problem,
  • Variable selection,
  • Cross-validation,
  • Calibration of a predictive model
  • Text mining and web scraping

• Describe how a Generative AI (GenAI) works in order to optimize its use in daily tasks
• Apply prompt engineering methods to use GenAI tools effectively
• Identify use cases within your professional environment

Half-Day Session:

Introduction to AIs:

• History and functioning of AI

Prompt Engineering:

• Methods and applications

Professional Use Cases:

• Hands-on practice with real-world applications

• Deepen the tools to represent and manipulate complex data, effectively use the pandas library, import data sources (CSV, JSON, XML, SQL), perform a simple or multiple linear regression model with the statmodels library, perfect your knowledge of matplotlib and know how to use seaborn or plotly..
• At the end of this training, the participant should be able to import or even merge structured or unstructured data sources, apply advanced processing on quantitative and qualitative data and build elaborate static or dynamic graphs.

DAY 1

• Advanced Data Processing:
  • The numpy library :
    • advanced functions (views, slices)
    • the interface with scipy
  • The pandas library:
    • The import of external data sources,
    • The aggregation of data,
    • The reshaping,
    • Indexing,
    • The merging of data sources
  • The statmodels library:
    • Single and multiple regression,
    • The testing of regression coefficients,
    • The diagnosis of the model,
    • Point and interval prediction
• The processing of strings, regex
• Date processing and time series management

DAY 2

• The generators, itertools, lazy evaluation
• The database interface (SQL, NoSQL)
• The Seaborn package:Advanced graphing features (trellis graphs, statistical distributions, heatmap)
• Interactive graphics with the Bokeh and Plotly packages.

• Understand how data is represented, know how to manipulate simple data structures, master the basics of the numpy and scipy libraries for numerical computation and basic statistical functions, learn the basics of graphical visualization with matplotlib.
• At the end of this training, the participant should be able to write simple analysis scripts working either with artificial data or with data sources that do not require major pre-processing. He/she will know how to implement the main statistical tests for the comparison of two samples and perform basic exploratory graphs.

Day 1

• The working environment: Python 2 and 3.x
• The presentation of the different consoles and debugging in Python:
  • Anaconda
  • Jupyter
  • Spyder
• Data types: lists, dictionaries
• Control structures
• The functions, methods and packages

DAY 2

• Data handling and cleaning:
  • numpy: Basic objects and manipulation of 2-dimensional arrays (array and numeric calculation functions, random number generators)
  • scipy: Basic functionality (scientific functions and basic statistical tests)
  • The probability distributions and univariate statistics.
    • matplotlib: basic features: scatterplot, box plot, histogram
  • Simple scripting

Learn more about the tools used to represent and manipulate complex data, discover the dplyr and data.table packages to optimise data processing, import data sources (CSV, JSON, XML, SQL), create a simple or multiple linear regression model using the {stats} package, improve your knowledge of graphs and know how to use ggplot2 or plotly.

DAY 1

• Importing external data sources with the{base}, {foreign} and {haven} packages

• The tools for optimizing data processing, {data.table} and {dplyr}:
  • Advanced data frame manipulation,
  • Data aggregation,
  • The reshaping,
  • Indexing,
  • The merging of data sources

• The realization of a simple or multiple linear regression model with the {stats} package:
  • The simple and multiple regression,
  • The testing of regression coefficients,
  • The diagnosis of the model,
  • Point and interval prediction

DAY 2

• The processing of strings, regex
• The processing of dates and time series management
• The functional approach and lazy evaluation
• The database interface (SQL, NoSQL)
• The advanced graphical features with the {ggplot2} package:
  • Trellis charts,
  • Statistical distributions,
  • Data presentation with heatmap
• Building elaborate interactive static or dynamic graphs with the {ggvis} and {plotly} packages

• Understand how data is represented, know how to manipulate simple data structures, master basic functions in the {base} and {stats} packages for numerical computation and basic statistical functions, learn the basics of graphical visualization with the basic graphics package {graphics}.
• At the end of this training, the participant should be able to write simple analysis scripts working with either artificial data or data sources that do not require major pre-processing. He/she will know how to implement the main statistical tests for comparing two samples and perform basic exploratory graphs.

DAY 1

• The work environment:
  • Introduction to R 3.x
  • Introduction to RStudio
• The different basic data types (vector, list, data frame), control structures, simple functions
• The basic control structures of R
• The important functions and packages for data manipulation

DAY 2

• The basic features:
  • Simple numeric functions,
  • The random number generators
• The probability distributions and univariate statistics
• Scientific functions {stats} and basic statistical tests
  • Elementary statistical graphical management: scatterplot, box plot, histogram)
• The writing of simple programming scripts with R

• Learn to account for different sources of endogeneity in a regression model

3.5 hour module

1. The different sources of endogeneity and the consequences for the properties of estimators
2. Estimation methods
3. Synthetic ordering that allows for these different sources to be considered in a single model
4. Practical exercise

Processing and Analyzing Time Series with Stata.

Please contact us if you would like to have this training course held in English & for the whole program.

Know how to process time series with R.

Please contact us if you would prefer to have this training course held in English.

