Training Catalog

Modular training course - NVivo Advanced

Discussing with participants on their NVivo practices and deepen the following knowledge and skills:

• Mastering the NVivo environment.
• Source management
• Case management
• Source coding
• Queries and matrices
• Documenting your analysis; memos, annotations and links to.
• Viewing and exporting
• Importing and using data from social networks and the web
• Collaborative work and coding comparison.

 

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

1 half-day of 3 hours and 6 modules of 2 hours

First ½ Day (3 hours)

• Review of the basic principles of qualitative analysis with NVivo.
  Discussion with participants on their practices.
  Reminder of previous course if needed

Six Modules

1. The generation and use of cases (2h)
2. Queries: Deepening the understanding of one’s corpus and its coding (2h)
3. Automatic queries and collaborative work (2h)
4. Documenting one’s analyses, work and visualizations (2h)
5. Working from web data (2h)
6. The literature review with Nvivo and discussion about publishing with Nvivo (2h)

 

Analysis Coaching Short courses On-site courses Short courses On-site courses Remote/Virtual Face-to-face Remote/Virtual Face-to-face English French

Modular training course - NVivo basics

• 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.

• 1 half-day of 4h
• 4 additional 2-hour modules

Training program

 

First Half Day:

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

Reminder of the basic principles in qualitative analysis

The place of qualitative analysis software in the research process

Nvivo its interface and philosophy

2. The preparation of a project (1h)

Preparation of sources, organization of the software and import of sources (textual data in word and pdf and 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 treatment of audio and video data and transcription (2h)

The different types of transcription.

Exchange between participants on their practices.

The tools to carry out a transcription efficiently

Demonstration of NVivo transcription I

Importing a transcription made outside of 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

Automatic coding of data tables

 

Module 4: Documenting your analyses, your work + Crossover matrices (2h)

Memos and annotations

Links to

Internal links

Crossover matrices to explore the links between ideas

 

Analysis Coaching English English French

Stata Lasso

• 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.

Analysis Analysis Analysis Analysis Analysis Analysis Analysis Analysis Analysis Analysis Analysis Analysis Analysis Analysis Analysis Analysis Analysis Analysis

Agile Management of scientific projects

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

Change Management

Innovation with Design thinking

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

Change Management

High-throughput sequencing and microbial ecology

• 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.

Chemistry and biology Chemistry and biology Laboratory processes and applications Laboratory processes and applications Laboratory processes and applications Laboratory processes and applications

Jump Start Signals NotebookChemistry and biology

First steps with R

• 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

 

Data Science Data Science Data Science Data Science Data Science Data Science Data Science Data Science Data Science Data Science Data Science Data Science Face-to-face Face-to-face Face-to-face Face-to-face Face-to-face Face-to-face Face-to-face Face-to-face Face-to-face Face-to-face Face-to-face Face-to-face Face-to-face Face-to-face Face-to-face Face-to-face Face-to-face Face-to-face Face-to-face Face-to-face Face-to-face Face-to-face Face-to-face Face-to-face Face-to-face Face-to-face Face-to-face Face-to-face

Time Series with R

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

Data Science Blended

Discovering Stata software: Stata Deb1 - Stata Deb4

• 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)

 

Econometrics / Finance Theoretical and applied statistics

Stata, Datetimes treatment and times series analysis (Series Temp 1 & 2)

• 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.

Econometrics / Finance

Development Environment in Data Science

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

Detailed training program

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

 

Engineering and development

DOE with Design Expert

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

Engineering and development Laboratory processes and applications Coaching Coaching Coaching Consulting Coaching

DOE with Design Expert

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

Engineering and development Laboratory processes and applications Coaching Coaching Coaching Consulting Coaching

High-throughput sequencing and microbial ecology

• 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.

Chemistry and biology Chemistry and biology Laboratory processes and applications Laboratory processes and applications Laboratory processes and applications Laboratory processes and applications

Python - Advanced

• 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.

 

Open Source

Python - basics

• 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

 

Open Source Open Source Open Source Open Source Open Source Open Source Open Source Open Source Open Source French French French French French French French French French French French French French French French French French French French French French French French French French

Citavi : bibliographic and reference management

• Creating your Citavi project
• Organize and manage your references with Citavi
• Feed its database with new references through different exports: DOI, PDF, websites,…
• Cite its bibliographic references with Citavi and publish documents with Word, articles containing bibliographic references
• Exchange and share one’s references and knowledge items

1. Introduction to Citavi: theoretical presentation (30 min)
2. Starting with Citavi: Discovering the interface and working on a project (create, open, save), Collaborating with Citavi: applied exercises (1h30)
3. Feeding the project : Adding references (manually, automatically), Searching and then inserting references (from Citavi, from the Internet, with the Picker) : concrete exercises with imports of different document formats, browsing the Internet to search for new documents (1h30)
4. Organize and plan : Structure and sort your references (ranking, filter, table) , Search in your project (in references and full text), Modify your references (fields, linked documents, keywords, evaluation), Plan your work (tasks) : presentation and practical exercises, case study (1h)
5. Enriching with knowledge elements: using the knowledge organizer, working on one’s PDFs (annotations), Adding thoughts to the project, Linking an article and its review: applied exercises (1h30)
6. Exploiting your project: Using citation styles Exporting references (clipboard, text file, spreadsheet, via email) Creating a project bibliography Writing documents with Word theoretical presentation and practical application (1h)

 

Publishing Publishing Allemand Italien

Set up a scientific and technological monitoring in an innovative project

• 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.

Publishing

Oral communication in English for scientific conferences

• 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 Scientific communication and writing Scientific communication and writing On-site courses On-site courses On-site courses On-site courses On-site courses On-site courses On-site courses On-site courses On-site courses On-site courses On-site courses On-site courses On-site courses On-site courses On-site courses On-site courses On-site courses On-site courses On-site courses On-site courses On-site courses On-site courses On-site courses On-site courses On-site courses On-site courses On-site courses On-site courses On-site courses

Writing science for publicationScientific communication and writing Short courses On-site courses Remote/Virtual Face-to-face

Discovering Stata software: Stata Deb1 - Stata Deb4

• 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)

 

Econometrics / Finance Theoretical and applied statistics

Using Stata Effectively: Data Management, Analysis, and Graphics Fundamentals

• 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

Program :

 

• 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
    • Using and Saving Stata data files
    • 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

Theoretical and applied statistics Theoretical and applied statistics Theoretical and applied statistics Theoretical and applied statistics Theoretical and applied statistics Theoretical and applied statistics Theoretical and applied statistics