#datascienceassignment dataassignmenthelp

The goal of Data Science in Practice is to introduce the practical elements of doing data science.

Data science is an emerging, multidisciplinary field, organized around the practice of analyzing data and all the questions, methods, and problems that come with it.

This content focuses on the practical elements of finding, analyzing, interpreting, and contextualizing data analysis to practice answering questions with data.

Step 1 - Take a certified course.

Step 2: Read more than you speak.

Step 3 – Become an active member of the data science community.

Step 4 - Get involved in open source projects.

Step 5: Master technical skills.

What are programming languages?

Programming languages, simply put, are the languages ​​used to write the lines of code that make up software programs. These lines of code are digital instructions, commands, and other syntax that are translated into digital output. 

There are 5 main types of programming languages:

Procedural programming language

Functional programming language

Object Oriented Programming Language

Script programming language

Logic programming

Each of these types of programming languages ​​performs different functions and has specific advantages and disadvantages.

Python

Python has grown in popularity in recent years, ranking first in several programming language popularity indexes, including the TIOBE index and the PYPL index. Python is an open-source, general-purpose programming language that is widely applicable not only in the data science industry but also in other domains such as web development and game development.

Best used for: Python is best used for automation. Task automation is extremely valuable in data science and will ultimately save you a lot of time and provide you with valuable data 

          2. R

R is quickly rising up the ranks as the most popular programming language for data science, and for good reason. R is a highly extensible and easy-to-learn language that creates an environment for graphics and statistical computing.

All this makes R an ideal choice for data science, big data and machine learning.

R is a powerful scripting language. Since this is the case, it means R can handle large and complex data sets. This, combined with its ever-growing community, makes it a top-tier choice for the aspiring data scientist.

Best used for: R is best used in the data science world. It is especially powerful when performing statistical operations.

MATLAB

MATLAB is a very powerful tool for mathematical and statistical computing, which allows the implementation of algorithms and the creation of user interfaces. Creating UIs is especially easy with MATLAB because of its built-in graphics for plotting and data visualization.

This language is particularly useful for learning data science, it is primarily used as a resource to accelerate data science knowledge. Because of the deep learning toolbox functionality, learning Matlab is a great way to easily transition into deep learning.

Best Used For: MATLAB is commonly used in academia to teach linear algebra and numerical analysis.

SAS

SAS is a tool used primarily to analyze statistical data. It literally means statistical analysis system. The primary purpose of SAS is to retrieve, report, and analyze statistical data.

SAS may not be the first language you learn, but for beginners, knowing SAS can open up many more opportunities. It will help you a lot if you are looking for a job in data management.

Best Used For: SAS is used for machine learning and business intelligence with tools on your belt such as predictive and advanced analytics.

SQL

SQL is a very important language to learn to become a great data scientist. This is very important because data scientists need SQL to process data. SQL gives you access to data and statistics, making it a very useful resource for data science.

Data science requires a database, hence the use of a database language such as SQL. Anyone working with big data needs to have a solid understanding of SQL to be able to query databases.