Most Data Science courses and textbooks describe the basic algorithms and skills required to successfully complete various tasks. But when it comes to real projects, it turns out that this knowledge is far from working realities.
How to get the most hands-on experience and get ready to work as a Data Scientist? Here are some guidelines: computer engineering job descriptions
Use standard open source libraries. The data science application field relies on libraries that are well documented, tested, and have a literate API. Using alternative or custom libraries invariably leads to problems and bugs - they distract from the data and the context in which the model will be applied.
Spend more time examining the data and putting it into the right format. In a number of projects, a lot of data manipulation will be required, and it will take relatively little time to set up the model. Data science beginners are able to describe the structure of algorithms, but they lack the skills to work with pandas and other libraries that are important for real cases.
Practice different techniques. If you cannot describe the practical benefits of what you are learning, you are probably not yet ready to apply this knowledge in your work.
Learn how to interpret model output. Learn to extract meaningful inferences from any machine learning model using Machine Learning Explainability techniques.
Create projects in the area that interests you. It can be movies, news, sports, food, and so on. You will learn how to formulate questions about the world in such a way that you understand how you can solve them using technical tools. This is one of the essential skills for a Data Scientist.
But perhaps the most important skill is sharing your work to learn how to interpret and discuss the results.
But is it possible to skip the theory of algorithms and just practice in order to quickly become a Data Scientist? Not.
You can learn a lot about manipulating data, interpreting it, and applying models in real life. Try to devote more time to practical skills and not dwell on abstract theories: this approach will help you better prepare for serious projects.
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