Module 1: Introduction to Python and Data Management

During the first week, you will dive into the world of Python programming and learn how to effectively manage and manipulate data. This foundational knowledge is essential for your journey in data science. We will also explore data visualization techniques to present insights effectively.

Key topics covered:

• Introduction to Python programming
• Variables, data types, and control structures
• Data management with Pandas library
• Exploratory data analysis
• Introduction to data visualization using Matplotlib

Project 1: Exploratory Data Analysis. Apply Python and Pandas to load and explore a dataset, perform data cleaning, and generate meaningful visualizations.

Module 2: Mathematics and Probability for Data Science

In the second module we will strengthen your mathematical and statistical foundations, providing you with the tools needed to understand and implement various data science algorithms. You will gain insights into probability theory and hypothesis testing.

Key topics covered:

• Linear algebra for data science
• Calculus essentials for optimization algorithms
• Probability distributions and statistical inference
• Hypothesis testing and p-values
• Correlation and regression analysis

Project 2: Statistical Analysis. Apply statistical concepts to analyze a dataset, perform hypothesis testing, and draw meaningful conclusions.

Module 3: Classical Machine Learning Techniques

During this module you will dive into classical machine learning algorithms. We will explore the theory behind these algorithms and their practical implementations. You will gain a solid understanding of supervised and unsupervised learning techniques.

Key topics covered:

• Supervised learning: Linear regression, logistic regression, decision trees, and random forests
• Unsupervised learning: Clustering algorithms (k-means, hierarchical clustering)
• Model evaluation and validation techniques

Project 3: Predictive Modeling. Develop a predictive model using regression or classification algorithms to make accurate predictions on a given dataset.

Module 4: Deep Learning and Neural Networks with TensorFlow

In the fourth module we will delve into the exciting field of deep learning and its applications. You will gain a strong foundation in neural networks, TensorFlow, and key optimization techniques.

Key topics covered:

• Introduction to deep learning and neural networks
• TensorFlow basics and building neural networks
• Gradient descent and backpropagation algorithms
• Fine-tuning and optimizing neural networks

Project 4: Image Classification with Neural Networks. Build a neural network model using TensorFlow to classify images from a given dataset.

Module 5: Convolutional Neural Networks (CNN) and Computer Vision

During the fifth module we will focus on convolutional neural networks (CNNs) and their applications in computer vision tasks. You will learn to extract meaningful features from images and leverage CNNs for tasks like image classification, object detection, and segmentation.

Key topics covered:

• Introduction to convolutional neural networks (CNNs)
• CNN architecture and layers
• Image classification with CNNs
• Object detection and localization
• Image segmentation

Project 5: Object Detection with CNNs. Develop an object detection system that can detect and localize objects within an image using CNN-based techniques.

Module 6: Natural Language Processing (NLP) and Text Analysis

In this module we will explore the field of natural language processing (NLP) and its applications in text analysis. You will learn techniques to process and analyze textual data, perform sentiment analysis, and build models for text classification.

Key topics covered:

• Introduction to natural language processing (NLP)
• Text preprocessing and tokenization
• Sentiment analysis
• Text classification techniques
• Word embeddings and language models

Project 6: Sentiment Analysis. Develop a sentiment analysis model using NLP techniques to analyze and classify the sentiment of textual data.

Module 7: Advanced Deep Learning Techniques

In this module you will be introduced to advanced deep learning techniques including generative models such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders). You will learn how to generate new content and explore unsupervised learning with these models.

Key topics covered:

• Introduction to generative models
• Generative Adversarial Networks (GANs)
• Variational Autoencoders (VAEs)
• Unsupervised learning with generative models

Project 7: Image Generation with GANs. Implement a GAN model to generate new images based on a given dataset, showcasing the ability to create realistic synthetic data.

Final Project and Portfolio Showcase

In the final module you will work on an independent final project where you can apply the knowledge and skills acquired throughout the course. This project will serve as a centerpiece in your portfolio, demonstrating your ability to solve real-world data science problems.

Key topics covered:

• Project planning and scoping
• Data exploration and preprocessing
• Model development and evaluation
• Documentation and presentation of the project
• Portfolio development for job readiness

Final project.

Develop a complete data science project from start to finish, incorporating various techniques and methodologies learned throughout the course. The project should demonstrate your ability to tackle real-world data challenges and deliver actionable insights.

Note: Each week will include additional resources, readings, coding exercises, and assessments to reinforce learning and provide feedback to students.