• Become familiar with three main components of Stata: data management, data analysis, and data visualization.
• Upon completion of the course, you will be able to use Stata efficiently for data management, basic analyses and graphics.
• You will be able to create reproducible analysis, for better collaborative works and simplified follow-up analyses

Fundamentals of Using Stata (1h30 – Day1)

• Keeping organized
• Knowing how Stata treats data
• Using dialog boxes efficiently
• Using the Command window
• Saving time and effort while working
• A sample Stata Session
• Getting Help

Basic Data Management in Stata (3h30 – Day1 and Day2)

• Reading Data in Stata
  o Using and Saving Stata data files
  o Reading in datasets of various standard formats, such as those from spreadsheets or databases
• Labeling data, variables and values and setting up encoded variables
• Creating and Recoding variables in an efficient fashion
• Generating statistics within groups, and working across variables

Intermediate Data Management in Stata (2h – Day2)

• Combining datasets by adding observations and by adding variables
• Reshaping data from wide to long
• Reshaping data from long to wide
• Collapsing data across observations

Workflow (1h30 – Day3)

• Using menus and the Command window to work quickly
• Setting up Stata for your profile
• Keeping complete records of what is done inside Stata: saving dofile
• Creating reproducible analyses, which are completely documented
• Finding, installing, and removing community-contributed extensions to Stata
• Customizing how Stata starts up and where it looks for files

Analysis (3h30 – Day3 and Day 4)

• Using basic statistical commands
• Reusing results of Stata commands
• Using common postestimation commands
• Working with interactions and factor variables

Graphics (2h – Day 4)

• Introduction to graphics
• Overview of graph two-way plots
• Building up complex graphs
• Using the Graph Editor

• Mastery of the basic functions in order to be autonomous with Stata on the following subjects: Descriptive statistics, graphs and first estimates.

4 modules of 3.5 hours each

Stata Deb1 : Meeting the software 

• Presentation of the software environment
• How to set up a Stata session to be efficient
• The general syntax of a Stata command
• How to use the help to become autonomous
• Commented example of a Stata session to understand its possibilities
• Importing data, describing them and visualizing them: a first approach
• A first exercise

Stata Deb2: Working with your data 

• Exploring a data file: the if, by and in conditional
• Manipulating variables: creation, recoding, labels and many other tricks
• Handling data: sorting, deleting, merging, changing format and producing aggregated data
• An exercise to test yourself

Stata Deb3: Descriptive Statistics, Tables and Charts 

• Descriptive statistics
• Synthetic statistical tables
• Univariate analysis
• An introduction to analysis of variance
• Graphs with Stata
• A synthetic exercise

Stata Deb4: An introduction to regression

• Linear regression: estimation, post-estimation, diagnostics and tests
• Logistic regression: estimation, post-estimation, diagnostics and tests
• Discovering programming: loops
• Synthetic exercise (continued)

Processing and Analyzing Time Series with Stata.

Please contact us if you would like to have this training course held in English & for the whole program.

Understand the data science ecosystem and know the tools related to the realization of a data science project.

DAY 1

• The unix environment, interacting with a shell, open source tools (sed, awk, grep, jq, csvkit, etc.), R and Python, SQL and NoSQL
• Revision control and collaborative work with Git
• The methodology for managing a data science project
• Software engineering fundamentals and best practices

DAY 2

• Information gathering and processing (experimental designs and clinical trials, surveys and polls, web data, open data)
• Distributed architecture and database, map-reduce, big data, Apache Spark

Learn how to create and analyse Factorial Design of Experiments with Design Expert.

Please contact us if you would prefer to have this training course held in English.

Addressing the Agile mindset and concepts.

1. Why move to Agile ?
2. Introduction to Agile
3. The vocabulary of Agile
4. The principles of Scrum

Learn how to create and analyse Factorial Design of Experiments with Design Expert.

Please contact us if you would prefer to have this training course held in English.

• Understand high-throughput genomic sequencing
• Be able to choose the appropriate technology for your project
• Know the bioinformatics tools used
• Know the possible statistical analyses

Please contact us if you would like to have this training course held in English.

Understand the Design Thinking process in order to use it to innovate and solve complex problems.

1. Discover: discover the challenge
2. Define: define precisely the problem to be solved
3. Develop: imagine the most relevant solution
4. Deliver: build the solution and collect feedbacks according to an iterative approach

• Understand Generative AI, its functioning, and its limitations
• Explore the applications of Generative AI and use cases for scientists
• Define a plan for integrating Generative AI into your team/laboratory and prioritize the first actions

Sequence 1:

• What is AI?
• Predictive AI vs. Generative AI
• Acceleration of AI development
• Overview of Generative AI
• LLMs, RAG, and agents
• Adoption and generational challenges
• Risks of AI
• Ethical & Legal considerations

Sequence 2:

• AI in a Laboratory

  • A lever for innovation & competitiveness
  • Use cases in laboratories
  • Choosing the right tools & providers

Starting the Integration

• What are the different ‘AI projects’?
• What are the areas for transformation?

• Acquisition of the basics in image analysis
• Mastery of tools and techniques for AI-assisted analysis
• Hands-on practice and development of image pipelines using Generative AI

Half-Day 1:

Introduction to Imaging:

• Overview of imaging and computer vision
• Basic concepts in analysis
• Introduction to image analysis tools (ImageJ, Python, ICY, etc.)

Focus on Artificial Intelligence (AI):

• Understanding language models (LLMs) and their benchmarking for bio-image analysis
• Description of image analysis pipeline assisted by AI

Practical Sessions:

• Testing and comparing tools with basic image processing scripts
• Introduction to GenAI (ChatGPT, Claude, etc.) for integrating AI into image analysis

Half-Day 2:

Deep Dive into Generative AI Applications:

• Using GenAI in image pipelines: preprocessing and image processing
• Developing complex workflows with automation via macros and graphical interface generation

Focus on Prompt Engineering:

• Presentation of prompts applied to image analysis (practical exercises)
• Documentation and illustration of generated codes using tools like Roboflow

Advanced Practical Sessions:

• Application of image processing pipelines with GenAI
• Executing generated scripts in Python and Fiji
• Generating automated reports with GenAI

• Understand the fundamentals of Generative AI and its applications in communication
• Use AI in communication strategy: defining target audiences, structuring messages, choosing formats…

Half-Day 1:

Introduction to Generative AI

• History
• How it works

Applications in Communication

Module 1: Transforming Your Knowledge into a Clear and Impactful Message

• Understand different types of scientific communication
• Define your target audience
• Structure a scientific message methodically
• Practical exercises

Module 2: AI as a Time-Saver in Scientific Communication

• Structuring prompts (role-play of AI) and creating different profiles
• Creating prompts based on objectives (and previously defined target audiences)
• Practical exercise

Half-Day 2:

Applications in Communication

Module 3: Building a Scientific Communication Strategy

• Choosing the right format
• Empowering texts, infographics, and visuals with AI
• Practical exercise

Module 4: Practical Case

• Create your first scientific publications, communication plan, and resource bank with the help of AI

Knowing how to manipulate data and extract information from it with R.

