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You should select a language to learn depending on your needs, the problems you are trying to solve, and whom you are solving them for.

The popular languages are Python, R, SQL, Scala, Java, C++, and Julia.

For data science, you can use Python's scientific computing libraries like Pandas, NumPy, SciPy, and Matplotlib.

Python can also be used for Natural Language Processing (NLP) using the Natural Language Toolkit (NLTK).

Python is open source, and R is free software.

R language’s array-oriented syntax makes it easier to translate from math to code for learners with no or minimal programming background.

SQL is different from other software development languages because it is a non-procedural language.

SQL was designed for managing data in relational databases.

If you learn SQL and use it with one database, you can apply your SQL knowledge with many other databases easily.

Data science tools built with Java include Weka, Java-ML, Apache MLlib, and Deeplearning4.

For data science, popular program built with Scala is Apache Spark which includes Shark, MLlib, GraphX, and Spark Streaming.

Programs built for Data Science with JavaScript include TensorFlow.js and R-js.

One great application of Julia for Data Science is JuliaDB.
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