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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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What is Data Science?

 What is Data Science?

Data Science is about data gathering, analysis, and decision-making.

Data Science is about finding patterns in data, through analysis, and make future predictions.

By using Data Science, companies are able to make:

  • Better decisions (should we choose A or B)
  • Predictive analysis (what will happen next?)
  • Pattern discoveries (find a pattern, or maybe hidden information in the data)

How Does a Data Scientist Work?

A Data Scientist requires expertise in several backgrounds:

  • Machine Learning
  • Statistics
  • Programming (Python or R)
  • Mathematics
  • Databases

A Data Scientist must find patterns within the data. Before he/she can find the patterns, he/she must organize the data in a standard format.

Here is how a Data Scientist works:

  1. Ask the right questions - To understand the business problem.                 
  2. Explore and collect data - From the database, weblogs, customer feedback, etc.                                                                                 
  3. Extract the data - Transform the data to a standardized format.             
  4. Clean the data - Remove erroneous values from the data.                     
  5. Find and replace missing values - Check for missing values and replace them with a suitable value (e.g. an average value).                        
  6. Normalize data - Scale the values in a practical range (e.g. 140 cm is smaller than 1,8 m. However, the number 140 is larger than 1,8. - so scaling is important).                                                                         
  7. Analyze data, find patterns and make future predictions.                 
  8. Represent the result - Present the result with useful insights in a way the "company" can understand.
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