Please contact us if you would like to have this training course held in English.

Understand the data science ecosystem and know the tools related to the realization of a data science project.

DAY 1

• The unix environment, interacting with a shell, open source tools (sed, awk, grep, jq, csvkit, etc.), R and Python, SQL and NoSQL
• Revision control and collaborative work with Git
• The methodology for managing a data science project
• Software engineering fundamentals and best practices

DAY 2

• Information gathering and processing (experimental designs and clinical trials, surveys and polls, web data, open data)
• Distributed architecture and database, map-reduce, big data, Apache Spark

• Deepen your mastery of R to perform advanced statistical analyses.
• Discover and leverage generative AI tools to automate common tasks in data analysis.
• Optimize analytical processes by integrating automated workflows.
• Structure complex workflows effectively and innovatively.
• Strengthen the understanding and application of advanced statistical concepts to real and simulated cases.

• Discover the R language and software and learn the first basics of this language

R1: First steps in R

• Introduction
• Entering a command in the console
• Writing a clean, structured and commented script
• Create, modify, view and delete an object
• Manipulating different data types and data structures
• The R objects: vectors, factors, arrays, lists, data frames, functions

R2: Import, control and export data arrays

• View and edit working directory
• Import data contained in a .csv file
• Check the types of its variables and modify them if needed
• Categorical variables: factors
• Controlling for missing data
• Exporting a data table to a .csv file

R3: Numerical valuation of data

• Manipulating your dataset (selecting variables, rows …)
• Numerical valuation: getting to know the dataset, summarizing and quantifying the information
• Descriptive statistics, counts, pivot tables
• Data aggregation (statistics by group of observations)

R4: Graphical valuation of data

• Creating basic graphs: histogram, scatterplot, box plot, bar chart, pie chart
• Changing the various basic chart options (color, title, point and line type, size, …)
• Add elements to a chart (points, lines, segments, legends, …)
• Save a graph

• Understand the fundamentals of generative AI and its applications.
• Master AI-assisted qualitative analysis techniques.
• Use AI for analyzing and presenting qualitative data.

• Discover the general principles of Deep Learning and Generative Artificial Intelligence, and benefit from the potential of Generative AI tools.
• Use advanced prompting techniques to meet business needs.
• Classify generative AI tools according to the media they implement (text-to-text, text-to-image, etc.), and select the right tool for a specific use case.
• Produce educational activities such as course notes, study guides, or chapter summaries to assist students in their learning.
• Design exams and quizzes based on the training content developed for students, saving time in course preparation.
• Generate scenarios or case studies for group projects, based on current topics or scientific advancements, to expand the possibilities of course facilitation (content, group workshops, etc.).
• Adapt your teaching and prepare your students for the Generative AI revolution.

Part 1: The Basics of AI and Prompt Engineering (1h30)

• Describe the general operating principle of a generative artificial intelligence
• Discover prompt engineering

Part 2: Uses of Prompt Engineering (1h30 + 30min practical work)

• Describe the main models of prompt engineering
• Share use cases in research and teaching
• Create several resources: summaries, articles, translations, presentations, etc.

Part 3: Going Further with Generative AIs – Real-world Cases & Tools (3h00 + 30min practical work)

• Experiment with creative uses of AI to generate content, brainstorm, argue
• Discover generative AI tools to simplify tasks (Chatbase, Custom GPT, Brancher.ai, Scispace, etc.). Example: create GPTs to automate the writing of references in the correct format
• Discuss challenging questions: ethics, copyright, security…

Part 1: Basics of AI and Prompt Engineering (1h30)

• Describe the general working principle of a generative artificial intelligence
• Discover prompt engineering

Part 2: Uses of Prompt Engineering (1h30 + 30min practical work)

• Describe the main models of prompt engineering
• Share use cases in the fields of research and education
• Create multiple resources: summaries, articles, translations, presentations, etc.

Part 3: Going Further with Generative AI – Case Studies & Tools (3h00 + 30min practical work)

• Experiment with creative uses of AI to generate content, brainstorm, and argue
• Discover generative AI tools to make life easier (Chatbase, Custom GPT, Brancher.ai, Scispace, etc.). Example: create a GPT to automate the formatting of references
• Discuss the challenges and upheavals of generative AI for the sector: ethics, copyrights, security…

• Understand Generative AI, its functioning, and its limitations
• Explore the applications of Generative AI and use cases for scientists
• Define a plan for integrating Generative AI into your team/laboratory and prioritize the first actions

Sequence 1:

• What is AI?
• Predictive AI vs. Generative AI
• Acceleration of AI development
• Overview of Generative AI
• LLMs, RAG, and agents
• Adoption and generational challenges
• Risks of AI
• Ethical & Legal considerations

Sequence 2:

• AI in a Laboratory

  • A lever for innovation & competitiveness
  • Use cases in laboratories
  • Choosing the right tools & providers

Starting the Integration

• What are the different ‘AI projects’?
• What are the areas for transformation?

• Discover the fundamentals of R and generative AI tools.
• Learn the basics of descriptive statistical analysis and apply them to real-world datasets.
• Learn how to automate common tasks in R using generative AI.
• Master the creation of simple visualizations and data presentation.
• Get introduced to the concepts of simple regressions and hypothesis testing.
• Generate workflows and automated reports with R Markdown.

