Data Science For R Full Course 2026 | Data Science Tutorial | Simplilearn

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In this video on Data Science For R Full Course 2026, we’ll start by learning data science from an animated video. You will then learn the essential concepts in data science and understand the important packages in R for data science. You will look at some of the widely used data science algorithms such as Linear regression, logistic regression, decision trees, random forest, including time-series analysis. Finally, you will get an idea about the Salary structure, Skills, Jobs, and resume of a data scientist.

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This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping, and natural language processing.

➡️ About Data Science Course in collaboration with IBM

This Data Science course in collaboration with IBM propels your career to become a data scientist. Gain expertise in in-demand skills like Python, SQL, Excel, Machine Learning, Tableau, generative AI, and more. Dive deep into data interpretation nuances, master Machine Learning, and enhance programming skills to elevate your Data Science career.

Key Features
✅ Simplilearn’s JobAssist helps you get noticed by top hiring companies
✅ Masterclasses from IBM experts
✅ Dedicated live sessions by faculty of industry experts
✅ Industry-recognized Data Scientist Master’s certificate from Simplilearn
✅ Industry-recognized IBM certifications for IBM courses
✅ Ask-Me-Anything (AMA) sessions with IBM leadership
✅ Capstone from 3 domains and 25+ projects
✅ Exclusive hackathons conducted by IBM
✅ Lifetime access to self-paced learning content
✅ Program crafted to initiate your journey as a Data Scientist
✅ Integrated labs for hands-on learning experience

Skills Covered
✅ Generative AI
✅ Prompt Engineering
✅ ChatGPT
✅ Exploratory Data Analysis
✅ Descriptive Statistics
✅ Inferential Statistics
✅ Explainable AI
✅ Conversational AI
✅ Large Language Models
✅ Model Building and Finetuning
✅ Ensemble Learning
✅ Data Visualization
✅ Database Management

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