Data Science 101 | Data Science Explained | Data Science For Beginners | Data Science | Simplilearn

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This Data Science tutorial will help you understand what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.

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➡️ 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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