Languages and Tools you should know to become a Data Scientist

Before starting with ‘languages and tools you should know to become a data scientist-Part 1’, you may look our introductory article Introduction to data science.

Let’s start…

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Languages for Data Science

One of the most confusing things for someone who is entering the field of Data Science is to know which programming language to choose from. The answer depends on what field you are working in, what are your uses, for whom you are working, etc. Some of the most common languages that are used by Data Scientists are:

Python, R, SQL, Scala, Java, C++, Julia, JS, PHP, GO, Ruby, and Visual Basic

But the most popular and most used languages among these are:

1. Python
2. R
3. SQL

Python

Python is an Open Source high-level programming language, developed by Guido van Rossum, back in the late 1980s. It is one of the most popular programming languages.

What makes python so popular is its simplified syntax which makes it very easy to learn even for someone new to programming. Also, python has one of the largest sets of standard libraries that would provide additional functions.

For Data Science it has:

• Scientific computing libraries like Pandas, NumPy Matplotlib, etc.
• For Artificial Intelligence and Machine Learning, it has PyTorch, TensorFlow, Keras, Scikit-Learn, NLTK (For Natural Language Processing (NLTK-Natural Language Toolkit))

R

It is often found that learning more than one programming language can be an advantage if you are aiming for a high salary job. R is a Free Software created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand. It is most often used by statisticians, mathematicians, and data miners for developing statistical software, graphing, and data analysis.

Data mining Is the process of finding patterns in a huge set of data.

Advantages of R programming language:

• Easy to translate from math to code
• Easy for programming beginners

R has a large set of libraries available which makes exploratory data analysis easier. It also integrates well with other programming languages like C++, JAVA, C, .Net, and Python. R is also equipped with object-oriented programming, which is better than other statistical computing languages.

SQL (Structured Query Language)

SQL was developed at IBM by Donald D. Chamberlin and Raymond F. Boyce during the 1970s. Even though it is not considered among the usual programming languages used for data science it is still widely used by data scientists. SQL is used to handle relational databases, for example, an excel datasheet or any datasets.

Some of the most common SQL databases available are:

MySQL, IBM DB2, ORACLE DATABASE, SQLite, Microsoft SQL Server, etc.

Other languages for Data science

Even though Python, R, and SQL are the most commonly used languages by data scientists, many other languages can be used to serve similar purposes in solving a particular data science problem. Some of these include:

Scala, Java, C++, Julia, JS, PHP, GO, Ruby, and Visual Basic.

Tools for Data Science

Before moving to tools for data science we shall look at some of the basic tasks in Data science.

• Data Management – Process of making and retrieving data.
• Data Integration and Transformation – Process of retrieving data from different remote management systems to one and changing formats of data according to the preferred format in that particular system.
• Data Visualization – Part of initial data exploration and is also used for presenting the final results.
• Model Building – Process of creating an ML or DL model from the data. (see the Introduction to Data Science article to know more about ML and DL).
• Model Deployment – Make the ML or DL model available in the form of applications.
• Model Monitoring & Assessment – Monitoring the deployed model for improving its functions.
• Code Asset Management – Uses different collaborative methods to facilitates teamwork such as versioning (creating newer versions of the same application).
• Data Asset Management – Supports replication, backup, and access rights to data.
• Development Environment – Tools that help developers to build prototype, test, develop, and deploy their work.
• Execution Environment – Tools for data processing, model training, and deployment.
• Fully Integrated Visual Tools – It covers all the tools mentioned above either partially or fully with a user interface where we can activate some functions with a button click rather than coding the whole thing.

Now we shall look into the tools…

Open Source Tools for Data Science

Data Management Tools