Programming Languages You Should Learn To Get “The Sexiest Job of The 21st Century”

Over the last 5 years data consumption in the world has increased exponentially. From personal devices to industrial supercomputers, hundreds of zettabytes of data are being processed every day. To handle such gigantic volume of data with precision all industries are now looking for experts. Programming, statistics and design – these three skills are crucial for such designation. Yes,  we call them data scientists and data analysts and they possess the sexiest job of the 21st century.

Best Thing About The Job

It’s a dynamic field where various new technologies are being implemented and discussed everyday. Cutting edge softwares help in building beautiful and surprisingly accurate prediction models.

Data gives us the ocean of opportunities to explore unknown information. Analyzing such data leads to intuitive insights that help companies make better decision and save large amount of money.

There is a thrill in the nature of the job since the implementation of an idea is possible in infinite combinations and better the creativity, higher the value of the professional. Oh, did I mention , it is also one of the highest paying jobs in the market. According to Forbes magazine, the average base salary of a data scientist is $117,000 per year.

Programming Skills Required

To become a data scientist/analyst one must be well equipped with programming languages that have good statistical and visualization properties and libraries. Here we are going to discuss the top 5 programming skills that are in demand for the role of a data scientist.

1. R

When it comes to statistical computation, graphical design and analysis, R has become one of the most in demand language among data scientists and statisticians. This open source programming language is supported by the R Foundation for statistical computing, primarily created by Ross Ihaka and Robert Gentleman.

R-Programming

R is being used in Facebook’s data science center for its awesome custom visualization packages like ggplot2, dplyr, plyr, and reshape. The New York Times used R to forecast senate elections. Also the popular BreakOut Detection package by twitter for getting user experience and trend monitoring throughout the network.

To get started with R first download RStudio, then you can check out the R tutorial in the Programming Hub Application or learn directly on the web version.

2. Python

Python one is one of the most popular high-level general purpose programming language since its launch in 1991, created by Guido Van Rossum. This interpreted language is not only compact and readable but also has some amazing tools and packages that allows data analysts to monitor, clean and prepare data sets. The regular expressions are used for getting data into format and cleaning the data sets. Some of the popular scientific libraries used in data science are- NumPy, SciPy, Matplotlib and Pandas.

Source: dataiku.com( Programming )
                       Source: dataiku.com

Python can be used along with external web API’s from which data can be easily pulled, web scrapping and system administration all of these factors make python stand out from other statistical model programming languages.

You can download python directly from the project site. There are other packages which comes with some popular pre-installed libraries. e.g: Anaconda  (Yup! It’s Python’s friend). You can start learning python from the Programming Hub Application or learn directly on the web version.  Then try out  dataquest to get started with data science.

3. SQL

The Structured Query Language (here on-wards and for all eternity referred as SQL) is capable of managing structured data in Relational Database Management Systems(RDBMS).Though it is said that not only SQL or NoSQL databases are primarily being accessed for data mining but that’s not all. The data processing frameworks built on top of non-RDBMS systems have implementation of SQL.

SQL is popular in data science for 3 reasons: Aggregations, Data Modelling and Window Function.

Understanding relational data model is fundamental in data science. The mapping of one to one or one to multiple data models are understood through SQL. Also being able to take raw data and settle it into data frames with proper clauses and attributes is necessary to build a model. Moving averages, cumulative sums, meta data navigation- all these are just some of the few perks SQL has to offer in data preparation.

Start learning SQL with the introduction present in the Programming Hub Application . You can also try this wonderful course from KhanAcademy. You can install an SQL server in your preferred operating system to start implementing.

4. SAS

The SAS(Statistical Analysis System) is a software developed by the SAS institute for advanced analytics. From simple data solutions to high performance distributed processing, SAS can do all, its dynamic and flexible. Industrial giants from all domains like IT, banking, retail are using SAS to deliver high performance business analytics model. MuSigma, IBM, Accenture, Amazon, Dell Analytics and many more companies are taking up SAS as their utility platform for analytics.

Since SAS has a proprietary encryption algorithm given to its products and limitation among industrial companies, it’s not the first choice for people to start off with data science.

You can learn SAS from the official SAS academy, though the courses are high priced.

5. Java

Java is one of the most popular programming languages throughout all industries. This language gives us the full power of building object oriented modular softwares that can be created separately and built and packaged as required. The Java Virtual Machine(JVM) is a great environment for deploying large scalable softwares.

The core packages of Apache Hadoop and Spark (Used in Big Data analytics) are written in Java. Weka a popular software package is used for data mining, it’s a collection of Machine Learning algorithms. The core built of Weka is also done in Java.

Even though the key visualization or analysis may not be done through Java directly due to some restrictions, but Java has helped in building amazing tools and softwares to manage data analysis. The knowledge of Java will help us rebuild and modify these packages as needed.

You can install Java from the official site of ORACLE. There are many good IDEs like Eclipse and NetBeans that can be used as the development environment.  For those who are new to Java or wish to brush up those skills, can refer to the Java tutorial in the Programming Hub Application or learn directly on the web version.

 

There are many more programming languages that are getting popularity in data science domain like Scala and Julia. In our upcoming editions, we will talk  in details about these languages and guide you how to start learning them.

So, start with one language from the above list and begin your data science journey today.