Data Science Full Courses.

About Course

This course provides a practical and structured approach to learning data science using modern industry tools and workflows. Learners will build skills in Python, SQL, statistics, data analysis, data visualization, and machine learning while working with real datasets throughout the program.

The course focuses on both technical implementation and analytical thinking, helping students understand how to clean data, explore patterns, build models, and evaluate results effectively. As the program progresses, learners are introduced to machine learning, introductory deep learning concepts, and real-world project workflows. Additional modules cover version control, project organization, and deployment fundamentals commonly used in professional data science environments.

By the end of the course, students will have completed practical projects that demonstrate end-to-end data science skills.

Course Content

SQL FOR DATA SCIENCE AND ANALYTICS.

Understanding Databases and Their Role in Data Science and Analytics.

00:00
MYSQL SETUP AND PRACTICE DATABASE INSTALLATION

00:00
Database Creation and Management Fundamentals.
SELECT, FROM, WHERE Fundamentals (Part 1: Filtering and Comparison Operators)

00:00
SELECT, FROM, WHERE Fundamentals (Part 2: Filtering with Logic and Patterns)

00:00
Data Aggregation and Grouping in SQL

00:00
Inner Join in SQL: Combining Related Tables

00:00
Advanced Joins in SQL: LEFT, RIGHT, FULL AND SELF JOIN.

00:00
Subqueries in SQL: Querying within queries.

00:00
SQL Functions and Conditional Logics (Core Functions)

00:00
SQL Functions and Conditional Logic (CASE Statement)

00:00
Common Table Expression in SQL (CTEs)

00:00
Window Functions in SQL: Advanced Row Level Analytics

00:00

BUSINESS INTELLIGENCE FOR DATA ANALYTICS WITH MICROSOFT POWERBI.

FUNDAMENTALS OF PYTHON PROGRAMMING FOR DATA SCIENCE

Introduction to Jupyter Notebooks and Python for Data Analytics

00:00
Variables, Data Types, and Type Conversion in Python

00:00
Operators in Python.

00:00
Basic Data Structures I (Lists)

00:00
Basic Data Structures In Python II (Tuples)

00:00
Basic Data Structures In Python III (Dictionaries & Sets)

00:00
Control Flow in Python I: Conditional Statements

00:00
Control Flow in Python II: For Loops.

00:00
Control Flow in Python III: While Loop.

00:00
Comprehensions in Python

00:00
Functions In Python.

00:00
Exception Handling In Python.

00:00
Lambda Functions and Functional Tools: map, filter, and reduce

00:00
Object Oriented Programming (OOP) I

00:00
Object Oriented Programming (OOP) II (Encapsulation & Inheritance)

00:00
From Jupyter Notebooks to Modular Python Projects
Python Environment Management and Development Tooling
Fundamentals of Unit Testing with Pytest

EXPLORATORY DATA ANALYSIS WITH PYTHON

UNDERSTANDING STATISTICS FOR DATA SCIENCE AND ANALYTICS.

Introduction to Statistics for Data Science and Analytics
Types of Data and Measurement Scales
Descriptive Statistics: Summarizing Data
Understanding Data Distribution
Probability Fundamentals
Sampling and Data Collection.
Hypothesis Testing 1 (Introduction)
Hypothesis Testing 2 (Tests of Difference)
Hypothesis Testing 3 (Tests of Association)
Confidence Intervals
Time Series Analysis
Applying Statistics in Real-World Data Science and Analytics Workflow
Common Statistical Mistakes in Data Analysis

CLASSICAL MACHINE LEARNING

Introduction To Machine Learning.
Understanding Linear Regression

00:00
Understanding Logistic Regression.

00:00
Decision Trees.

00:00
Bagging With RandomForests Algorithm.

00:00
Gradient Boosting With XGBoost.

00:00
Cross-Validation with K-Fold and Stratified K-Fold

00:00
HyperParameter Optimization I : GridSearch

00:00
Hyperparameter Optimization II: Randomized Search

00:00
Hyperparameter Optimization III: OPTUNA

00:00
Unsupervised Learning I: K-Means Clustering

00:00
Unsupervised Learning II: DBSCAN Clustering

00:00
Dimensionality Reduction with Principal Component Analysis (PCA)

00:00

DEEP LEARNING

Introduction to Deep Learning and Neural Networks with PyTorch
PyTorch Fundamentals: Tensors, Autograd, and GPU Acceleration
Building Neural Networks with torch.nn
Training Loops and Optimization in PyTorch
Data Handling with Dataset and DataLoader
Convolutional Neural Networks for Computer Vision
Transfer Learning in Computer Vision (ResNet, EfficientNet, VGG)
Sequence Models: RNNs & LSTMs (Text)
Sequence Models II: RNNs & LSTMs (Time Series)
Transformer Architecture Fundamentals
Model Evaluation, Regularization, and Debugging Deep Learning Models
End-to-End Deep Learning Project with PyTorch: Vision
End-to-End Deep Learning Project with PyTorch: Text

