Applied Data Science With Python Full Course 2026 [Free] | Python For Data Science | Simplilearn

🔥Partnership is with E&ICT of IIT Kanpur - Professional Certificate Course in Generative AI and Machine Learning - https://www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?utm_campaign=_kA4hMNFPnU&utm_medium=DescriptionFirstFold&utm_source=Youtube 🔥Partnership is with IITM Pravartak - Advanced Executive Program In Applied Generative AI - https://www.simplilearn.com/applied-generative-ai-course?utm_campaign=_kA4hMNFPnU&utm_medium=DescriptionFirstFold&utm_source=Youtube In this video, you will dive into a comprehensive, project‑driven learning journey on Applied Data Science with Python – Full Course 2026, brought to you by Simplilearn. The session is designed for absolute beginners as well as working professionals who want to build end‑to‑end data‑science skills using Python in a practical, hands‑on way. The course starts with a quick refresher on core Python programming concepts, emphasizing syntax, data structures, and functions that are most commonly used in data analysis. From there, it smoothly transitions into data manipulation with libraries like NumPy and Pandas, where you learn how to load, clean, reshape, and explore real‑world datasets. Next, the video walks you through exploratory data analysis (EDA) and statistical thinking, teaching you how to summarize data, detect outliers, and derive meaningful insights using descriptive and inferential statistics. The course then shifts focus to data visualization, using Matplotlib and Seaborn to create clear, business‑ready charts and plots that help communicate data stories effectively. After mastering data wrangling and visualization, the video moves into machine learning fundamentals, covering key algorithms such as linear regression, logistic regression, decision trees, and ensemble methods using scikit‑learn. Throughout the course, you also get exposure to data preprocessing and feature engineering, including handling missing values, encoding categorical variables, and scal