Master the art of extracting insights from data and building intelligent systems
Data Science and Machine Learning is the field at the intersection of statistics, programming, and domain expertise that transforms raw data into actionable insights and intelligent systems. This track will take you from the fundamentals of data analysis to advanced machine learning algorithms and deep learning models.
You'll learn how to collect, clean, and process data, build predictive models, evaluate their performance, and deploy them into production environments. By the end of this track, you'll have a portfolio of projects showcasing your ability to solve complex problems using data-driven approaches and AI technologies.
Your step-by-step journey to becoming a data scientist and ML engineer
Master the essential tools and concepts for working with data, including programming, statistics, and data manipulation.
Learn how to analyze datasets, extract meaningful patterns, and communicate insights through effective visualizations.
Master the core algorithms and techniques of machine learning to build predictive models and automated systems.
Explore cutting-edge techniques in artificial intelligence, including neural networks and natural language processing.
Learn from industry-leading instructors and platforms
Learn how to use NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, and other libraries in this comprehensive data science and machine learning course.
Learn to create machine learning algorithms in Python and R, master supervised and unsupervised learning, and solve real-world business cases.
Andrew Ng's comprehensive program covering neural networks, improving deep learning algorithms, structuring ML projects, CNNs, and sequence models.
Master data science techniques including machine learning, visualization, text analysis, and social network analysis with Python.
Learn to build and train neural networks with TensorFlow for computer vision, NLP, time series forecasting, and more.
Master SQL fundamentals and learn how to perform complex queries to extract insights from databases for data analysis.
Harvard's comprehensive program teaching probability, statistics, visualization, machine learning, and data wrangling with R.
Learn to apply computer vision techniques using PyTorch to analyze images, implement CNNs, and build applications with real-world impact.
Learn to build and train neural networks for sentiment analysis, text classification, word embeddings, translation, and more.
Master statistical concepts essential for data science including probability, distributions, hypothesis testing, and regression analysis.
Learn to apply big data processing techniques with Apache Spark to analyze large datasets and extract meaningful insights at scale.
A comprehensive Arabic course covering Python, data analysis, visualization, machine learning algorithms, and deep learning fundamentals.
Where this skillset can take your career
Extract insights from complex datasets and build predictive models to solve business problems at tech companies, startups, or research institutions.
Design and implement machine learning systems and deploy models into production environments to automate processes and decisions.
Advance the field of artificial intelligence by researching and developing new algorithms and approaches to solve complex problems.
Join thousands of students who have successfully transformed their careers through our data science and machine learning track.