Course
Data Science Bootcamp
Master modern Data Science with Python, Machine Learning, Deep Learning, and Generative AI through hands-on projects and real-world datasets.
Who this is for
Designed for individuals with basic programming or data analytics knowledge who want to advance into Data Science and Artificial Intelligence. Suitable for students, data analysts, software developers, engineers, researchers, and professionals looking to build predictive models, analyze complex datasets, and develop intelligent applications using modern AI technologies.
The Data Science Bootcamp is an advanced, project-based program that builds on data analytics fundamentals and prepares you to solve complex business problems using artificial intelligence. You will strengthen your Python programming skills, work with real datasets, perform exploratory data analysis, and develop machine learning models for prediction, classification, clustering, and forecasting. As the program progresses, you will move into deep learning with neural networks, computer vision, and natural language processing using modern frameworks such as PyTorch. You will also explore the latest developments in Generative AI, including large language models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG), fine-tuning concepts, and practical AI applications. Throughout the bootcamp, you will complete hands-on projects, evaluate and optimize models, and learn industry best practices for deploying AI solutions. By the end of the program, you will have a portfolio of real-world projects demonstrating your ability to build intelligent, data-driven applications.
What you'll learn
SQL and Data Visualization
data querying, aggregation, dashboards, and analytical reporting.
Python for Data Science
NumPy, Pandas, data cleaning, preprocessing, EDA, and visualization.
Feature Engineering
missing values, scaling, encoding, dimensionality reduction, and feature extraction.
Machine Learning
regression, classification, clustering, anomaly detection, and model selection.
Model Evaluation
cross-validation, metrics, hyperparameter tuning, overfitting, and ensemble methods.
Time Series Analysis
trend, seasonality, moving averages, forecasting, and ARIMA basics.
24 weeks
