Data Science Master Level

Curriculum covering Introduction to Data Science for Data Science Master Level. Topics include: • Data Science Fundamentals • What is Data Science? • Data Science Lifecycle • Role of Data Scientist • Data Analyst vs Data Scientist vs ML Engineer • Industry Applications of Data Science • Real-Time Data Science Workflow • Data Science Process • Problem Understanding • Data Collection • Data Cleaning • Data Analysis • Model Building • Model Deployment • Performance Monitoring • Tools Overview • Python • Jupyter Notebook • Google Colab • Anaconda • VS Code • Git & GitHub

Curriculum covering Python Programming for Data for Data Science Master Level. Topics include: • Science (Master Level) • Python Fundamentals • Python Installation & Environment Setup • Variables & Data Types • Operators • Input & Output • Conditional Statements • Loops • Functions • Advanced Python • Object-Oriented Programming • Classes & Objects • Inheritance • Exception Handling • File Handling • Regular Expressions • Python Data Structures • List • Tuple • Set • Dictionary • String Operations • Advanced Concepts • Lambda Functions • Map, Filter, Reduce • Iterators • Generators • Decorators • Libraries Covered • NumPy • Pandas • Matplotlib • Seaborn

Curriculum covering Statistics & Mathematics for for Data Science Master Level. Topics include: • Data Science • Statistics Fundamentals • Data Types • Population & Sample • Mean • Median • Mode • Variance • Standard Deviation • Probability • Probability Basics • Conditional Probability • Bayes Theorem • Probability Distribution • Statistical Analysis • Correlation • Covariance • Hypothesis Testing • Confidence Interval • P-Value • Mathematics • Linear Algebra Basics • Matrices • Vectors • Calculus Basics • Optimization Techniques

Curriculum covering Data Analytics & Data for Data Science Master Level. Topics include: • Visualization • Data Analysis Using Pandas • Data Loading • Data Cleaning • Missing Value Handling • Data Transformation • Data Filtering • Data Aggregation • Data Visualization • Charts & Graphs • Bar Charts • Line Charts • Scatter Plots • Histograms • Heatmaps • Visualization Tools • Matplotlib • Seaborn • Plotly • Dashboard Development • Power BI Basics • Data Import • Data Modeling • DAX Introduction • Interactive Dashboards

Curriculum covering SQL for Data Science (Master Level) for Data Science Master Level. Topics include: • Database Fundamentals • Database Concepts • Relational Database • SQL Architecture • SQL Queries • SELECT Statements • WHERE Conditions • ORDER BY • GROUP BY • HAVING • Advanced SQL • Joins • Subqueries • Views • Stored Procedures • Functions • Indexing • Database Management • MySQL • PostgreSQL • Data Analysis Using SQL • Business Queries • Data Extraction • Reporting

Curriculum covering Machine Learning (Master Level) for Data Science Master Level. Topics include: • Introduction to Machine Learning • AI vs ML vs Deep Learning • Machine Learning Workflow • Types of Machine Learning • Supervised Learning • Regression Algorithms • Linear Regression • Multiple Regression • Polynomial Regression • Ridge Regression • Lasso Regression • Classification Algorithms • Logistic Regression • Decision Tree • Random Forest • KNN • Naive Bayes • Support Vector Machine • Unsupervised Learning • Clustering • K-Means Algorithm • Hierarchical Clustering • DBSCAN • Model Evaluation • Accuracy • Precision • Recall • F1 Score • Confusion Matrix • ROC Curve • Feature Engineering • Feature Selection • Data Scaling • Encoding Techniques

Curriculum covering Deep Learning & Neural for Data Science Master Level. Topics include: • Networks • Deep Learning Fundamentals • Introduction to Neural Networks • Biological Neural Network • Artificial Neural Network • Neural Network Concepts • Layers • Activation Functions • Loss Functions • Optimization • Deep Learning Frameworks • TensorFlow • Keras • PyTorch Basics • Advanced Deep Learning • CNN • Image Classification • RNN • LSTM • Time Series Prediction

Curriculum covering Natural Language Processing for Data Science Master Level. Topics include: • (NLP) • NLP Fundamentals • Text Processing • Tokenization • Stop Words Removal • Stemming • Lemmatization • NLP Techniques • Bag of Words • TF-IDF • Word Embedding • NLP Applications • Sentiment Analysis • Chatbots • Text Classification • Advanced NLP • Transformers Basics • Large Language Models Introduction • Generative AI Overview

Curriculum covering Computer Vision for Data Science Master Level. Topics include: • Image Processing Basics • Digital Images • Image Features • Image Transformation • Computer Vision Techniques • Image Classification • Object Detection • Face Detection • Tools • OpenCV • TensorFlow

Curriculum covering Data Engineering Basics for Data Science Master Level. Topics include: • Data Handling • Data Collection Methods • Data Storage • Data Pipeline Concepts • Big Data Introduction • Hadoop Overview • Spark Basics • Cloud Data Platforms • AWS Data Services Overview • Azure Data Services Overview • Google Cloud Basics

Curriculum covering Generative AI & AI Tools for Data Science Master Level. Topics include: • Generative AI Fundamentals • AI Evolution • Machine Learning to Generative AI • LLM Concepts • Tools Covered • ChatGPT • Google Gemini • Microsoft Copilot • AI APIs Basics • AI Applications • AI Content Generation • AI Data Analysis • Prompt Engineering • AI Automation

Curriculum covering Data Science Deployment for Data Science Master Level. Topics include: • Model Deployment • Machine Learning Pipeline • Model Saving • API Development • Deployment Tools • Flask Basics • FastAPI Basics • Streamlit Applications • Cloud Deployment • AWS Basics • Azure ML Overview • Version Control • Git • GitHub

Curriculum covering Live Industry Projects for Data Science Master Level. Topics include:

Curriculum covering Career Preparation for Data Science Master Level. Topics include: • Resume Building • Data Science Resume