Data Analyst Program

Course Description

Intensive training to master core data analysis skills, including data manipulation with Python and Excel, using SQL, visualization with Power BI or Tableau, and applying statistical techniques to generate insights from data.

Learning Objectives

By the end of this course, participants will be able to:

• Understand the fundamentals of data analysis: learn to manipulate, clean, and explore data.
• Master visualization tools: design interactive dashboards and clear reports.
• Gain database skills: query data with SQL and manage relational databases.
• Apply advanced analytical methods: explore descriptive statistics and predictive analysis to solve business problems.
• Deliver an end-to-end project: integrate all skills in a practical case.

Who is this course for?

The course is aimed at a broad audience, including:

• Students and recent graduates in computer science or statistics: gain practical data analysis skills and prepare for the job market.
• Career changers: those wishing to switch to a data career and develop analytics skills aligned with current needs.
• Business analysts: strengthen skills in data manipulation, reporting, and insight-driven decision-making.
• Developers or IT engineers: looking to expand into data analysis and visualizations.
• Managers and decision-makers: interested in better understanding analytics tools and techniques to interpret data for strategic decisions.

Prerequisites

No specific prerequisites are required.

Course Syllabus

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.
  Handling missing data and transforming variables.

Exploratory Data Analysis (EDA)

• Objective: Explore and understand data trends.
• Content:
  Visualization with Matplotlib and Seaborn.
  Outlier detection and pattern identification.

Advanced use of pandas and Excel

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

Databases and SQL (3 days)

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

• Objective: Learn SQL basics.
• Content:
  Writing simple queries: SELECT, WHERE, JOIN.
  Grouping and aggregations.

Day 5: Advanced SQL

• Objective: Optimize and analyze complex databases.
• Content:
  Subqueries, views, and stored procedures.
  Query optimization.

Day 6: Introduction to NoSQL databases

• Objective: Explore non-relational databases.
• Content:
  Key NoSQL concepts: JSON, key-value, documents.
  Hands-on with MongoDB or Firebase.

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

• Objective: Master interactive visualization tools.
• Content:
  Introduction to Tableau/Power BI.
  Building interactive dashboards.

Advanced visualization and storytelling

• Objective: Tell a story with data.
• Content:
  Visual storytelling techniques.
  Embedding visualizations into presentations.

Automated reporting

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

Block 4: Final project and certification prep (3 days)
Final project kickoff

• Objective: Apply knowledge to a real case.
• Content:
  Project definition and initial data exploration.
  Developing an analysis plan.

Analysis and visualization

• Objective: Build a complete analytical solution.
• Content:
  Constructing the analytical model.
  Creating dashboards and visualizations.

Presentation and validation of learning

• Objective: Prepare and present results.
• Content:
  Final project presentations.
  Feedback and certification preparation (Tableau Desktop, Power BI Analyst, etc.).

Program Highlights

• Pedagogical and modular approach: alternates theory and practice for better mastery of concepts.
• Cloud integration: strong emphasis on cloud and distributed solutions.
• Qualified instructors: trainers with concrete, real-world experience.
• Tools and learning materials: access to online resources, live demos, and real case studies.
• Accessibility: training open to all, no advanced technical prerequisites.
• Hands-on practice: complete project at the end of modules to consolidate learning.
• Industry preparation: focus on certifications and standard tools used in the workplace.

Teaching Methods and Tools Used

• Live demonstrations with data science services.
• Hands-on workshops and real case studies across various sectors (industry, retail, healthcare).
• Feedback: sharing best practices and common pitfalls in companies.
• Simulations and tools: using simulators for interactive workshops.

Assessment

• End-of-course multiple-choice quiz to test understanding of covered concepts.
• Practical case studies or group discussions to put acquired knowledge into practice.
• Ongoing evaluation during practical sessions.
• Hands-on practice: complete project at the end of modules to consolidate learning.

Normative References

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