Data Science is at the forefront of technological innovation. This Skill Tree offers a beginner - friendly and comprehensive exploration of the data analysis and interpretation world. By 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 strengthen your skills in statistical analysis, machine learning, and data visualization.
242 skills|7 courses|91 projects
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.
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 lab
PostgreSQL for Beginners
Beginner
PostgreSQLDatabase
In this course, learn PostgreSQL basics from installation to data operations, including database management, table creation, and simple queries.
0 lab
Quick Start with NumPy
Beginner
NumPyPython
This course will teach you the fundamentals of NumPy, a library that supports many mathematical operations.
0 lab
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 lab
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 lab
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.