Welcome to the repository of my projects from the DataCamp Data Analyst track! Here, you'll find a collection of practical and challenging projects that complement the theoretical knowledge acquired on the course and that I've developed over the course of the lessons.
Introduction to Python for Data Analysis: Begin your data analyst training with interactive exercises. Explore the fundamentals of Python, setting the stage for your data analysis journey.
Working with Data in Python: Dive into hands-on exercises using popular Python libraries such as pandas, NumPy, and Seaborn. Gain practical experience in importing, cleaning, and visualizing data.
Intermediate Python: Explore intermediate Python concepts crucial for data analysis, ensuring a comprehensive understanding of the language.
Data Manipulation with pandas: Deepen your expertise in data manipulation using the pandas library. Learn how to join datasets, perform advanced operations, and handle missing data effectively.
Statistical Thinking in Python (Part 1): Acquire fundamental statistical skills, including hypothesis testing, to make informed decisions based on data analysis.
Statistical Thinking in Python (Part 2): Extend your statistical knowledge with advanced topics, enabling you to draw meaningful insights from complex datasets.