Question 1

What are the major job responsibilities of a data scientist?

Accepted Answer

A data scientist's primary job responsibilities are - To dig data from primary and secondary sources To utilize machine learning techniques for selecting features, and building and optimizing classifiers To enhance data collection procedures for building analytic systems To process, clean, and verify the integrity of data To create automated anomaly detection systems and track their performance

Question 2

What are the best resources to learn Python in Data Science?

Accepted Answer

You can learn Python through tutorials that cover concepts from beginner to advanced levels. Multiple eLearning portals provide such tutorials. You can learn Python from Books, tutorials, MOOCs, classroom courses, YouTube and also live Apps. However, you cannot become a good data scientist by just learning Python. You should master Data Science with Python which covers programming with Python, Database Technologies, a good hold on Mathematics and Statistical principles as well as Machine Learning with Python. You should also be good with Information Retrieval.

Question 3

How is machine learning different from data science?

Accepted Answer

Machine learning, as well as statistical principles, are a small part of data science. Algorithms applied in machine learning are data dependent and apply a training set so as to fine-tune a model for algorithmic parameters. Most of them comprise of techniques like regression, naive Bayes or supervised clustering, etc.

Question 4

How can I turn unstructured data into structured data in data science projects?

Accepted Answer

NLP and Information Extraction are the processes to do it. Suppose you are having a template that needs to be filled with data extracted from an unstructured information feed. Honestly, this is a very basic method of creating structured data out of an unstructured feed. Based on research, you can also discover structures of data from unstructured data. While there will be no template in the same you can construct a graph with multiple nodes which in a way represent data extracts as well as links that represent how or why information that is related to each other gets fragmented.

Question 5

What is the difference between Big Data and Data Science?

Accepted Answer

The term big data is popularly used to describe exponential growth and availability of data. This data can be present in both structured and unstructured formats. Anybody who works on this data or deals with it in some way or the other to process, analyze, or make sense of such massive amounts of data is a Big Data Professional. Whereas, Data Scientists are basically given the task of investigating complex problems. They apply their knowledge of mathematics and statistical principles in conjunction with computer science algorithms to arrive at answers. Such areas not only represent their knowledge but also portray that they are the most proficient Scientists of data.

Question 6

Is data science worth learning?

Accepted Answer

Data Science is an ever-growing industry with a lot of scopes so yes, it's worth learning. Data Scientists should have strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge

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