Duration:
60 hours
 
Project Overview:
Explore and analyze a dataset to uncover insights about food products worldwide. This project focuses on data cleaning, visualization, and using statistical tools.
 
Key Learning Outcomes:
Data Cleaning & Preprocessing: Handle missing values, filter relevant features, and ensure data integrity.
Exploratory Data Analysis (EDA): Use visualization tools to uncover patterns and insights within the dataset.
Statistical tools: Build and evaluate several statistical tools.
Python Programming: Utilize libraries like pandas, seaborn, statmodels, and scikit-learn, for data manipulation and analysis.
Data Visualization: Create compelling visualizations to effectively communicate findings.
 
Tools & Libraries:
Programming Language: Python
Libraries: pandas, seaborn, matplotlib, scikit-learn, statmodels
 
Deliverables:
Data Cleaning Notebook.
Data Analysis Notebook.
Final Report.
 
Who Should Enroll:
Data enthusiasts interested in real-world datasets and food industry insights.
Individuals looking to enhance their data cleaning, analysis, and statistical skills.
Professionals aiming to apply data analysis techniques to large-scale datasets.
 
Why This Project? By working with the extensive dataset, you'll gain practical experience in handling large datasets, performing insightful analyses, and building statistical analysis. This project bridges the gap between data collection and actionable insights, making it ideal for aspiring data scientists.