Half-day 1: Introduction to Generative AI and Its Application in Statistical Analysis

1. Introduction to Generative AI

   • Definition and fundamental principles
   • How generative AI models work
   • Strengths and limitations of generative AI in data processing

2. How Generative AI Can Simplify Learning Statistical Tools

3. Practical Use Case for Beginners

   • Generating fake data examples to illustrate statistical concepts
   • Offering simplified explanations for basic concepts (mean, standard deviation, etc.)
   • Structuring a step-by-step approach for a simple statistical analysis

Half-day 2: First Steps with R

1. Installation and Getting Started with RStudio Interface

2. First Scripts: Introduction to Key Object Types in R

3. Introduction to Essential Packages: dplyr, tidyr, ggplot2

4. Exploring a Simple Dataset

Half-day 3: Effectively Visualizing and Analyzing Your Data

1. Descriptive Analysis: Means, medians, standard deviations, frequencies.

2. Creating Visualizations: Histograms, boxplots, bar charts.

3. Case Study: Analyzing and visualizing a real dataset.

4. Introduction to Automation: Using AI to generate simple visualization scripts.

Half-day 4: Introduction to Regression and Hypothesis Testing

1. Simple Linear Regressions: Concepts and implementation.

2. Introduction to Hypothesis Testing

3. Practical Workshop with Simulated or Real Datasets

Half-day 5: Automating Analysis with R and Generative AI

1. Prompt Engineering for Automating Analysis with R

   • Writing simple prompts to generate R code.
   • Automating key steps: data loading, cleaning, analysis, and visualization.

2. Practical Example: Creating an Automated Workflow with R and Generative AI

   • Loading a data file.
   • Summarizing data and producing simple visualizations.
   • Generating an automated report (HTML or PDF) using R Markdown.

• Master more complex machine learning models, in particular ensemble methods based on bagging and boosting techniques, to use and optimize penalty models (lasso and elasticnet), to understand the bootstrap resampling technique for estimation and cross-validation, to know how to implement collaborative filtering techniques.
• At the end of this training, the participant will have a global vision of the different multivariate modeling techniques.

Day 1

• Advanced data mining:
  o DBSCAN, unsupervised data clustering algorithm
  o Manifold Learning

DAY 2

• Gaussian Mixture Modelling (GMM)
• Optimizing penalty models with Lasso and elasticnet (regression, PLS)
• Support Vector Machine (SVM)

DAY 3

• Random Forest and Gradient Boosting Machines
• Bootstraping estimation and cross-validation
• Collaborative filtering and recommendation system

• Understand the basics of machine learning and machine learning on structured data, apply standard dimension reduction and clustering methods, know how to implement a regression model by controlling overfitting and validating model predictions, understand the basics of text mining.
• At the end of this training, the participant will be able to determine the type of techniques to apply based on the questions asked and perform elaborate pre-processing to implement predictive models.

DAY 1

• Introduction to unsupervised methods:
  • Principal component analysis (PCA)
  • Automatic classification (k-means)
  • Association rules (apriori, eclat)
• Introduction to supervised methods:
  • Linear and logistic regression models with regularization (ridge regression)
  • Decision trees (regression and classification)

DAY 2

• Standard methods for implementing predictive models:
  • Feature engineering: learning to reduce the complexity of a problem,
  • Variable selection,
  • Cross-validation,
  • Calibration of a predictive model
  • Text mining and web scraping

• Describe how a Generative AI (GenAI) works in order to optimize its use in daily tasks
• Apply prompt engineering methods to use GenAI tools effectively
• Identify use cases within your professional environment

Half-Day Session:

Introduction to AIs:

• History and functioning of AI

Prompt Engineering:

• Methods and applications

Professional Use Cases:

• Hands-on practice with real-world applications

• Deepen the tools to represent and manipulate complex data, effectively use the pandas library, import data sources (CSV, JSON, XML, SQL), perform a simple or multiple linear regression model with the statmodels library, perfect your knowledge of matplotlib and know how to use seaborn or plotly..
• At the end of this training, the participant should be able to import or even merge structured or unstructured data sources, apply advanced processing on quantitative and qualitative data and build elaborate static or dynamic graphs.

DAY 1

• Advanced Data Processing:
  • The numpy library :
    • advanced functions (views, slices)
    • the interface with scipy
  • The pandas library:
    • The import of external data sources,
    • The aggregation of data,
    • The reshaping,
    • Indexing,
    • The merging of data sources
  • The statmodels library:
    • Single and multiple regression,
    • The testing of regression coefficients,
    • The diagnosis of the model,
    • Point and interval prediction
• The processing of strings, regex
• Date processing and time series management

DAY 2

• The generators, itertools, lazy evaluation
• The database interface (SQL, NoSQL)
• The Seaborn package:Advanced graphing features (trellis graphs, statistical distributions, heatmap)
• Interactive graphics with the Bokeh and Plotly packages.

• Understand how data is represented, know how to manipulate simple data structures, master the basics of the numpy and scipy libraries for numerical computation and basic statistical functions, learn the basics of graphical visualization with matplotlib.
• At the end of this training, the participant should be able to write simple analysis scripts working either with artificial data or with data sources that do not require major pre-processing. He/she will know how to implement the main statistical tests for the comparison of two samples and perform basic exploratory graphs.

Day 1

• The working environment: Python 2 and 3.x
• The presentation of the different consoles and debugging in Python:
  • Anaconda
  • Jupyter
  • Spyder
• Data types: lists, dictionaries
• Control structures
• The functions, methods and packages

DAY 2

• Data handling and cleaning:
  • numpy: Basic objects and manipulation of 2-dimensional arrays (array and numeric calculation functions, random number generators)
  • scipy: Basic functionality (scientific functions and basic statistical tests)
  • The probability distributions and univariate statistics.
    • matplotlib: basic features: scatterplot, box plot, histogram
  • Simple scripting

Learn more about the tools used to represent and manipulate complex data, discover the dplyr and data.table packages to optimise data processing, import data sources (CSV, JSON, XML, SQL), create a simple or multiple linear regression model using the {stats} package, improve your knowledge of graphs and know how to use ggplot2 or plotly.

