Mastering Data Science Programming: 7 Languages You Need to Know

Hello friend, the exciting field of data science continues growth at a breakneck pace. As organizations unlock greater value from ever expanding datasets, demand for data literacy skills surges in turn. While many programming languages hold utility for crunching data, clear leaders have emerged as favorites among professional data scientists. Allow me to outline 7 languages in particular worth mastering to launch or advance your own data science career.

We stand at the dawn of the big data revolution, with IDC forecasting global data volume growing at a 23% compound annual rate to reach 163 zettabytes by 2025. As the world’s data generation accelerates exponentially, extracting meaningful signal from the noisy firehose of information presents enormous opportunity. Data science programming languages provide the keys for translating raw data into practical insights.

McKinsey estimates a massive talent shortage of 250,000 data scientists in the United States alone. The sooner you expand your programming language arsenal, the faster you’ll elevate your skills against intense competition. While specializing initially Python and R as common starter languages, aspiring data science programmers should continually broaden abilities to wield different tools. Let‘s explore 7 languages delivering invaluable yet unique strengths to tame data.

Python: The Best Overall Language for Budding Data Talent

As the most widely adopted programming language globally across all domains, Python naturally finds immense application for data tasks. Created in 1991 by Guido Van Rossum, Python prioritizes readable, simplified syntax allowing developers to express complex application logic in fewer lines than Java or C++. The Zen of Python ethos values beautiful code over terse efficiency.

For data science, Python dominates as the lingua franca thanks chiefly to its pandas library first released in 2008. Pandas introduces intuitive, expressive data structures and analysis functions tailored specifically for data manipulation. Operations like merging, reshaping and pivoting dataset slices feel native. Tight integration with other leading packages like NumPy and SciKit-Learn establishes a powerful data science toolkit within Python eco-system.

import pandas as pd
data = pd.DataFrame(data) 

data.groupby([‘column’]).mean()
data.plot(kind=‘hist’,bins=50)

While Python may lack raw computational throughput of a Java or C++, its flexibility and development velocity boost productivity for exploratory, iterative data tasks. Python forms a proven, newbie-friendly gateway to data science capable of handling many real-world data needs.

R: Domain-Specific Mastery of Statistical Analysis

Whereas Python offers extraordinarily broad utility, R exists as a domain-specific language tailored expressly for statistical analysis and visualization. Conceived in 1992 out of the S environment for interpreting data, R evolved over 20+ years instrumenting statistics, econometrics, finance and general data analysis tasks. Designed by statisticians for statisticians, R understands matrix math and analytics as native language elements rather than bolted-on libraries.

Ease of generating charts, plots and other graphical data presentations gives R true rapid prototyping feel. The ggplot2 graphics grammar system stands out for concise declarative visualization construction. Layering graphical elements via code makes iteratively developing visual data stories simple and fast.

library(ggplot2)
ggplot(data) + 
   geom_point(mapping = aes(x = column1, y = column2))

R’s vocabulary resonates intuitively for data scientists from math and statistics backgrounds. Whether via base R or extensions like tidyverse for data wrangling, R speaks statistics fluently. While less generalizable than Python, R’s analytical specialization makes it a must-know language for data science programmers.

Julia: Crafted for Scientific Computing Performance

Unlike Python and R added data science capabilities organically, Julia focuses expressly on technical computing prowess from its inception. Created in 2009 by an accomplished team of PhDs, Julia fuses expressiveness and ease of use with compiled language capability. Julia combines interactive developer feedback loops with JIT (just-in-time) compilation deploying optimized machine code matching C and Fortran speeds.

Julia’s multi-paradigm design leverages dynamic typing, higher order functions and other scripting language advantages while retaining fine grain control of memory layout, parallelism and computational resources. Julia’s prime differentiation comes through integrating symbolic math syntax and notation seamlessly, enabling direct expression of complex statistical and analytical operations intuitively. Developers spend more time data wrangling rather than wrestling language semantics or performance.

using DataFrames

df = DataFrame(column_1 = 1:100)
@time df.column_1 ./ 100 

As the newest language covered, Julia risks playing catch up on data science libraries compared to Python and R. However Julia’s expanding community and purpose-built emphasis firmly back Julia as an emerging analytics powerhouse for programmers craving productivity and speed simultaneously.

