You may have heard about how machine learning will impact our lives. The question is, what’s the difference between data science and machine learning?
We’ve recently explored the skyrocketing demand for Data Scientists and the reasons the field is growing at such a rapid rate.
If you’re interested in the technical skills required by the field, read on for the top programming languages for Data Science.
With a manageable learning curve and an array of libraries that allow for near-endless applications, Python is the top programming language of choice for many Data Scientists who appreciate its accessibility, ease of use and general-purpose versatility. In fact, BrainStation’s 2019 Digital Skills Survey found that Python was the most frequently used tool for Data Scientists overall.
Since being introduced in 1991, Python has built up a growing number of dedicated libraries to carry out tasks including data preprocessing, analysis, predictions, visualization, and preservation. Meanwhile, Python libraries including Tensorflow, pandas, and scikit-learn allow for more advanced machine learning or deep learning applications.
Data Scientists also tend to find Python to be generally faster than R and better for data manipulation.
You can find out more about this language with BrainStation’s Python Programming Certificate Course.
A free, open-source programming language that was released in 1995 as a descendant of the S programming language, R offers a top-notch range of quality domain-specific packages to meet nearly every statistical and data visualization application a Data Scientist might need, including neural networks, non-linear regression, advanced plotting and much more. Its visualization library ggplot2 is a powerful tool, and R’s static graphics can make it easier to produce graphs and mathematical symbols and formulae.
Yes, Python does have a speed advantage over the slower R, but for specific statistical and data analysis purposes, R’s vast range of packages gives it a slight edge between the two.
It is worth noting that R isn’t a general-purpose programming language and is intended to be used specifically for statistical analysis. And R does feature a steeper learning curve than the approachable Python.
Standing for “Structured Query Language,” SQL has been at the core of storing and retrieving data for decades now. SQL is a domain-specific language for managing data in relational databases and it’s a must-have skill for Data Scientists, who rely on SQL for updating, querying, editing and manipulating databases and extracting data.
SQL is particularly helpful for managing structured data, especially within large databases. And since SQL is a core skill, it’s helpful that its declarative language is quite readable and intuitive.
Though SQL is not as useful as an analytical tool, it is a highly efficient and crucial tool for data retrieval.
One of the oldest general-purpose languages used by Data Scientists, Java’s strength lies in part in its popularity and ubiquity: many companies, especially big, international companies, used Java to create backend systems and applications for desktop, mobile or web.
Java skills are increasingly attractive in part because of the possibility of weaving data science production code directly into an existing database. It’s also highly regarded for its performance, type safety and portability between platforms. And it’s worth noting that Hadoop runs on the Java virtual machine (JVM), another reason Java is a must-have skill for Data Scientists.
User-friendly and flexible, Scala is the ideal programming language when dealing with great volumes of data. Combining object-oriented and functional programming, Scala avoids bugs in complex applications with its static types, facilitates large-scale parallel processing, and, when paired with Apache Spark, provides high-performance cluster computing.
Engineered to run on the JVM, anything written on Scala can run anywhere that Java runs. It is becoming especially popular for anyone building complex algorithms or performing large-scale machine learning.
Scala does feature a steeper learning curve than some programming languages, but its massive user base is a testament to the value in sticking with it.
A much newer programming language than others on this list, Julia has nevertheless made a fast impression thanks to its lightning-fast performance, simplicity, and readability. Designed for numerical analysis and computational science, Julia is especially useful for solving complex mathematical operations, which explains why it’s becoming a fixture in the financial industry. It’s also becoming widely known as a language for artificial intelligence, and many large banks are now using Julia for risk analytics.
Because the language is relatively young, Julia does lack the variety of packages offered by R or Python.
Used widely in statistical analysis, this proprietary numerical computing language will be helpful for Data Scientists dealing with high-level mathematical needs, including Fourier transforms, signal processing, image processing, and matrix algebra. MATLAB has become widely used in industry and academia for its intensive mathematical functionality. It’s also worth mentioning its value as a data visualization tool, as it features some great inbuilt plotting capabilities.
MATLAB can also help cut down on time spent preprocessing data and help you find the best machine-learning models, regardless of your level of expertise.
Find out more about BrainStation’s Data Science Diploma program.