DAY 1

• Importing external data sources with the{base}, {foreign} and {haven} packages

• The tools for optimizing data processing, {data.table} and {dplyr}:
  • Advanced data frame manipulation,
  • Data aggregation,
  • The reshaping,
  • Indexing,
  • The merging of data sources

• The realization of a simple or multiple linear regression model with the {stats} package:
  • The simple and multiple regression,
  • The testing of regression coefficients,
  • The diagnosis of the model,
  • Point and interval prediction

DAY 2

• The processing of strings, regex
• The processing of dates and time series management
• The functional approach and lazy evaluation
• The database interface (SQL, NoSQL)
• The advanced graphical features with the {ggplot2} package:
  • Trellis charts,
  • Statistical distributions,
  • Data presentation with heatmap
• Building elaborate interactive static or dynamic graphs with the {ggvis} and {plotly} packages

• Understand how data is represented, know how to manipulate simple data structures, master basic functions in the {base} and {stats} packages for numerical computation and basic statistical functions, learn the basics of graphical visualization with the basic graphics package {graphics}.
• At the end of this training, the participant should be able to write simple analysis scripts working with either artificial data or data sources that do not require major pre-processing. He/she will know how to implement the main statistical tests for comparing two samples and perform basic exploratory graphs.

DAY 1

• The work environment:
  • Introduction to R 3.x
  • Introduction to RStudio
• The different basic data types (vector, list, data frame), control structures, simple functions
• The basic control structures of R
• The important functions and packages for data manipulation

DAY 2

• The basic features:
  • Simple numeric functions,
  • The random number generators
• The probability distributions and univariate statistics
• Scientific functions {stats} and basic statistical tests
  • Elementary statistical graphical management: scatterplot, box plot, histogram)
• The writing of simple programming scripts with R

Know how to process time series with R.

Please contact us if you would prefer to have this training course held in English.

• Learn the inherent rules of scientific article writing
• Understand how Generative AI works and master Prompt Engineering
• Use AI for article writing: assistance with structure, reformulations, translations…

Half-Day 1: The Steps of Scientific Article Writing

• Objectives of a Scientific Article and Key Guidelines
• Writing an Abstract and a Graphical Abstract
• Introduction and Bibliography
• Results (Creating Figures and Tables)
• Discussion and Conclusion

Half-Day 2:

Introduction to Generative AI

• How Generative AI Works
• Prompt Engineering Applied to Article Writing
• Ethical Considerations Around AI

Application to Article Writing

• Overview of Various AI Tools for Different Article Sections
• Using AI for Other Applications Beyond Writing (Translation, Proofreading, Statistical Analysis, etc.)
• Practical Exercises and Real-World Use Cases of AI in Scientific Writing

• Understand the fundamentals of Generative AI and its applications in communication
• Use AI in communication strategy: defining target audiences, structuring messages, choosing formats…

Half-Day 1:

Introduction to Generative AI

• History
• How it works

Applications in Communication

Module 1: Transforming Your Knowledge into a Clear and Impactful Message

• Understand different types of scientific communication
• Define your target audience
• Structure a scientific message methodically
• Practical exercises

Module 2: AI as a Time-Saver in Scientific Communication

• Structuring prompts (role-play of AI) and creating different profiles
• Creating prompts based on objectives (and previously defined target audiences)
• Practical exercise

Half-Day 2:

Applications in Communication

Module 3: Building a Scientific Communication Strategy

• Choosing the right format
• Empowering texts, infographics, and visuals with AI
• Practical exercise

Module 4: Practical Case

• Create your first scientific publications, communication plan, and resource bank with the help of AI

• Creating your Citavi project
• Organize and manage your references with Citavi
• Feed its database with new references through different exports: DOI, PDF, websites, etc.
• Cite its bibliographic references with Citavi and publish documents with Word, articles containing bibliographic references
• Exchanging and sharing references and citations from consulted sources

1. Introduction to Citavi: Theoretical Presentation (30 min.)
2. First Steps with Citavi: Discovering the interface and working on a project (creating, opening, saving), Teamwork with Citavi: applied exercises (1h30)
3. Managing the project: Adding references (manually, automatically), searching and inserting references (from Citavi, from the Internet, with the Picker): practical exercises with importing different document formats; browsing the Internet for new documents (1h30)
4. Organizing and planning: Structuring and sorting references (classification, filter, table), searching the project (in references and full text), editing references (fields, linked documents, keywords, evaluation), planning work (tasks). Presentation and practical exercises, case studies (1h)
5. Organizing the elements of knowledge: Using the Knowledge Organizer, Working on PDF (annotations), Adding personal reflections to the project (Thoughts), Linking an article with its review. Practical exercises (1h30)
6. Using the project: Using citation styles, exporting references (via clipboard, text file, spreadsheet, email), Creating a project bibliography, Writing documents with Word. Theoretical presentation and practical applications (1h)

• Creating your bibliographic database
• Organize and manage your references with EndNote
• Feed your database with new references through various exports: DOI, PDF, websites,…
• Cite your bibliographic references with EndNote and publish documents with Microsoft Word, articles containing bibliographic references
• Exchange and share your references

Please contact us if you would like to have this training course held in English.

• Deepen your mastery of R to perform advanced statistical analyses.
• Discover and leverage generative AI tools to automate common tasks in data analysis.
• Optimize analytical processes by integrating automated workflows.
• Structure complex workflows effectively and innovatively.
• Strengthen the understanding and application of advanced statistical concepts to real and simulated cases.

