Master’s in Data Science

• Basics of Python programming
• Data structures and algorithms
• Introduction to libraries (NumPy, Pandas, Matplotlib, Seaborn)
• Data manipulation and visualization techniques

• Linear Algebra and Calculus for Machine Learning
• Linear Algebra and Calculus for Machine Learning
• Probability distributions, hypothesis testing, p-values
• Understanding Bayes' Theorem and its application in Data Science

• Data cleaning and handling missing values
• Feature selection and engineering
• Data normalization, scaling, and encoding
• Text Preprocessing and Tokenization

• Advanced plotting techniques (Matplotlib, Plotly, Seaborn)
• Interactive dashboards (Dash, Streamlit)
• Visualizing large datasets
• Storytelling with data

• Advanced probability theory and its application
• ANOVA, Chi-square tests
• Confidence intervals, margin of error
• A/B Testing and Statistical Experimentation

• Introduction to Machine Learning (ML)
• Linear Regression, Logistic Regression
• Decision Trees, Random Forest, Gradient Boosting
• Model evaluation (Accuracy, Precision, Recall, F1 Score, ROC Curve)

• Clustering (K-Means, DBSCAN, Hierarchical Clustering)
• Dimensionality reduction (PCA, LDA)
• Anomaly detection
• Association Rule Learning (Apriori Algorithm)

• Introduction to Artificial Neural Networks
• Convolutional Neural Networks (CNN) for image processing
• Recurrent Neural Networks (RNN), LSTM for sequential data
• Transfer Learning and fine-tuning pre-trained models

• Text mining and processing
• Sentiment analysis, Topic modeling
• Sentiment analysis, Topic modeling
• Word embeddings (Word2Vec, GloVe), Transformers (BERT, GPT)

• Fundamentals of reinforcement learning
• Markov Decision Process (MDP)
• Q-Learning and Deep Q-Networks (DQN)
• Applications of Reinforcement Learning (Gaming, Robotics, etc.)

• Hadoop, Spark, and MapReduce
• Distributed computing and parallel processing
• Working with large datasets (Data Lakes, NoSQL databases)
• Introduction to cloud computing (AWS, Azure)

• Understanding time series data and trends
• ARIMA, Exponential Smoothing, and other forecasting techniques
• LSTM networks for time series forecasting
• Evaluating forecast models

• Grid Search, Random Search, and Bayesian Optimization
• Cross-validation techniques
• Model tuning and performance improvement
• Ensemble methods and stacking

• Ethical concerns in data collection, analysis, and model deployment
• Bias in data and
• Privacy concerns and Data Protection regulations (GDPR, CCPA)
• Fairness and Transparency in AI models

• Real-world project where students apply data science methodologies to solve complex business problems.
• In-depth research on a problem, with opportunities for students to work on an industry collaboration.
• Final project report and presentation.

• Image classification, object detection, segmentation
• CNN architectures: AlexNet, ResNet, VGG
• OpenCV and TensorFlow for Computer Vision tasks

• Sequence-to-sequence models
• Transformer-based architectures (GPT, BERT)
• Building and deploying NLP models in production

• Predictive analytics for healthcare data
• AI applications in medical imaging
• Data-driven decision-making for healthcare management

• Applying data science to public policy and social issues
• Predicting crime, poverty, and other social phenomena
• Case studies and real-world applications

By the end of the program, students will:

• Develop a strong understanding of data science techniques and their real-world applications.
• Gain hands-on experience with data preprocessing, analysis, and visualization.
• Be equipped to build and deploy machine learning models, including deep learning and reinforcement learning applications.
• Work on real-world data science problems through industry collaborations and internships.
• Gain a comprehensive understanding of the ethical challenges in data science and AI.