Data science with AI

Index:

• Introduction to Data Science and AI:

• Overview of data science lifecycle and AI applications.
• Understanding the role of data scientists and AI engineers in industry.
• Ethical considerations and societal impacts of AI and data-driven decision-making.

• Data Exploration and Visualization:

• Exploratory data analysis (EDA) techniques: summarizing data, identifying patterns, and outliers.
• Data visualization using libraries like Matplotlib, Seaborn, and Plotly.
• Interactive dashboards for data exploration and communication.

• Statistical Analysis and Machine Learning Basics:

• Fundamentals of statistics: probability distributions, hypothesis testing, and regression analysis.
• Introduction to supervised and unsupervised learning algorithms.
• Hands-on exercises using Python libraries (NumPy, Pandas) for data manipulation and analysis.

• Machine Learning Algorithms:

• Linear regression, logistic regression, decision trees, and ensemble methods.
• Clustering algorithms (K-means, hierarchical clustering) for unsupervised learning.
• Model evaluation metrics: accuracy, precision, recall, F1-score, and ROC curves.

• Feature Engineering and Selection:

• Techniques for transforming and selecting features in machine learning models.
• Handling missing data, encoding categorical variables, and scaling features.
• Dimensionality reduction methods (PCA, t-SNE) for high-dimensional data.

• Deep Learning Fundamentals:

• Introduction to artificial neural networks (ANNs) and deep learning architectures.
• Building and training deep learning models using TensorFlow/Keras or PyTorch.
• Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.

• Natural Language Processing (NLP) and Text Mining:

• Processing and analyzing textual data with NLP techniques.
• Tokenization, text preprocessing, sentiment analysis, and named entity recognition (NER).
• Building NLP models using libraries like NLTK, SpaCy, and Transformers.

• AI Applications and Case Studies:

• Real-world applications of AI in industries such as healthcare, finance, and e-commerce.
• Case studies on implementing AI solutions for predictive analytics and recommendation systems.
• Best practices and challenges in deploying AI models in production environments.

• Ethics and Responsible AI:

• Understanding biases and fairness in AI algorithms.
• Ethical considerations in AI model development and deployment.
• Regulatory frameworks and guidelines for ethical AI practices.

• Capstone Project and Practical Applications:

• Applying data science and AI techniques to solve a real-world problem.
• Designing and implementing an end-to-end AI solution from data preprocessing to model deployment.
• Presenting findings and recommendations based on project outcomes.