ADVANCED PROGRAMME IN DATA SCIENCE
MASTER PROGRAM​
The Data Scientist Master’s Program propels your data science career, providing worldclass training and essential skills. Achieve expertise in data science through a deep exploration of data interpretation nuances, mastery of machine learning, and the acquisition of powerful programming skills, elevating your career to new heights.
Introduction
- Overview of Data Science
- Importance and Applications
- Skills Required
- Tools and Technologies
Mathematics for Data Science
- Algebra
- Vectors and
- Matrices
- Linear Transformations
- Eigenvalues and Eigenvectors
- Statistics
- Descriptive
- Statistics
- Inferential
- Statistics
- Hypothesis Testing
- Multivariate Calculus
- Partial Derivatives
- Gradient, Hessian
- Optimization Techniques
Programming Fundamentals
- Introduction to Python
- Programming Language
- Variables, Data Types, and Operators
Control - Structures: Loops and Conditionals
- Functions and Modules
- OPPs Concepts
- Concepts with
- Python Threading and multi-Threading
- String and data manipulation
- File handling
Data Scarping and AI Data Collection - Handling Data with Python
- Libraries:NumpyPandas
Data Base Concept
- SQL
1. My-SQL - SQL Statements for (DML, DDL, TCL, DQL)
1. No – SQL - Concept
1. Query Statement - Connection With Python
Data Visualization
- Introduction to Data Visualization
- Data Visualization Libraries:
- Matplotlib
- Seaborn
Data Preprocessing and Cleaning
- Data Cleaning Techniques
- Handling Missing Data
- Data Transformation
- Feature Engineering
Exploratory Data Analysis (EDA)
- Overview of EDA
- Descriptive Statistics
- Data Visualization for EDA
- Insight Generation
Introduction to Machine Learning
- Basics of Machine Learning
- Types of Machine Learning:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Evaluation Metrics
SUPERVISED LEARNING ALGORITHMS
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forests
- Support Vector Machines (SVM)
UNSUPERVISED LEARNING ALGORITHMS
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- T-Distributed Stochastic Neighbor Embedding (t-SNE)
MODEL EVALUATION AND SELECTION
- Cross-Validation
- Hyperparameter Tuning
- Model Evaluation Techniques
- Model Selection Criteria
INTRODUCTION TO DEEP LEARNING
- Basics of Neural Networks
- Activation Functions
- Forward Propagation
- Backpropagation
DEEP LEARNING FRAMEWORKS
- Introduction to TensorFlow and Keras
- Building Neural Networks with TensorFlow/Keras
- Training and Evaluation
ADVANCED TOPICS IN DEEP LEARNING
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Transfer Learning
- Generative Adversarial Networks (GANs)
NATURAL LANGUAGE PROCESSING (NLP)
- Introduction to NLP
- Text Processing
- Sentiment Analysis
- Named Entity Recognition (NER)
GENERATIVE AI
- Introduction to Generative AI
- Understanding Generative AI
- Introduction to Models like GPT, DALL-E, Midjourney, Sora (and others)
- Capabilities and Limitations
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EXPLORING THE POTENTIAL OF GENERATIVE AI
- Using APIs of Large Models (e.g., OpenAI)
ENHANCING APPLICATIONS WITH ADVANCED TECHNIQUES
- Advanced Interaction with Generative Models
- Prompt Engineering Techniques and Fine-Tuning
- Retrieval-Augmented Generation (RAG)
BIG DATA AND SCALABILITY
- Introduction to Big Data
- Hadoop and MapReduce
- Apache Spark
- Distributed Computing
MODEL DEPLOYMENT AND PRODUCTION
- Deployment Strategies
- Containerization (Docker)
- Cloud Services (AWS, GCP, Azure)
- Monitoring and Maintenance
ETHICAL AND LEGAL CONSIDERATIONS
- Bias and Fairness in Machine Learning
- Privacy Concerns
- Regulatory Compliance (GDPR, HIPAA)
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CAPSTONE PROJECT
- Real-world Data Science Project
- End-to-End Implementation
- Presentation and Documentation
Learning Approach
- Â Engage in real-world projects that mimic the challenges faced by data science professionals.
- Â Work in teams to solve complex problems and share knowledge.
- Â Learn from industry experts and seasoned data scientists.
- Â Balance your learning with professional commitments through part-time or online study options.
Why Choose This Program?
- Â Designed in collaboration with industry leaders to ensure relevance and applicability.
- Â Access to career services including resume workshops, mock interviews, and job placement assistance.
- Join a community of data science professionals and alumni across various industries.
- Â Learn using the latest tools and technologies in data science.
Data science is a multidisciplinary field that combines statistical analysis, computer science, and domain knowledge to extract insights from data. The Advanced Programme in Data Science is designed for professionals seeking to deepen their understanding of data-driven decision-making and to master the tools and techniques used in the field.
The Advanced Programme in Data Science is designed for professionals looking to deepen their expertise in data analytics, machine learning, and data engineering. This program equips participants with advanced skills to handle complex data-driven challenges in various industries.