Kaggle offers a no-setup, customizable, Jupyter Notebooks environment. Access GPUs at no cost to you and a huge repository of community published data & code. Inside Kaggle you’ll find all the code & data you need to do your data science work. Use over 50,000 public datasets and 400,000 public notebooks to conquer any analysis in no time.
Data science has critical applications across most industries, and is one of the most in-demand careers in computer science. Data scientists are the detectives of the big data era, responsible for unearthing valuable data insights through analysis of massive datasets. And just like a detective is responsible for finding clues, interpreting them, and ultimately arguing their case in court, the field of data science encompasses the entire data life cycle.
That starts with capturing lots of raw data using data collection techniques, and then building and maintaining data pipelines and data warehouses that efficiently “clean” the data and make it accessible for analysis at scale. This data infrastructure allows data scientists to efficiently process datasets using data mining and data modeling skills, as well as analyze these outputs with sophisticated techniques like predictive analysis and qualitative analysis. Finally, these findings must be presented using data visualization and data reporting skills to help business decision makers.
Depending on the size of the company, data scientists may be responsible for this entire data life cycle, or they might specialize in a particular portion of the life cycle as part of a larger data science team