• Discover the general principles of Deep Learning and Generative Artificial Intelligence, and benefit from the potential of Generative AI tools.
• Use advanced prompting techniques to meet business needs.
• Classify generative AI tools according to the media they implement (text-to-text, text-to-image, etc.), and select the right tool for a specific use case.
• Produce educational activities such as course notes, study guides, or chapter summaries to assist students in their learning.
• Design exams and quizzes based on the training content developed for students, saving time in course preparation.
• Generate scenarios or case studies for group projects, based on current topics or scientific advancements, to expand the possibilities of course facilitation (content, group workshops, etc.).
• Adapt your teaching and prepare your students for the Generative AI revolution.

Part 1: The Basics of AI and Prompt Engineering (1h30)

• Describe the general operating principle of a generative artificial intelligence
• Discover prompt engineering

Part 2: Uses of Prompt Engineering (1h30 + 30min practical work)

• Describe the main models of prompt engineering
• Share use cases in research and teaching
• Create several resources: summaries, articles, translations, presentations, etc.

Part 3: Going Further with Generative AIs – Real-world Cases & Tools (3h00 + 30min practical work)

• Experiment with creative uses of AI to generate content, brainstorm, argue
• Discover generative AI tools to simplify tasks (Chatbase, Custom GPT, Brancher.ai, Scispace, etc.). Example: create GPTs to automate the writing of references in the correct format
• Discuss challenging questions: ethics, copyright, security…

Part 1: Basics of AI and Prompt Engineering (1h30)

• Describe the general working principle of a generative artificial intelligence
• Discover prompt engineering

Part 2: Uses of Prompt Engineering (1h30 + 30min practical work)

• Describe the main models of prompt engineering
• Share use cases in the fields of research and education
• Create multiple resources: summaries, articles, translations, presentations, etc.

Part 3: Going Further with Generative AI – Case Studies & Tools (3h00 + 30min practical work)

• Experiment with creative uses of AI to generate content, brainstorm, and argue
• Discover generative AI tools to make life easier (Chatbase, Custom GPT, Brancher.ai, Scispace, etc.). Example: create a GPT to automate the formatting of references
• Discuss the challenges and upheavals of generative AI for the sector: ethics, copyrights, security…

• Discover the fundamentals of R and generative AI tools.
• Learn the basics of descriptive statistical analysis and apply them to real-world datasets.
• Learn how to automate common tasks in R using generative AI.
• Master the creation of simple visualizations and data presentation.
• Get introduced to the concepts of simple regressions and hypothesis testing.
• Generate workflows and automated reports with R Markdown.

Half-day 1: Introduction to Generative AI and Its Application in Statistical Analysis

1. Introduction to Generative AI

   • Definition and fundamental principles
   • How generative AI models work
   • Strengths and limitations of generative AI in data processing

2. How Generative AI Can Simplify Learning Statistical Tools

3. Practical Use Case for Beginners

   • Generating fake data examples to illustrate statistical concepts
   • Offering simplified explanations for basic concepts (mean, standard deviation, etc.)
   • Structuring a step-by-step approach for a simple statistical analysis

Half-day 2: First Steps with R

1. Installation and Getting Started with RStudio Interface

2. First Scripts: Introduction to Key Object Types in R

3. Introduction to Essential Packages: dplyr, tidyr, ggplot2

4. Exploring a Simple Dataset

Half-day 3: Effectively Visualizing and Analyzing Your Data

1. Descriptive Analysis: Means, medians, standard deviations, frequencies.

2. Creating Visualizations: Histograms, boxplots, bar charts.

3. Case Study: Analyzing and visualizing a real dataset.

4. Introduction to Automation: Using AI to generate simple visualization scripts.

Half-day 4: Introduction to Regression and Hypothesis Testing

1. Simple Linear Regressions: Concepts and implementation.

2. Introduction to Hypothesis Testing

3. Practical Workshop with Simulated or Real Datasets

Half-day 5: Automating Analysis with R and Generative AI

1. Prompt Engineering for Automating Analysis with R

   • Writing simple prompts to generate R code.
   • Automating key steps: data loading, cleaning, analysis, and visualization.

2. Practical Example: Creating an Automated Workflow with R and Generative AI

   • Loading a data file.
   • Summarizing data and producing simple visualizations.
   • Generating an automated report (HTML or PDF) using R Markdown.

• Succeed in the implementation of scientific, technical and technological monitoring for a project.
• Know the watch cycle and organize your monitoring plan.
• Organize the different stages of the monitoring concretely

Please contact us if you would like to have this training course held in English.

• Learn the inherent rules of scientific article writing
• Understand how Generative AI works and master Prompt Engineering
• Use AI for article writing: assistance with structure, reformulations, translations…

Half-Day 1: The Steps of Scientific Article Writing

• Objectives of a Scientific Article and Key Guidelines
• Writing an Abstract and a Graphical Abstract
• Introduction and Bibliography
• Results (Creating Figures and Tables)
• Discussion and Conclusion

Half-Day 2:

Introduction to Generative AI

• How Generative AI Works
• Prompt Engineering Applied to Article Writing
• Ethical Considerations Around AI

Application to Article Writing

• Overview of Various AI Tools for Different Article Sections
• Using AI for Other Applications Beyond Writing (Translation, Proofreading, Statistical Analysis, etc.)
• Practical Exercises and Real-World Use Cases of AI in Scientific Writing

• Understand the fundamentals of Generative AI and its applications in communication
• Use AI in communication strategy: defining target audiences, structuring messages, choosing formats…

Half-Day 1:

Introduction to Generative AI

• History
• How it works

Applications in Communication

Module 1: Transforming Your Knowledge into a Clear and Impactful Message

• Understand different types of scientific communication
• Define your target audience
• Structure a scientific message methodically
• Practical exercises

Module 2: AI as a Time-Saver in Scientific Communication

• Structuring prompts (role-play of AI) and creating different profiles
• Creating prompts based on objectives (and previously defined target audiences)
• Practical exercise

Half-Day 2:

Applications in Communication

Module 3: Building a Scientific Communication Strategy

• Choosing the right format
• Empowering texts, infographics, and visuals with AI
• Practical exercise

Module 4: Practical Case

• Create your first scientific publications, communication plan, and resource bank with the help of AI

This course is designed for scientific researchers and anyone working in scientific communication. It will provide trainees with the means and journalistic techniques, both written and oral, to simplify their complex messages with the help of metaphors that are easier to understand and prevents from using the dry language of experts.

