Data is at the heart of modern business decision-making, but in today's AI-driven world, Data Scientists need more than traditional analytics skills alone. Organizations are increasingly looking for professionals who can combine Data Science, Machine Learning, Python, and Generative AI to solve real business problems and deliver measurable value. In this hands-on course, you'll learn the complete Data Science lifecycle, from translating business questions into analytical problems, to exploring data, building predictive models, communicating insights, and leveraging modern AI tools. Along the way, you'll discover how Generative AI is transforming the role of the Data Scientist and learn practical ways to use AI assistants to accelerate coding, analysis, visualization, and model interpretation. Using Python and industry-standard libraries such as Pandas, Matplotlib, Seaborn, and Scikit-Learn, you'll build real-world solutions including customer churn models, recommendation systems, customer segmentation models, predictive forecasting models, and social network analyses. You'll also explore emerging topics shaping the future of the profession, including Foundation Models, GPTs, Retrieval-Augmented Generation (RAG), embeddings, AI agents, synthetic data, Explainable AI, and Responsible AI. Through practical exercises, guided labs, and AI-assisted challenges, you'll gain hands-on experience applying Data Science and Machine Learning techniques to realistic business scenarios. By the end of the course, you'll understand not only how Data Science is practiced today, but how it is evolving in the Age of AI. Introduction to AI, Data Science & Machine Learning with Python Benefits In this course, you will: Understand the role of the modern Data Scientist and how Machine Learning, Generative AI, and AI-assisted workflows fit into the Data Science lifecycle Translate business questions into Machine Learning and AI solutions that support data-driven decision-making Use Python, Pandas, and AI assistants to acquire, explore, analyze, and visualize data Learn how Generative AI can accelerate coding, data preparation, visualization, reporting, model interpretation, and analytical workflows Apply Exploratory Data Analysis (EDA) techniques to uncover patterns, assess data quality, detect bias, and evaluate model readiness Explore contemporary AI concepts including Foundation Models, GPTs, embeddings, Retrieval-Augmented Generation (RAG), synthetic data, and AI agents Build predictive models using Linear Regression, Logistic Regression, Decision Trees, Naïve Bayes, and Neural Networks, while learning how Generative AI can assist model development and interpretation Segment customers using Clustering, discover purchasing patterns using Association Rules, and build Recommendation Systems that support personalization and business growth Analyze relationships between people, products, and organizations using Social Network Analysis, graph analytics, and modern AI applications Understand the importance of Responsible AI, Explainable AI, fairness, governance, and the future of Data Science in the Age of AI Test your knowledge with an end-of-course assessment Training Prerequisites None.
Related
Related
Related