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Top 10 Advance Java Interview questions?

Top 10 Advance Java Interview questions?   What are the differences between abstract classes and interfaces in Java? What is the difference between ArrayList and LinkedList in Java? What is the purpose of the finalize() method in Java? What is polymorphism in Java and how is it achieved? What are the different types of inner classes in Java? What is the difference between static and non-static methods in Java? What are the different types of exceptions in Java and how do they differ? What is the difference between checked and unchecked exceptions in Java? How does Java handle multithreading and synchronization? What are the different types of JDBC drivers in Java and how do they differ?
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Why Python for Data Analysis?

 Why Python for Data Analysis? 

For many people (myself among them), the Python language is easy to fall in love with. Since its first appearance in 1991, Python has become one of the most popular dynamic, programming languages, along with Perl, Ruby, and others. Python and Ruby have become especially popular in recent years for building websites using their numerous web frameworks, like Rails (Ruby) and Django (Python). Such languages are often called scripting languages as they can be used to write quick-and-dirty small programs or scripts. I don’t like the term “scripting language” as it carries a connotation that it cannot be used for building mission-critical software. Among interpreted languages, Python is distinguished by its large and active scientific computing community. Adopt�tion of Python for scientific computing in both industrial applications and academic research has increased significantly since the early 2000s.

 For data analysis and interactive, exploratory computing and data visualization, Python will inevitably draw comparisons with the many other domain-specific open source and commercial programming languages and tools in wide use, such as R, MATLAB, SAS, Stata, and others. In recent years, Python’s improved library support (primarily pandas) has made it a strong alternative for data manipulation tasks. Combined with Python’s strength in general-purpose programming, it is an excellent choice as a single language for building data-centric applications.

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