In a nutshell: how to formulate dynamic headlines, how to synthesize and simplify these messages, and how to popularize them so that they can be understood by the greatest number of people.

• Understand the fundamentals of generative AI and its applications.
• Master AI-assisted qualitative analysis techniques.
• Use AI for analyzing and presenting qualitative data.

• Discover the general principles of Deep Learning and Generative Artificial Intelligence, and benefit from the potential of Generative AI tools.
• Use advanced prompting techniques to meet business needs.
• Classify generative AI tools according to the media they implement (text-to-text, text-to-image, etc.), and select the right tool for a specific use case.
• Produce educational activities such as course notes, study guides, or chapter summaries to assist students in their learning.
• Design exams and quizzes based on the training content developed for students, saving time in course preparation.
• Generate scenarios or case studies for group projects, based on current topics or scientific advancements, to expand the possibilities of course facilitation (content, group workshops, etc.).
• Adapt your teaching and prepare your students for the Generative AI revolution.

Part 1: The Basics of AI and Prompt Engineering (1h30)

• Describe the general operating principle of a generative artificial intelligence
• Discover prompt engineering

Part 2: Uses of Prompt Engineering (1h30 + 30min practical work)

• Describe the main models of prompt engineering
• Share use cases in research and teaching
• Create several resources: summaries, articles, translations, presentations, etc.

Part 3: Going Further with Generative AIs – Real-world Cases & Tools (3h00 + 30min practical work)

• Experiment with creative uses of AI to generate content, brainstorm, argue
• Discover generative AI tools to simplify tasks (Chatbase, Custom GPT, Brancher.ai, Scispace, etc.). Example: create GPTs to automate the writing of references in the correct format
• Discuss challenging questions: ethics, copyright, security…

Part 1: Basics of AI and Prompt Engineering (1h30)

• Describe the general working principle of a generative artificial intelligence
• Discover prompt engineering

Part 2: Uses of Prompt Engineering (1h30 + 30min practical work)

• Describe the main models of prompt engineering
• Share use cases in the fields of research and education
• Create multiple resources: summaries, articles, translations, presentations, etc.

Part 3: Going Further with Generative AI – Case Studies & Tools (3h00 + 30min practical work)

• Experiment with creative uses of AI to generate content, brainstorm, and argue
• Discover generative AI tools to make life easier (Chatbase, Custom GPT, Brancher.ai, Scispace, etc.). Example: create a GPT to automate the formatting of references
• Discuss the challenges and upheavals of generative AI for the sector: ethics, copyrights, security…

• Be able to speak in English with confidence in front of a scientific audience
• Know how to use your body (breathing, stance, posture, gestures) to pace your speech
• Know how to project yourself into the space and attract the attention of the audience by giving authority to your speech
  Identify one’s weak and strong points with the help of other participants
• Know how to summarize information to make it more dynamic
• Know how to use your presentation as a springboard to express yourself, not a crutch
• Prepare an argument to back up your speech and answer questions quickly
• Be more fluid in English and handle questions.
Scientific communication and writing Short courses On-site courses Face-to-face English

• Describe how a Generative AI (GenAI) works in order to optimize its use in daily tasks
• Apply prompt engineering methods to use GenAI tools effectively
• Identify use cases within your professional environment

Half-Day Session:

Introduction to AIs:

• History and functioning of AI

Prompt Engineering:

• Methods and applications

Professional Use Cases:

• Hands-on practice with real-world applications

• Succeed in the implementation of scientific, technical and technological monitoring for a project.
• Know the watch cycle and organize your monitoring plan.
• Organize the different stages of the monitoring concretely

Please contact us if you would like to have this training course held in English.

Understand and know how to use the Anglo-Saxon approach to writing scientific articles. Apply what you learn in the field to your own work. Understand the importance of the reader and therefore of the clarity and precision of your message. Understand that you need to think in the language in which you are writing to make it more comprehensible. Know how to make better use of grammar and syntax in your writing to make it clearer. Master the structure of the article and therefore structure your speech better. Have a framework and a toolbox that can be reused for other articles.

Knowing how to manipulate data and extract information from it with R.

Please contact us if you would like to have this training course held in English.

• Mastery of the basic functions in order to be autonomous with Stata on the following subjects: Descriptive statistics, graphs and first estimates.

4 modules of 3.5 hours each

Stata Deb1 : Meeting the software 

• Presentation of the software environment
• How to set up a Stata session to be efficient
• The general syntax of a Stata command
• How to use the help to become autonomous
• Commented example of a Stata session to understand its possibilities
• Importing data, describing them and visualizing them: a first approach
• A first exercise

Stata Deb2: Working with your data 

• Exploring a data file: the if, by and in conditional
• Manipulating variables: creation, recoding, labels and many other tricks
• Handling data: sorting, deleting, merging, changing format and producing aggregated data
• An exercise to test yourself

Stata Deb3: Descriptive Statistics, Tables and Charts 

• Descriptive statistics
• Synthetic statistical tables
• Univariate analysis
• An introduction to analysis of variance
• Graphs with Stata
• A synthetic exercise

Stata Deb4: An introduction to regression

• Linear regression: estimation, post-estimation, diagnostics and tests
• Logistic regression: estimation, post-estimation, diagnostics and tests
• Discovering programming: loops
• Synthetic exercise (continued)

Learn how to create and analyse Factorial Design of Experiments with Design Expert.

