Data Science Full Course 2026 | Learn Data Science In 48 Hours | Data Science Course | Simplilearn
🔥Micrososft Azure - Data Analyst Course - https://www.simplilearn.com/data-analyst-certification-course?utm_campaign=etdlNRHdg-w&utm_medium=DescriptionFF&utm_source=Youtube 🔥IITM Pravartak - Professional Certificate Program in AI-Powered Data Analytics - https://www.simplilearn.com/iitm-ai-data-analytics-program?utm_campaign=etdlNRHdg-w&utm_medium=DescriptionFF&utm_source=Youtube 🔥IIT Kanpur - Professional Certificate Course in AI-Powered Data Analytics - https://www.simplilearn.com/iitk-professional-certificate-course-data-analytics?utm_campaign=etdlNRHdg-w&utm_medium=DescriptionFF&utm_source=Youtube 🔥Michigan Engineering Professional Certificate in AI and Machine Learning - https://www.simplilearn.com/professional-aiml-program?utm_campaign=etdlNRHdg-w&utm_medium=DescriptionFF&utm_source=Youtube 🔥IIT Kanpur - Professional Certificate Course in Generative AI and Machine Learning - https://www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?utm_campaign=etdlNRHdg-w&utm_medium=DescriptionFF&utm_source=Youtube This video on Data Scientist Full Course 2026 by Simplilearn will help you learn data science from beginner to advanced level and build the skills needed for a career in data science. The course covers Python, SQL, statistics, data analysis, data visualization, machine learning, and exploratory data analysis using real-world datasets. You will also learn data preprocessing, feature engineering, model building, evaluation, and deployment techniques. Additionally, the tutorial introduces big data, deep learning fundamentals, and practical industry use cases. By the end of this course, you will have a strong understanding of the complete data science workflow and the essential skills required to become a data scientist. Following are the topics covered in the Data Science Full Course 2026: 00:00:00 - Introduction to Data Science Full Course 2026 00:03:44 - Data Science For Beginners 00:05:53 - Data Science Fundamentals 01:45:55 - Data Scientist vs Data Analyst 01:57:10 - Mathematics Use cases in Data Science 02:34:10 - How To Become a Data Scientist in 2026 03:02:41 - Excel For Data Science 04:14:37 - Data science pipeline and deployment demo 04:25:38 - NumPy fundamentals and array basics 04:57:53 - NumPy operations and special arrays 05:28:02 - Random numbers, arange/reshape, broadcasting 05:53:36 - Linear algebra and NumPy utilities 06:17:25 - Pandas: Series and DataFrame basics 06:45:56 - Reading data and practical I/O 07:37:12 - Indexing, feature engineering, sorting, cleaning 08:38:01 - GroupBy, pivot tables, and filtering 09:12:59 - Concatenation and merges 09:41:14 - Matplotlib fundamentals and chart choices 10:31:33 - Seaborn and Plotly visualizations 19:14:07 - Pandas For Data Science 19:53:06 - Statistics For Data Science 20:39:48 - Chi-Square Distribution 20:44:51 - Binomial Distribution in Probability 20:52:45 - Central Limit Theorm 20:58:17 - R Squared Error in Regression 21:15:34 - Mean Squared Error 21:47:09 - Probability Density function 22:11:00 - Cumulative Distribution Function 22:41:45 - Conditional Probability 22:48:08 - Bayes Theorm 23:29:09 - Natual Language Processing 23:37:04 - Deep Learning With Python 25:17:28 - Python libraries and plotting overview 25:21:27 - Environment setup: Anaconda and Colab 25:26:30 - NumPy intro: arrays and purpose 25:53:53 - Arrays vs lists; nd-array basics 27:26:51 - NumPy arithmetic operations 28:17:28 - String arrays with numpy.char 28:29:39 - arange and linspace explained 29:29:15 - Indexing and slicing arrays 29:57:28 - Pandas intro and Series basics 30:51:50 - Series analysis and missing data 31:06:26 - Sorting, alignment, and map 32:15:38 - DataFrames: create and display 32:50:22 - Read CSVs - Titanic upload demo 32:49:19 - SQL For Data Science 33:50:59 - SeaBorn Tutorial For Beginners 34:09:34 - Data Science Course With Projects 38:22:45 - Session format and LMS setup 39:04:34 - Python tools and chart basics 42:35:49 - DataFr