Coding geeks generally become data scientists. It does not mean that only coding experts can take up a data science career. There are several instances, where professionals with no/less coding experience took up a data science career and succeeded. Of course, they learned some coding essentials to become successful data science professionals.
All in all, coding is necessary. But you need not be a die-hard programmer to start the career journey in data science. Industry experts say that basic knowledge of programmings such as if-else, programming logic, functions, and loops are sufficient to become a successful data science professional.
The programming skills you need to learn to depend on the area of data science you are interested in or working in. If you are a non-programmer, you can learn GUI-based tools, build credibility through business acumen, and by being a great storyteller.
Let’s understand the most essential coding languages a data science professional can learn here.
Python
It is one of the best-recommended programming languages for data science. It is helpful in statistical analysis, easy readability, and data modeling. Python has extensive library support for data science and analytics by having a host of tools, functions, and methods for data analysis and its management.
R
Though R language was developed for statisticians by statisticians, it is popular in the data science field. It has an active community of itself and cutting-edge libraries. Each of the libraries is known for specific functionalities such as textual data or image management, manipulation of data, visualization of data, machine learning, and web crawling.
SQL
Structured Query Language is specifically created to manage and retrieve data that are stored in a relational database management system. As the role of data science professionals is to convert data into actionable insights, they need SQL for retrieving data from the database as required. The professionals can use SQLite, MySQL, Oracle, or Microsoft SQL Server.
MATLAB
As it is exclusive for mathematical operations, it is needed in data science for image processing, data analysis, and mathematical modeling. The mathematical functions are further useful in filtering, differential equations. Numerical integration, and Fourier analysis.
Java
As you all know, most of the big data and data science tools are written in Java. As Hadoop runs on the Java Virtual Machine [JVM], it is necessary to understand Java very well. In addition, data science libraries and tools are in Java. For instance: ML lib, Java-ML, and Deep learning. So, Java is an important language and foundation for several data science projects.
Scala
Scala is an extension of Java and can be used along with Apache Spark while managing humongous data. Scala is the go-to language for big data and several data science frameworks are created using Scala.
Let’s move forward and know how to avoid complexity. Though coding is learned by many, only experience can enable professionals to code perfectly. Many tend to make the whole process a complex network. Some of the habits to avoid complexity in coding include:
- Keeping the code clean
- Using functions
- Smuggling code out of Jupyter notebooks
- Applying test-driven development
- Making small and frequent commits
These precautions before coding make the data science job easier.
To conclude…
There are no specific programming languages meant for data scientists to learn. Depending on the job nature and industry trends, you can choose to learn a few of them or all of them. to learn coding and other essential skills and knowledge, earning a data science certification is preferred. A certified data scientist has more chances to gain a job in the top companies as they look for more competent professionals.
Get certified today and stay current with the trends. Make your career in data science successful.