Scala: Fusing Object and Functional Styles on the JVM

Developed at École Polytechnique Fédérale in Lausanne, Scala melds object oriented and functional disciplines atop rock solid Java Virtual Machine (JVM) portability. Released 2004 by Martin Odersky and team, Scala aims higher than Java with an expressiveness meeting concurrency, scalability and performance demands of modern data engineering.

Scala’s fusion of imperative and declarative thinking builds on strengths of both programming models for data systems. Scala adopts mainstays of functional coding like immutable data, referential transparency and higher order functions facilitating parallelism essential for hefty data workloads. Seamless Java interoperability lets Scala applications enjoy runtime portability and reuse existing data access code where convenient.

For data scientists, Scala supports essential big data interfaces like Apache Spark natively. Concise syntax further tames tangled MapReduce flows compared to Java without sacrificing JVM’s battle-tested distribution, security and monitoring capabilities. Scala offers an intriguing gateway to bridging data science theory into applied distributed data pipelines.

Java: Scale Out Data Insights with Enterprise Stability

Despite lack of media glamour recently, overlook Java for scalable data applications at your own risk. Java’s remarkable multi-platform consistency stretches back over 20 years securing proven libraries and stable, predictable performance. Some data teams under-estimate reinventing pipelines from scratch in trendy alternatives versus leveraging reliable legacy Java systems.

The Java ecosystem nurtures incredibly sophisticated data middleware powering analytics engines processing billions of transactions hourly for Netflix, Twitter and practically every major financial organization. DataFrames, machine learning toolkits and advanced monitoring capabilities depend upon Java’s rock-solid foundations without exception.

While perhaps less convenient for initial exploration, Java’s maturity shines orchestrating immense scale data applications. Established JVM languages like Scala interoperate easily for production systems, while Clojure and other functional options access similar runtime advantages. Java’s discipline and unparalled community support empowers data science accomplishments beyond most alternatives presently.

MATLAB: The Matrix Laboratory Continuing Technical Computing Leadership

MATLAB retains pole position for scientific computing prowess over 40 years since Fortran wizard Cleve Moler first began developing it in the late 1970s. MATLAB built an early reputation delivering blazing fast matrix and vector performance. Matrices enable expressing computational algorithms intuitively per fundamental linear algebra principles. MATLAB feels instantly familiar for manipulating data mathematically.

Integrated desktop experience centering interactive console with debugging, plotting, code editing and documentation gives MATLAB true IDE feel. MATLAB integrates visualization deeply for exploring data and assessing analytical workflows effortlessly. MATLAB’s math orientation establishes shortest path from idea to analysis for technically trained data scientists translating statistical theory into demonstrable technique.

With hundreds of domain libraries for signal, image processing, mapping, time-series analysis and machine learning, MATLAB leverages purpose-built environment outclassing makeshift analytics of general languages. Surveying aeronautics, astronautics, physics, financial services and computational biology research affirms enduring trust in MATLAB technical computing.

C++: Unseen Performance Foundation for Data Infrastructure

While perhaps not entering data scientist tool belts directly, overlooking C++‘s indispensable data contributions proving disastrous. C++‘s obsessive low-level control, close-to-metal efficiency and milestone standardization established software foundations underpinning analytics breakthroughs.

Nearly every math, machine learning, statistics, simulation and modeling library powering Python, R and MATLAB traces implementation back to C++ under the hood. Uncompromising C++ speed and consistency birthed data infrastructure thriving today. Math-intensive frameworks like NumPy, TensorFlow, SciKit, Pandas and more live or die by C++ optimizations enhancing iteration velocities elsewhere.

C++‘s legacy securing Linux and May Unix descendants provide scalable data platforms. Peerless optimizations extract hardware capabilities enabling distributing learning, mining and predictive applications at vast scopes. While C++ coding seems antiquated measured against modern languages, nearly all data science languages owe immense debt to C++‘s performance. Master builders respect foundational strength.

I hope surveying these 7 languages illustrate the ammunition modern data science practitioners wield cracking today’s exponential data growth. While Python and R make perfectly acceptable starting points, limiting your programming language skills risks major missed opportunities. Treatlanguages as instruments which combined unlock deeper understanding from data.

Rather than debating subjective language superiority, focus efforts instead on mastering basics then expanding vocabulary over time. The present explosive demand for multi-lingual data talents means your learning investments will compound greatly for years ahead. Build up your programming language portfolio today and embrace the bright data science future!