Data Scientist Full Course 2026 | Learn Data Science In 24 Hours | Data Science Course | Simplilearn

This video on Data Scientist Full Course 2026 by Simplilearn will help you learn data science from beginner to advanced level and understand how to analyze data, build models, and generate insights for real-world problems. The course begins with an introduction to data science and explains the role of a data scientist in modern organizations. You will learn the fundamentals of data analysis, statistics, and probability used in data science. The tutorial covers important tools such as Python, R, SQL, and libraries like NumPy, Pandas, and Matplotlib. You will understand how to perform data cleaning, data preprocessing, and exploratory data analysis. The course also explains machine learning concepts and algorithms such as regression, classification, and clustering. You will learn how to build predictive models and evaluate their performance. The tutorial also covers data visualization techniques and how to present insights effectively. You will understand real-world applications of data science in domains like healthcare, finance, and marketing. The course also introduces deep learning basics and AI concepts used in modern data science workflows. You will learn about model optimization, feature engineering, and deployment basics. By the end of this data science tutorial for beginners, you will clearly understand data science tools, techniques, and workflows needed to start a career as a data scientist.

Following are the topics covered in Data Science Full Course 2026:
00:00:00 - Introduction to Data Science Excel Full Course 2026
00:04:16 - What Is Data Science?
00:06:25 - Data Scientist Vs Data Analyst In 2026
00:17:40 - Data Science Life Cycle
00:34:45 - Use Cases Of Mathematics In Data Science
00:48:15 - Statistical Analysis And Business Applications
01:11:45 - Data Science Roadmap For 2026
01:40:16 - Excel for Data Analytics 2026
02:49:21 - Data Science With Python
04:12:18 - Applied Data Science With Python
04:15:25 - What is Data science
04:26:20 - Data science process and applications
04:39:20 - Python libraries and plotting overview
04:43:19 - Environment setup: Anaconda and Colab
04:48:22 - NumPy intro: arrays and purpose
04:57:34 - Why NumPy is fast: timing demo
05:15:45 - Arrays vs lists; nd-array basics
05:28:27 - 0D/1D/2D/3D arrays and attributes
06:13:19 - Reshape arrays: change dimensions
06:28:40 - Transpose arrays and use cases
06:48:43 - NumPy arithmetic operations
07:39:20 - String arrays with numpy.char
07:51:31 - arange and linspace explained
08:13:34 - Random numbers and distributions
08:51:07 - Indexing and slicing arrays
09:19:20 - Pandas intro and Series basics
10:13:42 - Series analysis and missing data
10:28:18 - Sorting, alignment, and map
11:37:30 - DataFrames: create and display
12:11:11 - SQL For Data Science Tutorial 2026
13:12:51 - Numpy Tutorial for Data Science
16:18:26 - Pandas For Data Science
16:57:25 - Statistics For Data Science
17:20:12 - Seaborn Library In Python
17:38:47 - Types Of Distribution In Statistics
18:02:42 - Chi-Square Distribution
18:07:45 - Binomial Distribution In Probability
18:12:45 - Poisson Distribution In Probability And Statistics
18:15:39 - Inferential Statistics
18:21:11 - R Squared Error
18:38:28 - Mean Squared Error
18:43:16 - Probability And Binomial Distribution
19:10:03 - Probability Density Function With Example
19:33:54 - Cumulative Distribution Function Tutorial With Example
20:04:39 - Conditional Probability
20:11:02 - Bayes Theorem
20:15:46 - Supervised Vs Unsupervised vs Reinforced Learning
20:52:03 - Natural Language Processing
20:59:58 - Deep Learning With Python
22:13:20 - Top 10 Data Science Projects For 2026
22:23:06 - Data Science Interview Questions

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