9 Languages to Learn to Become a Data Scientist: What Companies Are Looking for in 2018

There is a growing demand for data scientists in every organization. For the growth of any business, there is a need to evaluate the data you collect and data scientists need both the right skill set and the right tools to help you deliver results with your data. In this article, we will talk about open source data science tools you or they can use to do just that.

1. Python

Python is a popular high-level, general-purpose, dynamic programming language that is commonly referred to as the easiest language to read and to learn. It is emerging as the leading language for open data science because it combines rapid development with the ability to interface with high-performance algorithms written in C or Fortran.

For the first time in three years, Python has edged out R as the most popular data science software:

As a result, there is a rich suite of programming libraries that can be used as a foundation for data science.

Python’s syntax allows programmers to express concepts clearly and concisely. The fact that many web applications and most data science applications are now written in Python lowers the cost of entry.

2. R

The R programming language is the lingua franca of statistics. It is an open source programming language and software environment for statistical computing and graphics, supported by the R Foundation for Statistical Computing.

R offers a lot of statistical models, and many statisticians have written their apps in R. It has been the historical leader in open source statistical analysis, and there is a clear concentration of statistical models that have been written using R. The public R package archive, CRAN, contains over 8,000 community contributed packages. Microsoft, RStudio, and other companies provide commercial backing for R-based computing.

3. Julia

Julia is a high-level dynamic programming language designed to address the requirements of high performance numerical and scientific computing while also being effective for general-purpose programming. It is a newer language and hasn’t been around as long as Python or R. Its core is MIT-licensed and therefore, free software with the capability of ingesting community updates.

The high speed of execution makes Julia perfect for working on complex projects involving vast amounts of data. Many basic benchmarks run 30 times faster than Python and often run a bit faster than C code. If you have too much data but enjoy Python’s syntax, Julia is the next programming language to learn.

Julia provides a powerful type inference engine that can help ensure faster code. If you enjoy metaprogramming, the language is flexible enough to be extended. The most valuable additions, however, may be Julia’s simple mechanisms for distributing parallel algorithms across a cluster.

It is suitable for innovators and early adopters who are looking for the highest performance parallel computing language focused on numerical algorithms.

4. Scala

Scala, a syllabic abbreviation for scalable language, is a general-purpose, open source programming language. Scala has full support for functional programming and a strong static type system. Like Java, Scala is object-oriented and runs on top of the Java JVM. Because of this Java connection, it has been adopted by the Big Data Hadoop ecosystem. It has also been popularized by Spark, which is implemented in Scala.

Currently, it lacks the broad spectrum of functionality and supporting data science libraries that are available in Python and R.

If you must juggle data in a thousand-processor cluster and have a pile of legacy Java code, Scala is a great open source solution.

5. Spark

The UC Berkeley AMP Lab spearheaded groundbreaking work to develop Spark, which uses distributed, in-memory data structures to improve speeds for many data processing workloads by several orders of magnitude.

The core data structure in Spark is a resilient distributed dataset (RDD). As the
name suggests, an RDD is Spark’s representation of a data set that’s distributed across the RAM, or memory, of a cluster of many machines. An RDD object is essentially a collection of elements we can use to hold lists of tuples, dictionaries, lists, etc. Similar to a pandas DataFrame, we can load a dataset into an RDD, and then run any of the methods accessible to that object.

While the Spark toolkit is in Scala, the open source community has developed a wonderful toolkit called PySpark that allows you to interface with RDDs in Python. Similarly, there are other toolkits available. Thanks to a library called Py4J, Python can interface with Java objects (in our case RDDs). Py4J is also one of the tools that make PySpark work.

6. SQL

Structured Query language or SQL is a programming language that works well for editing and querying the information stored in a relational database. SQL can also be used for advanced analytical operations and for transforming the queried database’s structure — you can add or delete tables of data, for example. There are open source framework implementations of SQL, including the most popular one: MySQL.

SQL is needed to work with relational databases. Many interviews for technical jobs test your skills in SQL first.

7. Tensorflow

A good, strong open source software library for numerical computation is TensorFlow. It is especially suitable and fine-tuned for large-scale Machine Learning.

It has a simple basic principle: you define a graph of computations to perform in Python and then TensorFlow will run it using a set of tuned C++ code.

A key benefit of TensorFlow is that the graph can be broken into multiple chunks that can run in parallel across multiple CPUs or GPUs. It also supports distributed computing so you can train enormous neural networks on massive training sets in a short amount of time.

TensorFlow powers many of Google’s large-scale services, such as Google Cloud Speech, Google Photos, and Google Search. This shouldn’t be surprising since the Google Brain team developed it.

8. D3.js

Communicating a story as visually as possible is what is known as data visualization. Data visualization is used by data scientists to find and explore the patterns in data and convey their results to stakeholders.

D3.js is an open-source JavaScript library for producing dynamic and interactive data visualizations in web browsers. However, it isn’t a graphics or data processing library. It doesn’t restrict creativity with inbuilt charts. Instead, it has functionalities that make crafting out the relationship between data and graphics easy.

D3.js works flawlessly with front-end web technologies like HTML, CSS, and SVG. D3.js.is gaining a lot of popularity because of its great documentation, the community and accessibility to its founding developer, Mike Bostock.

9. KNIME

The three core components of data preprocessing are extraction, transformation and loading, all of which KNIME does. You are given a graphical user interface which allows the assembly of nodes for data processing. It is an open source data analytics, reporting and integration platform. Through its modular data pipelining concept, KNIME integrates various components for data mining and machine learning. It has captured the attention of many business intelligence and financial data analysts.

Based on Eclipse and written in Java, KNIME is easy to extend and to add plugins to. You can also add supplementary functionalities on the go. Ample data integration modules are already included in the core version.