Key Highlights:
<ul>
	<li>Course Validity: 1 Year</li>
	<li>Video Access: Up to 2 Views per Video</li>
	<li>Comprehensive Curriculum: Covers Core Java, Advanced Java, Spring Framework, Spring Boot, Hibernate, RESTful APIs, Frontend Technologies, and Database Integration.</li>
	<li>Industry-Relevant Projects: Hands-on projects to build real-world applications.</li>
	<li>Flexible Learning: Learn at your own pace, anytime, anywhere.</li>
	<li>Certification: Receive a course completion certificate upon finishing.</li>
</ul>

Recorded Course - Full Stack Java

IT Shaala

PHASE 1: FOUNDATION 
Module 1: Programming Fundamentals (Python) 
Variables, data types, control flow, functions, lambda expressions, object-oriented programming basics, file handling, exception handling
Module 2: Git &amp; Version Control 
Repositories, commits, branching, and collaborative workflow used by real-world data teams
Module 3: Statistics &amp; Probability 
Descriptive statistics, measures of dispersion, probability distributions, hypothesis testing, correlation and causation
PHASE 2: DATA HANDLING 
Module 4: SQL for Data Analysis 
Joins, subqueries, CTEs, window functions, aggregations, query optimization
Module 5: NumPy &amp; Pandas (Advanced Python) 
Array operations, DataFrames, merge/join/groupby, missing value treatment, datetime handling
Module 6: Data Visualization 
Matplotlib, Seaborn, Plotly, Power BI, Tableau
Module 7: Exploratory Data Analysis (EDA) 
Univariate, bivariate, and multivariate analysis, outlier detection, data storytelling 
Project: End-to-end EDA on a real-world dataset
PHASE 3: MACHINE LEARNING CORE
Module 8: Supervised Learning &mdash; Regression 
Linear Regression, Polynomial Regression, Cost Function, Gradient Descent, overfitting and underfitting 
Project: Sales Prediction
Module 9: Supervised Learning &mdash; Classification 
Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, evaluation metrics (accuracy, precision, recall, F1-score, confusion matrix) 
Projects: Retail Classification, Healthcare Classification
Module 10: Unsupervised Learning 
K-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA) 
Project: Customer Segmentation
Module 11: Model Evaluation &amp; Feature Engineering 
Cross-validation, feature scaling, encoding, feature selection, hyperparameter tuning
PHASE 4: ADVANCED AI
Module 12: Deep Learning Foundations 
Perceptron, Neural Networks, Activation Functions, Keras, Gradient Descent variants
Module 13: Specialization Track (Computer Vision / NLP / Time Series) 
Computer Vision: OpenCV, Convolutional Neural Networks, image classification 
Natural Language Processing: Tokenization, word embeddings, TF-IDF, sentiment analysis 
Time Series Analysis: Trend and seasonality, forecasting with Facebook Prophet
Module 14: Generative AI &amp; LLMs 
How Large Language Models work, prompt engineering, applying GenAI tools in real data workflows
PHASE 5: CAREER READINESS
Module 15: Capstone Project 
One end-to-end project mapped to industry domain (Fintech, Healthcare, or Product Analytics)
Module 16: Resume, LinkedIn &amp; Interview Preparation 
ATS-friendly resume building, SQL and case-study interview questions, ML theory preparation, mock interviews, HR round preparation

Recorded Course - Full Stack Java

Data Science - Recorded