• IntermedioLivello di competenza
• 3.6+
• 358

Analyze product data for an online sports retail company to optimize revenue.

• IntermedioLivello di competenza
• 4.2+
• 356

Building an image processing pipeline for data augmentation.

• IntermedioLivello di competenza
• 3.9+
• 354

Chart electric vehicle charging trends to inform strategic planning.

• IntermedioLivello di competenza
• 4.1+
• 317

Use web scraping and NLP to find the most frequent words in classic literature: Herman Melvilles novel, Moby Dick.

• IntermedioLivello di competenza
• 4.1+
• 308

Build an AI-powered inbox assistant to classify your emails using Llama.

• IntermedioLivello di competenza
• 4.0+
• 307

Perform a hypothesis test to determine if more goals are scored in womens soccer matches than mens!

• IntermedioLivello di competenza
• 3.7+
• 300

Build models predicting customer churn for Indian telecom customers.

• IntermedioLivello di competenza
• 4.0+
• 299

Use MLBs Statcast data to compare New York Yankees sluggers Aaron Judge and Giancarlo Stanton.

• IntermedioLivello di competenza
• 3.7+
• 289

Query the VatComply API to gather exchange rate information and incorporate into a sales orders dataset!

• IntermedioLivello di competenza
• 4.0+
• 268

Powering Data for the Department of Energy - Building an ETL Pipeline

• IntermedioLivello di competenza
• 3.5+
• 268

Use mean-variance optimization to find optimal portfolio weights and then check how well they would have performed.

• IntermedioLivello di competenza
• 3.4+
• 258

Use joining techniques to discover the oldest businesses in the world.

• IntermedioLivello di competenza
• 4.6+
• 253

Explore facial recognition with ML by distinguishing Arnold Schwarzenegger from others using Python and scikit-learn.

• IntermedioLivello di competenza
• 4.2+
• 228

Analyze online shopping habits: returning vs. new customers using statistics & probability techniques!

• IntermedioLivello di competenza
• 4.1+
• 216

Use logistic regression to predict whether people will make a claim on their car insurance!

• IntermedioLivello di competenza
• 4.6+
• 212

Generate features from text and numeric variables to predict the popularity of books.

• IntermedioLivello di competenza
• 4.4+
• 202

Build a PyTorch-powered traffic prediction system using time-series skills and deep learning to forecast traffic volume.

• IntermedioLivello di competenza
• 4.4+
• 198

Classify food images using Hugging Face models, automatically recognizing and categorizing food items.

• IntermedioLivello di competenza
• 4.2+
• 197

Step into a data scientists role and learn how to make product decisions with A/B testing in Python!

• IntermedioLivello di competenza
• 4.1+
• 185

Help a fast food chain save money and place more accurate orders by building a model to predict food sales.

• IntermedioLivello di competenza
• 4.8+
• 167

Automate e-commerce processes with image classification.

• IntermedioLivello di competenza
• 4.4+
• 160

Leverage machine learning algorithms and models for marketing analytics tasks in a streaming platform.

• IntermedioLivello di competenza
• 4.9+
• 148

Build an AI-powered airline chat assistant to generate custom responses and identify user intent.

• IntermedioLivello di competenza
• 4.5+
• 144

Analyze subscription data for a SaaS company to identify what drives customer subscription renewals.

• IntermedioLivello di competenza
• 4.2+
• 142

Manipulate and plot time series data from Google Trends to analyze changes in search interest over time.

• IntermedioLivello di competenza
• 4.7+
• 139

Use DataFrames to read and merge employee data from different sources.

• IntermedioLivello di competenza
• 4.1+
• 139

Reanalyze the data behind one of the most important discoveries of modern medicine: handwashing.

• IntermedioLivello di competenza
• 3.4+
• 137

Apply your data manipulation skills to time series data on water levels of the River Thames.

• IntermedioLivello di competenza
• 4.3+
• 126

Examine the relationship between heart rate and heart disease using multiple logistic regression.

• IntermedioLivello di competenza
• 3.9+
• 126

Analyze Twitter follower data with Python! Use Pandas and NetworkX to build functions, identify influential users, and explore network structures.