Python - Data Analytics - Real World Hands-on Projects

These projects were completed as part of my course Python - Data Analytics - Real World Hands-on Projects on Udemy. The course focuses on applying Python to real-world data analysis scenarios using the Pandas library, among other tools. This repository contains the source code and datasets for each of the eight projects, which have provided me with practical insights and hands-on experience in data analysis.

The course covers several fundamental data analysis techniques, including data cleaning, manipulation, and visualization, using real-world datasets. Through this course, I have developed skills that are essential for a career in data analytics, including handling diverse datasets and performing advanced analysis with Python.

Key Projects Completed:

• Weather Data Analysis
  Analyzing weather patterns and trends using publicly available datasets.

• Cars Data Analysis
  Exploring data about various cars and conducting analyses to uncover insights related to performance, cost, and efficiency.

• Covid Data Analysis
  Understanding the impact of the COVID-19 pandemic by analyzing global data across multiple dimensions.

• London Housing Data Analysis
  Analyzing real estate data to examine trends in the London housing market.

• Census Data Analysis
  Exploring census data to identify trends in population demographics and related variables.

• Udemy Data Analysis
  Analyzing Udemy course data to uncover patterns related to course popularity, ratings, and user feedback.

• Netflix Data Analysis
  Conducting analysis on Netflix data to gain insights into viewing habits and trends.

Tools and Techniques Used:

• Python: The primary programming language for data analysis.
• Pandas: Used extensively for data manipulation and cleaning.
• Matplotlib: For data visualization and graphical representation of findings.
• Jupyter Notebook: The primary environment for running and documenting the projects.

What I Learned:

• How to handle and analyze large datasets using Pandas.
• How to visualize data effectively using Matplotlib.
• Techniques for cleaning and preparing data for analysis.
• How to draw insights from diverse datasets and apply data analytics techniques to real-world problems.

Each project in this repository reflects the hands-on application of Python-based data analytics, which has enhanced my skills and understanding of the field. These projects have also helped me build a portfolio that demonstrates my ability to handle real-world data and extract meaningful insights.

Links:

• Course Link
• Certificate of Completion

Feel free to explore the repository, dive into the code, and experiment with the datasets to deepen your understanding of data analysis with Python.