Data Science with AI Course Content:

This Data Science with AI course integrates the principles of data science with the power of artificial intelligence (AI) to help students understand how to leverage large datasets and sophisticated AI algorithms to derive actionable insights. The course focuses on both theoretical and practical aspects, with an emphasis on real-world applications in data analysis, machine learning, and AI techniques. By the end of the course, students will be well-equipped to work with data-driven AI models, from data cleaning and preparation to the development of complex AI-based solutions.

Key Learning Objectives:

• Understand core data science concepts, tools, and techniques.
• Learn how to use machine learning and AI algorithms to solve real-world data problems.
• Gain practical experience with Python and data science libraries such as Pandas, NumPy, Scikit-learn, and TensorFlow.
• Master data preprocessing, feature engineering, and model evaluation.
• Explore the integration of AI and data science in real-world business scenarios.
• Develop a solid understanding of AI technologies, including deep learning and natural language processing (NLP).

Course Topics:

1. Introduction to Data Science and AI

• Overview of Data Science and Artificial Intelligence
• The relationship between Data Science, Machine Learning, and AI
• Key concepts: Data wrangling, predictive modeling, algorithm development
• Tools and technologies used in Data Science: Python, R, Jupyter notebooks, etc.
• Overview of AI techniques: Machine learning, deep learning, NLP, and reinforcement learning
• Introduction to the data science pipeline: Data collection, preprocessing, analysis, modeling, and evaluation

2. Mathematical and Statistical Foundations for Data Science

• Statistics: Probability distributions, hypothesis testing, p-values, confidence intervals
• Linear Algebra: Vectors, matrices, matrix operations, eigenvectors, and eigenvalues
• Calculus: Derivatives, optimization, gradient descent
• Probability Theory: Bayes’ theorem, Markov chains, and stochastic processes
• Understanding bias-variance trade-off, overfitting, and model complexity

3. Data Preprocessing and Cleaning

• Data acquisition: Collecting data from various sources (APIs, CSV files, databases)
• Data cleaning techniques: Handling missing data, data imputation, outliers
• Data transformation: Normalization, standardization, log transformation
• Encoding categorical variables: One-hot encoding, label encoding
• Feature engineering: Creating new features, polynomial features, feature scaling
• Handling imbalanced datasets: Oversampling, undersampling, SMOTE

4. Exploratory Data Analysis (EDA)

• Introduction to EDA: The importance of visualizing data
• Descriptive statistics: Mean, median, standard deviation, variance, correlation
• Data visualization using libraries: Matplotlib, Seaborn, Plotly
• Distribution analysis: Histograms, box plots, and scatter plots
• Identifying patterns and outliers in the data
• Building effective reports and dashboards for business insights

5. Machine Learning Algorithms

• Supervised Learning Algorithms:
  • Linear Regression, Logistic Regression
  • Decision Trees and Random Forests
  • Support Vector Machines (SVM)
  • K-Nearest Neighbors (KNN)
  • Naive Bayes Classifier
• Unsupervised Learning Algorithms:
  • K-Means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis (PCA)
  • Anomaly Detection techniques
• Model evaluation: Confusion matrix, cross-validation, ROC curve, Precision, Recall, F1-Score

6. Deep Learning and Neural Networks

• Introduction to Neural Networks: Structure and components (neurons, layers, weights)
• Feedforward Neural Networks (FNN) and backpropagation
• Activation functions: ReLU, Sigmoid, Tanh
• Gradient Descent and Optimization Techniques
• Convolutional Neural Networks (CNNs):
  • Image classification and object detection
  • Convolution, pooling, and feature maps
• Recurrent Neural Networks (RNNs):
  • Time-series forecasting, speech recognition
  • Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)
• Deep learning frameworks: TensorFlow, Keras, PyTorch

7. Natural Language Processing (NLP) with AI

• Text preprocessing: Tokenization, stemming, lemmatization, stop words
• Text representation techniques: Bag-of-Words (BoW), TF-IDF, Word2Vec, GloVe
• Sentiment analysis and opinion mining
• Named Entity Recognition (NER) and Part-of-Speech (POS) tagging
• Sequence-to-sequence models: Text generation, machine translation
• Introduction to transformers and BERT (Bidirectional Encoder Representations from Transformers)

8. AI for Time Series Analysis

• Understanding time series data and forecasting
• Autoregressive models (AR, MA, ARMA, ARIMA)
• LSTM and GRU models for time-series forecasting
• Anomaly detection in time-series data
• Real-world applications: Stock price prediction, weather forecasting, and sales forecasting

9. Reinforcement Learning (RL)

• Overview of Reinforcement Learning: Agents, environments, rewards, and actions
• Key concepts: Markov Decision Processes (MDP), Q-Learning, Value Iteration
• Deep Q-Networks (DQN): Combining deep learning and reinforcement learning
• Policy Gradient Methods: REINFORCE, Actor-Critic models
• Applications of RL in gaming, robotics, self-driving cars, and recommendation systems

10. AI Model Evaluation and Hyperparameter Tuning

• Performance metrics for machine learning and AI models: Accuracy, Precision, Recall, F1-score, AUC-ROC
• Cross-validation techniques: K-fold, leave-one-out
• Hyperparameter tuning: Grid search, Random search, Bayesian optimization
• Model selection: Bias-variance tradeoff, Regularization (L1, L2)
• Preventing overfitting and underfitting in AI models

11. AI Ethics and Responsible AI

• Ethical considerations in AI: Bias in data, fairness, accountability, transparency
• Privacy and data protection in AI systems (GDPR, CCPA)
• AI explainability: Interpretable machine learning models (SHAP, LIME)
• Addressing societal impacts: Automation, job displacement, AI in healthcare
• Responsible AI frameworks and guidelines

12. Deployment of AI Models

• Model deployment concepts: Serving AI models in production environments
• Building AI-powered applications using Flask, FastAPI, or Django
• Using cloud platforms: AWS, Google Cloud AI, Microsoft Azure for deployment
• Versioning models and tracking with MLflow, DVC
• Continuous integration and deployment (CI/CD) for machine learning models

13. Capstone Project

• End-to-end project: From data collection and cleaning to model deployment
• Working on a real-world business problem (e.g., customer segmentation, recommendation systems)
• Applying AI techniques to solve a practical data science challenge
• Presenting insights and results in a professional format (report, dashboard, presentation)

Who Should Take This Course:

• Aspiring data scientists, machine learning engineers, AI practitioners, and business analysts.
• Individuals with a background in programming and data analytics looking to integrate AI into their work.
• Professionals seeking to use data-driven AI models to solve real-world business problems.
• Those aiming to advance their career in AI and data science through a hands-on approach.

By the end of the course, students will be able to apply machine learning and AI techniques to solve complex data problems, develop intelligent systems, and make data-driven decisions. They’ll have practical experience working with cutting-edge AI technologies such as deep learning, reinforcement learning, and natural language processing, and be prepared for real-world applications in various industries.