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Reasons to Choose a Career in Data Analytics
The career opportunities in the field of data analytics or data science or other similar profiles are seeing a growth like never before. It is a great time to be alive and witness this extraordinary phenomenon where the industry is evolving so fast and the future isn’t clear but it is definitely promising. This background provides an excellent platform for young professionals to take advantage of and build careers in this exciting new field. Listed below are a few reasons why the data analyst/scientist are great prospects for young professionals graduating in a next few years.
1. Interesting Work – Data analytics is definitely not as challenging and fun as software development but it has its own set of challenges that make it very interesting work. For one, it aims to answer question related to market trends and behaviours. More advanced projects would involve learning mathematical techniques used for statistics. Apart from being interesting, one can be part of any industry since the skillset is transferrable in most cases.
2. Pays Well – Any professional in this field would testify that this profile pays well. And since, there is so much demand, this trend is not going to slow down any time soon. Most young graduates, with a technical education and some relevant industry experience (internships) can be hired as data analysts or business analysts and these jobs should pay around USD 60-70K.
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Top 25 Python Interview Questions Prepared by Experts
1. What is JSON? How would convert JSON data into Python data? JSON – stands for JavaScript Object Notation. It is a popular data format for storing data in NoSQL databases. Generally JSON is built on 2 structures. A collection of <name, value> pairs. An ordered list of values. As Python supports JSON parsers, JSON-based data is actually represented as a dictionary in Python. You can convert json data into python using load() of json module. 2. How are the functions help() and dir() different? These are the two functions that are accessible from the Python Interpreter. These two functions are used for viewing a consolidated dump of built-in functions. help() – it will display the documentation string. It is used to see the help related to modules, keywords, attributes, etc. To view the help related to string datatype, just execute a statement help(str) – it will display the documentation for ‘str, module. ◦ Eg: ...
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