Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
3 Hour(s) 34 Minute(s)
Language
English
Taught by
Data Science Academy, School of AI
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
4 views
Course Overview
Practical journey into Data Science & AI with real projects, labs, and deployment skills to launch your career
“This course contains the use of artificial intelligence.”
This comprehensive Data Science and Artificial Intelligence Mastery course is designed to take you from beginner to job-ready professional in just 100 days. Through a carefully structured curriculum, you’ll gain both theoretical knowledge and hands-on experience with the most in-demand tools and technologies in the industry.
You’ll begin by building a strong foundation in data analysis, data cleaning, and feature engineering, learning how to work with structured and unstructured data. From there, you’ll dive deep into machine learning algorithms such as regression, classification, and clustering, while also mastering advanced topics like deep learning, neural networks, and generative AI.
Every step of the way, you’ll reinforce your skills with hands-on labs, real-world case studies, and a capstone project that simulates industry challenges. You’ll also explore data visualization, model deployment with APIs (FastAPI, Flask), and MLOps concepts like monitoring and drift detection, preparing you for the realities of production environments.
By the end of this course, you’ll have a polished portfolio showcasing end-to-end AI projects, a deep understanding of tools such as Python, Pandas, Scikit-Learn, TensorFlow, PyTorch, Docker, and Streamlit, and the confidence to apply for roles like Data Scientist, Machine Learning Engineer, or AI Specialist.
This isn’t just a course—it’s a complete career preparation journey, giving you the skills, projects, and confidence to stand out in today’s competitive data-driven job market.
Course Content
- 10 section(s)
- 26 lecture(s)
- Section 1 Phase 1: Introduction to Data Science & Tools
- Section 2 Phase 2: Python for Data Science
- Section 3 Phase 3: Data Wrangling & Preprocessing
- Section 4 Phase 4: Exploratory Data Analysis (EDA)
- Section 5 Phase 5: Machine Learning Basics
- Section 6 Phase 6: Unsupervised Learning
- Section 7 Phase 7: Advanced Machine Learning
- Section 8 Phase 8: Deep Learning & AI
- Section 9 Phase 10: Capstone Project
- Section 10 Phase 11: Career Preparation
What You’ll Learn
- Clean, analyze, and prepare data for real-world machine learning and AI applications.
- Build and evaluate machine learning models for regression, classification, clustering, and recommendation systems.
- Apply deep learning techniques such as neural networks, CNNs, RNNs, and generative AI for advanced use cases.
- Work confidently with Python, Pandas, NumPy, Scikit-Learn, TensorFlow, and PyTorch to solve end-to-end problems
- Perform feature engineering, model optimization, and hyperparameter tuning to improve accuracy
- Deploy models into production using APIs (FastAPI, Flask), Docker, and Streamlit dashboards.
- Understand the basics of MLOps, including model monitoring, performance tracking, and drift detection.
- Tackle real-world projects and a capstone, gaining the confidence to showcase a complete portfolio to employers.
- Translate technical outputs into business insights and decision-making strategies.
- Prepare for career roles like Data Scientist, Machine Learning Engineer, or AI Specialist.
Reviews
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CCarmen SOGAN
It's good
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OOla-ogedengbe oreoluwa
good
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NNirmalan Kanthalingam
great teaching
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AAbdelrhman Alaa
yes but my expectetion is beteer
