• 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.

• 중급숙련도 수준
• 3.7+
• 283

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

• 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!

• 기초적인숙련도 수준
• 4.1+
• 150

Explore a dataset containing a centurys worth of Nobel Laureates. Who won? Who got snubbed?

• 중급숙련도 수준
• 4.5+
• 149

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

• 기초적인숙련도 수준
• 4.5+
• 142

Explore Super Bowl viewership data in the age of soaring ad costs.

• 중급숙련도 수준
• 3.5+
• 141

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

• 기초적인숙련도 수준
• 4.5+
• 134

Join a leading HR consultancy to use regular expressions and data preprocessing to find tech job candidates from thousands of resumes.

• 중급숙련도 수준
• 4.2+
• 122

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

• 기초적인숙련도 수준
• 4.6+
• 103

Use SQL to figure out the golden era of video games!

• 중급숙련도 수준
• 4.1+
• 98

Analyze grocery store data to identify shopping patterns and preferences.

• 중급숙련도 수준
• 4.3+
• 88

Compare life expectancy across countries and genders with ggplot2.

• 중급숙련도 수준
• 4.7+
• 78

Write functions to forecast time series of food prices in Rwanda.

• 기초적인숙련도 수준
• 3.9+
• 58

Apply your knowledge of data types and categorical data to prepare a big dataset for modeling!