Data scientists are one of the most hirable specialists today, but it’s not so easy to enter this profession without a “Projects” field in your resume. You need experience to get the job, and you need the job to get the experience. Seems like a vicious circle, right? Statsbot’s data scientist Denis Semenenko wrote this article to help everyone with making the first simple, but yet illustrative data science projects which can take less than a week of work time. The great advantage of these projects is that each of them is a full-stack data science problem. This means that you need to formulate the problem, design the solution, find the data, master the technology, build a machine learning model, evaluate the quality, and maybe wrap it into a simple UI. This is a more diverse approach than, for example, Kaggle competition or Coursera lessons (but they are quite good too!). Keep reading if you want to improve your CV by using a data science project, find ideas for a university project, or just practice in a particular domain of machine learning.
Data science is the field of applying advanced mathematical and statistical techniques to large sets of data to uncover insights that can improve the performance of different parts of a business. Data science is used in growth to uncover commonalities between types of users that are either successful or unsuccessful with a product. Marketers and growth teams use data science findings to improve the performance of their product, user acquisition funnel, user retention and more. These are the best articles on how to use data science to grow your business. From how to analyze data using R, pandas for Python, regression, model building, SQL and more, you'll learn how to apply data science concepts to growth.
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