Data science is the systematic study of data that an organization gathers. It plays a crucial role in an organization because all the organization’s decisions are made on the basis of their understanding of data. The field of data science is also quite vast in itself and something definitely worth exploring. To form a simple conclusion from data collected requires acquiring knowledge and skills in pretty interesting subjects. To upgrade your skills, you can enrol in the data science and analytics course, or you could also take a data science course.


Let us now discuss a few exciting things to learn about Data Science in 2021-

Creative Data Visualization & Data Storytelling

Data visualization is one of the skills that all data scientists cultivate. The visualization and storytelling of the accumulated data have become an important and unique way to interact with project stakeholders for delivering the results.

For the machine learning algorithm, the typical reports and numeric results are the old ways to derive results of data analysis. A compelling and well-thought-out data visualization is the best way, right now, for arranging the desired results. R packages and Python libraries are the new data visualization techniques for compiling the reports.




Python is a general-purpose programming language whose design philosophy emphasizes code readability with its notable use of significant indentation. It is simple, easy to learn and emphasizes on readability.

It reduces the cost of program maintenance, and in the long run, is cost-effective and efficient. To subsume statistical code into the production of databases or manipulate data with web-based applications according to the advancement in technology, data scientists should use Python.

The tasks which Python can automate are more effective. You can read, write, interact with APIs, update spreadsheets, fill online forms, and optimize anything you want with the Python application.

Mathematics and Statistics

To understand the basics of machine learning, your foundations of mathematics and statistics must be strong. The study of mathematics can help avoid guesswork when it comes to hyperparameter values while tuning algorithms. This increases the precision of the work, increasing the quality of the report. The main areas in mathematics that you should focus on are – differential calculus, partial differential equations, integral calculus, linear algebra, statistics, and probability theory. These are the subjects to be mastered before you want to run problems on a machine learning algorithm.



SQL, along with R and Python, is the most used programming language in data science. It is identified as best in the query language, but it’s not a general-purpose programming language. Proficiency in SQL can help you organise the data and communicate with the compiler better.

In many cases, the data involved comes directly from an enterprise relational database. For acquiring that data and sorting it, knowledge of SQL is important. To improve the data frame, SQL, along with R and Python, is highly beneficial.

Data Transformation

When data is extracted from the source system into the destination system, there is a need to convert it for the convenience of the reader. Data integration and data management are the major tasks under data transformation.

It is defined as ‘simple’ or ‘complex’ depending on the modifications required before the complete transfer to the destination system. With the increase in the streaming of data, the risk of data compatibility increases. That is how data transformation helps you. It processes the big data to be integrated, stored, analyzed, and mined to be used by organizations investing in data science.


Frequently Asked Questions (FAQs):

  1. What is data science?

Data science refers to the study of data in an organization and deriving information from the same through the application of various scientific processes.

  1. Why is data science important?

Data science allows organizations to understand their performance better. It will enable organizations to make better decisions with regard to their business. If not data analysis isn’t conducted properly, it can cause significant damage to the data.

  1. What is Machine learning?

Machine learning is used to define a process in which computers improve their performance through self-programming. It is a crucial sub-domain of Artificial Intelligence (AI)

  1. What is Artificial Intelligence (AI)?

Just like humans have intelligence, the intelligence of computers/machines is called Artificial Intelligence because it is artificially developed. It is a simulation that consists of machines being conscious like Humans.

  1. Do I need to be good in math to excel in data science?

Yes, mathematical skills play an important role in the lives of data scientists, and as such, you must develop those skills and be good at it.