Скачать или смотреть How to Become a Data Scientist for Beginners | Data Science Roadmap 2025 | Tamil

How to Become a Data Scientist for Beginners | Data Science Roadmap 2025 | Tamil


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Chapters :
00:00 - Introduction
00:45 - What is Data Science?
01:05 - Lifecycle of Data Science
02:14 - Is Math Required for Data Science?
02:38 - Where to learn Data Science?
04:50 - Programming Language
05:06 - Tools & Technologies
05:38 - Data Cleaning
06:18 - Data Visualization
06:33 - Types of Machine Learning Algorithms
07:30 - Feature Engineering
08:00 - Ensemble Learning
08:30 - Neural Networks
08:55 - Big Data
09:09 - Natural Language Processing (NLP)
10:17 - Specialization
11:23 - Deployment of ML Models
11:55 - Ethics & Bias
12:35 - Reality of Data Science

Welcome to the ultimate guide to becoming a Data Scientist! 🚀 If you've been wondering how to become a data scientist, how to learn data science, or how to become a data analyst, you're in the right place. In this video, we'll explore the data scientist roadmap, providing insights into how to become a data scientist in 2023 and the steps to fast-track your journey into the world of data science jobs.

🔍 Data Science Basics:

Discover what is data science and its significance.
Get started with data science for beginners and explore the fundamental data science roadmap.
💡 Learn the Skills:

Understand the essential skills required for a data scientist.
Dive into the mathematics fundamentals, covering algebra, calculus, and probability.
Learn programming with Python or R, including basic syntax and Jupyter Notebooks.
📊 Data Manipulation and Visualization:

Explore data manipulation and visualization techniques.
Learn where to find datasets and how to clean and preprocess data.
Master data visualization using popular libraries like Matplotlib and Seaborn.
🧠 Machine Learning Fundamentals:

Delve into supervised and unsupervised learning.
Learn evaluation metrics such as Log Loss, Confusion Matrix, and F1 Score.
📈 Advanced Topics:

Explore advanced machine learning concepts, including ensemble methods and neural networks.
Optionally, delve into big data technologies like Hadoop, MapReduce, and Apache Spark.
🌐 Specialization:

Discover specialized areas like computer vision, reinforcement learning, and time series analysis.
Understand the deployment of models in real-world scenarios and build APIs.
🤔 Important Takeaways:

Don't just follow the trend for money; understand the field deeply.
Data Science pays well, but it demands significant effort and dedication.

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