MODEL DEPLOYMENT & MLOPS

Introduction to MLOps and Model Deployment Lifecycle
Orchestration and Experiment Tracking with MLflow, ZenML, and Weights & Biases
From Notebook to Production: Structuring ML Projects for Deployment
Model Serialization and Packaging for Deployment
Building ML APIs with FastAPI
Interactive ML Applications with Streamlit
Git and GitHub for Version Control and Collaboration
CI/CD Pipelines for Machine Learning Systems
Model Monitoring, Logging, and Observability with Pydantic Logfire
Scalable Model Serving and End-to-End Deployment Architecture

CAPSTONE PROJECT

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About Course

Course Content

SQL FOR DATA SCIENCE AND ANALYTICS.

Understanding Databases and Their Role in Data Science and Analytics.

MYSQL SETUP AND PRACTICE DATABASE INSTALLATION

Database Creation and Management Fundamentals.

SELECT, FROM, WHERE Fundamentals (Part 1: Filtering and Comparison Operators)

SELECT, FROM, WHERE Fundamentals (Part 2: Filtering with Logic and Patterns)

Data Aggregation and Grouping in SQL

Inner Join in SQL: Combining Related Tables

Advanced Joins in SQL: LEFT, RIGHT, FULL AND SELF JOIN.

Subqueries in SQL: Querying within queries.

SQL Functions and Conditional Logics (Core Functions)

SQL Functions and Conditional Logic (CASE Statement)

Common Table Expression in SQL (CTEs)

Window Functions in SQL: Advanced Row Level Analytics

BUSINESS INTELLIGENCE FOR DATA ANALYTICS WITH MICROSOFT POWERBI.

Introduction to Business Intelligence and Power BI

Data Wrangling In PowerBI With PowerQuery

Data Modeling and Relationships

Data Analysis Expression (DAX)

Data Visualization in Power BI

Building Interactive Dashboards

Advanced Reporting and Insights.

End-to-End Power BI Project.

FUNDAMENTALS OF PYTHON PROGRAMMING FOR DATA SCIENCE

Introduction to Jupyter Notebooks and Python for Data Analytics

Variables, Data Types, and Type Conversion in Python

Operators in Python.

Basic Data Structures I (Lists)

Basic Data Structures In Python II (Tuples)

Basic Data Structures In Python III (Dictionaries & Sets)

Control Flow in Python I: Conditional Statements

Control Flow in Python II: For Loops.

Control Flow in Python III: While Loop.

Comprehensions in Python

Functions In Python.

Exception Handling In Python.

Lambda Functions and Functional Tools: map, filter, and reduce

Object Oriented Programming (OOP) I

Object Oriented Programming (OOP) II (Encapsulation & Inheritance)

From Jupyter Notebooks to Modular Python Projects

Python Environment Management and Development Tooling

Fundamentals of Unit Testing with Pytest

EXPLORATORY DATA ANALYSIS WITH PYTHON

Numpy Fundamentals

Introduction to Pandas for Data Manipulation

Data Cleaning and Preparation with Pandas.

Fundamentals of Data Visualization with Matplotlib, Seaborn, and Plotly

Advanced Visualization with Matplotlib, Seaborn and Plotly.

Complete EDA Workflow Project

UNDERSTANDING STATISTICS FOR DATA SCIENCE AND ANALYTICS.

Introduction to Statistics for Data Science and Analytics

Types of Data and Measurement Scales

Descriptive Statistics: Summarizing Data

Understanding Data Distribution

Probability Fundamentals

Sampling and Data Collection.

Hypothesis Testing 1 (Introduction)

Hypothesis Testing 2 (Tests of Difference)

Hypothesis Testing 3 (Tests of Association)

Confidence Intervals

Time Series Analysis

Applying Statistics in Real-World Data Science and Analytics Workflow

Common Statistical Mistakes in Data Analysis

CLASSICAL MACHINE LEARNING

Introduction To Machine Learning.

Understanding Linear Regression

Understanding Logistic Regression.

Decision Trees.

Bagging With RandomForests Algorithm.

Gradient Boosting With XGBoost.

Cross-Validation with K-Fold and Stratified K-Fold

HyperParameter Optimization I : GridSearch

Hyperparameter Optimization II: Randomized Search

Hyperparameter Optimization III: OPTUNA

Unsupervised Learning I: K-Means Clustering

Unsupervised Learning II: DBSCAN Clustering

Dimensionality Reduction with Principal Component Analysis (PCA)

DEEP LEARNING