• 기초적인숙련도 수준
• 4.0+
• 374

Sharpen your debugging skills to enhance sales data accuracy.

• 중급숙련도 수준
• 4.3+
• 354

Building an image processing pipeline for data augmentation.

• 기초적인숙련도 수준
• 4.2+
• 354

Use SQL to analyze a database containing information about Transport for London journeys over 12 years!

• 중급숙련도 수준
• 3.8+
• 354

Chart electric vehicle charging trends to inform strategic planning.

• 고급의숙련도 수준
• 4.1+
• 335

Perform a machine learning experiment to find the best model that predicts the temperature in London!

• 고급의숙련도 수준
• 3.8+
• 328

Develop an inventory management system for a retail business using OOP in Python.

• 중급숙련도 수준
• 4.1+
• 321

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

• 중급숙련도 수준
• 3.6+
• 317

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

• 중급숙련도 수준
• 4.2+
• 314

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

• 고급의숙련도 수준
• 4.0+
• 312

Use deep learning to automate the classification of service desk tickets.

• 중급숙련도 수준
• 4.1+
• 308

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

• 고급의숙련도 수준
• 4.1+
• 299

Train a simple reinforcement learning agent in stock trading simulation.

• 중급숙련도 수준
• 3.6+
• 296

Build models predicting customer churn for Indian telecom customers.

• 중급숙련도 수준
• 4.0+
• 293

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

• 기초적인숙련도 수준
• 4.2+
• 287

Use a variety of data manipulation techniques to explore different aspects of Legos history.

• 기초적인숙련도 수준
• 3.9+
• 287

Exploring flight data from NYC using advanced data joining techniques in R.

• 기초적인숙련도 수준
• 4.4+
• 286

Build interactive stock charts with Plotly and uncover financial trends in the fast food industry.

• 기초적인숙련도 수준
• 4.3+
• 285

Use OpenAIs powerful API to enrich and summarize stock market data.

• 중급숙련도 수준
• 3.7+
• 283

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

• 기초적인숙련도 수준
• 4.1+
• 275

Use SQL to analyze a database containing information about Transport for London journeys over 12 years!

• 고급의숙련도 수준
• 3.9+
• 267

Explore transportation time series data analysis to uncover patterns and forecast travel costs.

• 중급숙련도 수준
• 3.6+
• 266

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

• 중급숙련도 수준
• 4.6+
• 264

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

• 중급숙련도 수준
• 3.9+
• 264

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

• 고급의숙련도 수준
• 4.6+
• 263

Use logistic regression to determine which treatment procedure is more effective for kidney stone removal.

• 중급숙련도 수준
• 3.4+
• 260

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

• 기초적인숙련도 수준
• 4.3+
• 239

Automatically generate keywords for a search engine marketing campaign using Python to send website visitors to the right landing page.

• 고급의숙련도 수준
• 4.4+
• 236

Transcribe customer support audio calls, evaluate sentiment, search in text and identify common entities to improve customer service!

• 중급숙련도 수준
• 4.3+
• 227

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

• 중급숙련도 수준
• 4.1+
• 210

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