Cursus Data analytics

Description of the training

Intensive training to master the key skills of data analysis, including data manipulation with Python and Excel, SQL usage, visualization with Power BI or Tableau, and application of statistical techniques to generate insights from data.

Objectives of training

At the end of this training, participants will be able to:

• Understanding the fundamentals of data analysis: Learning to manipulate, clean, and explore data.
• Mastering visualization tools: Design interactive dashboards and clear reports.
• Acquiring Database Skills: Knowing how to query data with SQL and manage relational databases.
• Apply advanced analytical methods: Explore descriptive statistics and predictive analysis to solve business issues.
• Carry out an end-to-end project: Integrate all skills in a practical case.

Who is this training for?

The training is aimed at a wide audience, including:

• Students and young graduates in computer science or statistics: To acquire practical skills in data analysis and prepare for the job market.
• Professionals in conversion: Those who want to reorient themselves towards a career in data and develop analytical skills adapted to current needs.
• Business Analysts: To strengthen their skills in data manipulation, reporting and decision-making based on insights.
• IT Developers or Engineers: Desirous of expanding their skills to data analysis and visualizations.
• Managers and decision makers: Interested in a better understanding of analytical tools and techniques to better interpret data in their strategic decisions.

Prerequisites

No specific prerequisites are required.

Training programme

Data Manipulation and Exploration (3 days)

• Objective: Introduction to data analysis and key tools.
• Content:
  Overview of data analysis.
  Introduction to Python for data manipulation.

• Objective: Clean and prepare data.
• Content:
  Manipulation with pandas.
  Missing data management and variable transformation.

Exploratory Data Analysis (EDA)

• Objective: To explore and understand data trends.
• Content:
  Visualization with Matplotlib and Seaborn.
  Outlet detection and pattern identification.

Advanced use of pandas and Excel

• Objective: Manage complex tasks in Excel and Python.
• Content:
  Advanced features of Excel for analysts.
  Integration between Excel and Python for workflows.

Databases and SQL (3 days)

• Objective: To understand relational databases.
• Content:
  Introduction to relational bases.
  Key concepts: tables, primary keys, relationships.

• Purpose: Learn the basics of SQL.
• Content:
  Writing simple requests: SELECT, WHERE, JOIN.
  Groupings and aggregations.

Day 5: Advanced SQL

• Objective: Optimize and analyze complex bases.
• Content:
  Sub-requests, views and stored procedures.
  Optimization of queries.

Day 6: Introduction to NoSQL Bases

• Objective: To explore non-relational bases.
• Content:
  Key concepts of NoSQL: JSON, key values, documents.
  Exploration with MongoDB or Firebase.

Block 3: Data visualization and storytelling (3 days)
Table and Power BI

• Objective: To master interactive visualization tools.
• Content:
  Introduction to Tableau/Power BI.
  Creating interactive dashboards.

Advanced visualization and storytelling

• Purpose: To tell a story from the data.
• Content:
  Visual storytelling techniques.
  Integration of visualizations into presentations.

Automated reporting

• Objective: Create automated and dynamic reports.
• Content:
  Automation with Excel and Python.
  Generating and distributing relationships with scripts.

Block 4: Final project and preparation for certifications (3 days)
Launch of the final project

• Objective: Apply knowledge to a concrete case.
• Content:
  Project Definition and Initial Data Exploration.
  Development of an analysis plan.

Analysis and visualization

• Objective: To develop a comprehensive analytical solution.
• Content:
  Construction of the analytical model.
  Creation of visualizations and dashboards.

Presentation and validation of achievements

• Objective: Prepare and present results.
• Content:
  Final presentation of the projects.
  Feedback and preparation for certifications (Tablee Desktop, Power BI Analyst, etc.).

Training assets

• Pedagogical and modular approach: Alternative between theory and practice for better assimilation of concepts.
• Cloud Integration: Strong focus on cloud and distributed solutions.
• Qualified speakers: Specialist trainers with practical experience in the field.
• Educational tools and materials: Access to online resources, live demonstrations and real-life case studies.
• Accessibility: Training is open to all, without advanced technical prerequisites.
• Implementation: Complete project from the end of the modules to consolidate the achievements.
• Preparation for Industry: Focus on standard certifications and tools used in the professional environment.

Pedagogical methods and tools used

• Live demonstrations with data science services.
• Practical workshops and real case studies in various sectors (industry, trade, health).
• Feedback: Sharing best practices and common mistakes in business.
• Simulations and tools: Using simulators for interactive workshops.

Evaluation

• End of training QCM to test the understanding of the concepts addressed.
• Practical case studies or group discussions to apply the knowledge gained.
• Ongoing evaluation during practical sessions.
• Implementation: Complete project from the end of the modules to consolidate the achievements.

Normative References

• Well-Architected Cloud Framework.
• GDPR (General Data Protection Regulation).
• ISO 27001, SOC 2 (Service Organization Control).
• NIST Cybersecurity Framework.