Big Data Strategies & Problem-solving Techniques
As a Google data scientist, you’re expected to understand the fundamentals of big data strategies and problem-solving techniques. Big data strategies involve collecting and analysing vast amounts of complex data while problem-solving techniques are used to extract meaningful insights from this data. In order to be successful in this role, you must have an understanding of the business context in which you’ll be operating as well. This means being able to identify the most appropriate algorithms for testing, the most valuable features for extraction, the most effective machine learning models for development and evaluation, and more.
Data collection is arguably the first step towards successful problem-solving. As a Google data scientist, you must be knowledgeable about various sources of data, both structured and unstructured. You should also be adept at utilizing various methods of retrieving and cleaning that data to ensure its accuracy and integrity. Once your data is collected, it’s time to dive into your analysis. Analyse your trends with statistical methods such as relational database queries or other forms of querying such as SQL or NoSQL databases.
As part of your analysis, it’s important to consider the business context in which this information will be used. Identify any potential risks associated with the decisions you plan on making while considering potential external factors that may impact these decisions such as regulations or customer preferences. After identifying these risks and external factors, integrate them into a comprehensive framework to interpret your results holistically without leaving important elements out. Source: Google Data Scientist Interview Questions
You can also read:
Best data analytics courses in mumbai
Best data science classes in pune
Best data analytics courses pune
Comments
Post a Comment