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What’s the best way to learn data science as a beginner?
Learning data science as a beginner can be a rewarding but challenging journey. Here’s a step-by-step guide to help you get started:
1. Understand the Basics
Mathematics and Statistics: Start with foundational concepts in statistics, probability, linear algebra, and calculus.
These are essential for understanding data analysis and machine learning.
Programming: Learn a programming language commonly used in data science, like Python or R. Python is highly recommended due to its
extensive libraries and community support.
2. Learn Data Manipulation and Analysis
Pandas: Start with the Pandas library in Python for data manipulation, such as data cleaning, transformation, and exploration.
NumPy: Learn NumPy for numerical operations, which is crucial for handling large datasets.
3. Master Data Visualization
Matplotlib & Seaborn: Learn to visualize data using these libraries. Visualization helps in understanding data trends and patterns.
Tableau or Power BI: Consider learning these tools for more advanced and interactive visualizations.
4. Understand Databases and SQL
SQL: Learn SQL to query and manage databases. Data is often stored in relational databases, so SQL is an essential skill.
NoSQL: Explore NoSQL databases like MongoDB for working with unstructured data.
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