Please contact us if you would prefer to have this training course held in English.

• Deepen your mastery of R to perform advanced statistical analyses.
• Discover and leverage generative AI tools to automate common tasks in data analysis.
• Optimize analytical processes by integrating automated workflows.
• Structure complex workflows effectively and innovatively.
• Strengthen the understanding and application of advanced statistical concepts to real and simulated cases.

• Discover the fundamentals of R and generative AI tools.
• Learn the basics of descriptive statistical analysis and apply them to real-world datasets.
• Learn how to automate common tasks in R using generative AI.
• Master the creation of simple visualizations and data presentation.
• Get introduced to the concepts of simple regressions and hypothesis testing.
• Generate workflows and automated reports with R Markdown.

Half-day 1: Introduction to Generative AI and Its Application in Statistical Analysis

1. Introduction to Generative AI

   • Definition and fundamental principles
   • How generative AI models work
   • Strengths and limitations of generative AI in data processing

2. How Generative AI Can Simplify Learning Statistical Tools

3. Practical Use Case for Beginners

   • Generating fake data examples to illustrate statistical concepts
   • Offering simplified explanations for basic concepts (mean, standard deviation, etc.)
   • Structuring a step-by-step approach for a simple statistical analysis

Half-day 2: First Steps with R

1. Installation and Getting Started with RStudio Interface

2. First Scripts: Introduction to Key Object Types in R

3. Introduction to Essential Packages: dplyr, tidyr, ggplot2

4. Exploring a Simple Dataset

Half-day 3: Effectively Visualizing and Analyzing Your Data

1. Descriptive Analysis: Means, medians, standard deviations, frequencies.

2. Creating Visualizations: Histograms, boxplots, bar charts.

3. Case Study: Analyzing and visualizing a real dataset.

4. Introduction to Automation: Using AI to generate simple visualization scripts.

Half-day 4: Introduction to Regression and Hypothesis Testing

1. Simple Linear Regressions: Concepts and implementation.

2. Introduction to Hypothesis Testing

3. Practical Workshop with Simulated or Real Datasets

Half-day 5: Automating Analysis with R and Generative AI

1. Prompt Engineering for Automating Analysis with R

   • Writing simple prompts to generate R code.
   • Automating key steps: data loading, cleaning, analysis, and visualization.

2. Practical Example: Creating an Automated Workflow with R and Generative AI

   • Loading a data file.
   • Summarizing data and producing simple visualizations.
   • Generating an automated report (HTML or PDF) using R Markdown.

• Improve your knowledge of the software and discover advanced functions of Stata
  • Declare data in different formats
  • Advanced estimation
  • Programming.

4 modules of 3.5 hours each

Stata Av1 : Advanced data management

• Treatment of alphanumeric variables
• Merging and aggregating data
• Processing of date variables and time series operators
• Advanced management of graphs

Stata Av2 : Programming with Stata

• Local and global macros
• Loops, sums and counters, temporary variables
• Programming to reproduce
• Application examples

Stata Av3: Estimation methods, tests and predictions

• Linear regression with categorical variables and interactions
• Presenting results in a synthetic table and exporting them
• Specification tests and diagnostics
• Extension to other estimation methods

Stata Av4: Introduction to panel data estimation

• Structure and visualize data
• Fixed effects model
• Random effects models
• Some extensions

• Learn to account for different sources of endogeneity in a regression model

3.5 hour module

1. The different sources of endogeneity and the consequences for the properties of estimators
2. Estimation methods
3. Synthetic ordering that allows for these different sources to be considered in a single model
4. Practical exercise

• How to master Lasso (least absolute shrinkage and selection operator) methods with Stata for prediction and/or inference on causal parameters. This regression method (typically used in high-dimensional problems) consists of penalizing the absolute size of the regression coefficients.

Please contact us if you would like to have this training course held in English.

Processing and Analyzing Time Series with Stata.

Please contact us if you would like to have this training course held in English & for the whole program.

• Become familiar with three main components of Stata: data management, data analysis, and data visualization.
• Upon completion of the course, you will be able to use Stata efficiently for data management, basic analyses and graphics.
• You will be able to create reproducible analysis, for better collaborative works and simplified follow-up analyses

Fundamentals of Using Stata (1h30 – Day1)

• Keeping organized
• Knowing how Stata treats data
• Using dialog boxes efficiently
• Using the Command window
• Saving time and effort while working
• A sample Stata Session
• Getting Help

Basic Data Management in Stata (3h30 – Day1 and Day2)

• Reading Data in Stata
  o Using and Saving Stata data files
  o Reading in datasets of various standard formats, such as those from spreadsheets or databases
• Labeling data, variables and values and setting up encoded variables
• Creating and Recoding variables in an efficient fashion
• Generating statistics within groups, and working across variables

Intermediate Data Management in Stata (2h – Day2)

• Combining datasets by adding observations and by adding variables
• Reshaping data from wide to long
• Reshaping data from long to wide
• Collapsing data across observations

Workflow (1h30 – Day3)

• Using menus and the Command window to work quickly
• Setting up Stata for your profile
• Keeping complete records of what is done inside Stata: saving dofile
• Creating reproducible analyses, which are completely documented
• Finding, installing, and removing community-contributed extensions to Stata
• Customizing how Stata starts up and where it looks for files

Analysis (3h30 – Day3 and Day 4)

• Using basic statistical commands
• Reusing results of Stata commands
• Using common postestimation commands
• Working with interactions and factor variables

Graphics (2h – Day 4)

• Introduction to graphics
• Overview of graph two-way plots
• Building up complex graphs
• Using the Graph Editor