To become a data scientist I will suggest you go ahead with Python, R and SQL, as these languages are the most trending languages.
Now lets see why these languages are important for data science and How data scientist uses them?
Why Python for Data Science?
It is the most popular language used by the data scientist.
is used for software development and web development. Data scientists used this language because it offers good libraries and frameworks. Used for various mathematical computations and data visualization.
The most popular tools and libraries for data science in python are mentioned below –
- Numpy: It is a machine learning library in Python. Other libraries such as Tensorflow uses Numpy for performing multiple operations. The most important feature of Numpy is the array interface.
- Pandas: It is a library that has the ability to convert complex operations to two or more commands.
- Tensorflow: An open-source library in python is Tensorflow, this library was developed by Google. It is a library used to write new algorithms that involve large operations and networks. In TensorFlow, your python code will get compiled and it as a number of applications.
- Scikitlearn: This library is considered as the best library to work with complex data. It is used in the data mining tasks to reduce the clustering and regression model.
- Scipy: This library contains modules for linear algebra, optimization. This library makes the use of Numpy, as scipy library is developed by Numpy. All the calculation process and linear algebra are handled by Scipy in Python.
learn Python from scratch –
All these libraries are important in python, and at least you should be known to them when you choose your skill to become a data scientist.
Why R for Data Science?
It’s an open-source language that is mainly used for statistical analysis and to solve complex data by using libraries. Many data scientists are using this language because under this language functions, objects are easily created. in this language, you can work with several data sources and packages.
This language allows us to work on cross-platform and multiple platforms. Data scientists use this programming language for data visualization and data analysis. It also has a wide range of libraries. Also nowadays almost every company such as Google, Facebook, Twitter, etc makes use of this language for data visualization and statistical analysis.
When it comes to data science it is the most demanding language for performing statistical modelling and it is also famous for its graphical libraries.
Features of R :
- Open-source, It is an open-source language and completely free to use.
- Graphical capabilities are good, it can produce quality graphs and plots of any kind. It makes data visualization and data representations easy
- Packages, a variety of packages are more than 15,000 for R like CRAN, GitHub. And also has several packages that interact with the database like RmySql, Roracle, etc.
- Cross-platform, it is a cross-platform supportive, so it can run on any OS
- Complier is not needed to convert code into a program.
Why SQL for Data Science?
In data science, SQL programming is used in data storage and data management. It allows you to create and manipulate data fast. It helps data scientist for retrieving the data usage in the machine learning task. It helps in creating database and tables. It is applauded for its simplicity and declarative statements. There are some SQL based tools that can be easily used by the data scientist.
- SQL has the ability to update, access control and store datasets, it’s an important skill for data science.
- Datasets in an SQL can be easily understand. Filtering, aggregations, SQL will allow you to play around the dataset.
- SQL will help you to investigate your datasets and identify the datasets, also it will allow you to find missing values and format of your dataset.
- SQL integrates well with other languages such as Python and R.
So, from these 3 languages if you choose one of them, you can easily start working on Data Science.