Data Science

Data Science

Data Science is at the forefront of technological innovation. This Skill Tree provides a comprehensive, beginner-friendly journey into the world of data analysis and interpretation. Following a well-structured roadmap, you'll learn essential concepts and tools through hands-on, non-video courses. Practical exercises in the interactive playground will solidify your skills in statistical analysis, machine learning, and data visualization.

211 skills|6 courses|91 projects
Quick Start with Python
Quick Start with Python
Quick Start with Python

Quick Start with Python

Beginner
LinuxPython
Master Python fundamentals in this hands-on course designed for beginners. Learn essential concepts like data types, control structures, functions, modules, and data structures through interactive labs and practical challenges. Perfect for those starting their Python programming journey.
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Quick Start with MySQL

Quick Start with MySQL

Beginner
LinuxMySQLSQL
In this course, you will learn what Structured Query Language (SQL) and databases are, the basics of database management, how to set up and configure MySQL, and how to get MySQL client to connect to a MySQL Server.
0%
0 lab
Quick Start with NumPy

Quick Start with NumPy

Beginner
NumPyPython
This course will teach you the fundamentals of NumPy, a library that supports many mathematical operations.
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0 lab
Quick Start with Pandas

Quick Start with Pandas

Beginner
PandasPython
This course is designed for beginners who want to start analyzing data with Pandas. It covers the basics of Pandas, including data structures, data manipulation, and data visualization.
0%
0 lab
Quick Start with Matplotlib

Quick Start with Matplotlib

Beginner
MatplotlibPython
This course is a quick tutorial on Matplotlib, a Python library for drawing 2D and 3D graphics. It is designed to get you started with Matplotlib quickly.
0%
0 lab
Quick Start with scikit-learn

Quick Start with scikit-learn

Beginner
scikit-learnMachine Learning
In this course, We will learn how to use scikit-learn to build predictive models from data. We will explore the basic concepts of machine learning and see how to use scikit-learn to solve supervised and unsupervised learning problems. We will also learn how to evaluate models, tune parameters, and avoid common pitfalls. We will work through examples of machine learning problems using real-world datasets.
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0 lab