Month 1 - Python Programming and Basic Data Analysis:

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Week 1

Introduction to Python Programming and Python Basics (using cheat sheets for syntax):

• Basics of Python: Syntax, data types, and basic operations

• Control structures: Loops, conditionals, and error handling

• Functions and modules: Creating reusable code blocks

Intermediate Python (OOP, functional programming):

• Object-Oriented Programming (OOP): Classes, objects, inheritance, and polymorphism

• Functional Programming Concepts: Lambda functions, higher-order functions

• Working with data: File I/O, handling CSV, JSON, and XML formats

Week 2

Data Analysis using Python (Numpy, Pandas):

• Numpy for numerical operations

• Pandas for data manipulation and analysis

• Data cleaning and preprocessing techniques

Visualization Basics (Matplotlib, Seaborn):

• Introduction to Matplotlib and Seaborn for data visualization

• Creating various types of plots and charts

• Basic principles of effective data visualization

Week 3

Introduction to SQL and No-SQL databases:

• SQL Basics: Queries, joins, and database operations

• Introduction to No-SQL Databases: Concepts of MongoDB and basic operations

• Data modeling and database design

Week 4

Group project to modelize, understand and use python code and algorithms to solve tasks:

• Hands-on exercises and mini-projects throughout the month

• Weekly assignments to reinforce learning

• Group project: Basic data analysis and visualization project

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Month 2 - Advanced Data Analytics and Machine Learning:

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Week 1

Advanced Python (complex data structures, algorithms):

• Advanced data structures in Python

• Algorithm design and analysis

• Debugging and performance optimization in Python

Probability and Statistics:

• Probability theory and statistical inference

• Descriptive and inferential statistics

• Hypothesis testing and regression analysis

Week 2

Machine Learning Fundamentals Concepts (gradient descent, matrix/vector algebra, function analysis, derivatives):

• Overview of machine learning concepts

• Gradient descent, matrix/vector algebra, function analysis

• Supervised vs. unsupervised learning

Machine Learning with Python (Scikit-learn):

• Implementing various machine learning algorithms

• Model evaluation and tuning

• Feature selection and dimensionality reduction techniques

Week 3

Introduction to Business Intelligence Tools: Power BI for data visualization:

• Basics of Power BI for data visualization and business analytics

• Creating dashboards and interactive reports

• Data integration from various sources

Week 4

Group projects to modelize, understand and analyze a machine learning problem:

• Weekly hands-on labs and exercises

• Case studies on machine learning applications

• Group project: Developing a machine learning model for a real-world problem

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Month 3 - Deep Learning, MLOps, and Project Development:

Week 1

Introduction to Deep Learning (Keras, PyTorch)

Advanced Deep Learning Concepts (using Hugging Face):

• Multimédia (image processing, sound processing, text processing (NLP))

• Graph processing

• Time series analysis

Week 2

MLOps fundamentals with AWS:

• Basic principles of MLOps and its role in AI project lifecycle

• Utilizing AWS services for model training, deployment, and monitoring

Automation Tools: Zapier and n8n:

• Introduction to automation tools and their significance in workflow optimization

• Practical sessions on setting up and using Zapier and n8n for automating tasks

• Integration of these tools with AI and data analytics projects

Week 3

Responding to Tenders and Technical Proposals:

• Understanding the process of bidding for projects and tenders

• Standards and best practices in preparing and submitting proposals

• Strategies for effectively addressing client requirements in technical proposals

Introduction to Web Technologies and Streamlit for AI Applications:

• Basics of web development: HTML, CSS, JavaScript

• Creating interactive web applications using Streamlit

• Integrating AI models with web interfaces through APIs

• Hands-on project: Developing a simple AI-powered web application

Week 4

Project Work: Group projects focusing on data analysis, client needs assessment, and technical proposal writing

Business Case Studies: AI and data analysis in business contexts

Capstone Project and Presentation:

• Group projects focusing on real-world AI solutions, incorporating elements learned throughout the course

• Emphasis on business analysis, technical solution design, and proposal writing

• Final presentation to faculty and industry professionals