What is Data Science? A Beginner’s Guide to Data Science

Data Science is a blend of Mathematics, Programming, Statistics, Algorithms, and Machine Learning techniques that help us find out the hidden patterns from raw data. Data Science is an amalgamation of statistics, tools, and business knowledge. It deals with structured and unstructured data, which can be of significant use in constructing big business decisions.

Why Data Science?

The data that we traditionally dealt with is significantly in a pre-defined structure and small in size. This data could, therefore, be easily analyzed. But today, most of the data we deal with are unstructured or semi-structured. And it is generated from different sources like financial logs, multimedia forms, sensors, text files, etc. Data Science courses help us design more advanced analytical tools and algorithms to process, analyze, and draw meaningful insights from data.

Data Science courses are significant for daily life events such as:

• understanding precise requirements from existing data like customers’ past purchase history, browsing history, age, and income.
• designing self-driving cars that gather live data from sensors. After data analysis, it takes decisions like when to speed up, when to slow down, when to overtake, when to make a turn.
• Data Science applies to weather forecasting. It helps in predicting the occurrence of natural calamities beforehand and thus saves precious lives.

Who is a Data Scientist? What does a Data Scientist do?

A Data Scientist practices the art of Data Science. They draw information from scientific fields and applications like Mathematics and Statistics. Data Scientists utilize their knowledge of Mathematics, Statistics, and Computer Science to solve complex data problems. They efficiently use the latest technologies to present data in a much more organized and comparable form.

Who can pursue Data Science?

Students from diverse backgrounds like Engineering, Mathematics, Statistics, Economics, Computer Science, and Commerce can pursue data science. Having clarity of basics of Mathematics and Statistics like Linear Algebra, Calculus, Probability is necessary to learn Data Science courses.  Programming skills in C, C++, and Java are essential to learning Data Science courses with programming tools like Python, and R. Knowledge of Machine Learning is also essential as it is an essential part of Data Science courses.

Components of Data Science

Without Machine Learning, Big Data, and Artificial Intelligence (AI), Data Science cannot prevail. These three courses are a crucial part of Data Science courses with significant curating Data Science models.

Machine Learning

Machine Learning involves algorithms and analytical model building. A crucial part of Data Science helps machines comprehend, identify patterns, and make future decisions and predictions with minimal human interference.

Big Data

Human beings are producing a lot of data every day in orders, videos, images, comments, etc. These data are semi-structured or unstructured and often called Big Data. Big Data tools and techniques help in converting this unstructured data into a structured form. Big Data has applications in social media, stock exchange, and airports where a tremendous amount of data generates every second.

Artificial Intelligence (AI)

Artificial Intelligence aims at enabling machines to perform tasks by replicating human intelligence. It stimulates human intelligence in machines. The AI experts rely on learning and language processing to help machines identify patterns and inferences.

Data Science Specialisations

Data Science has different specializations that students can learn during their course to develop an in-depth understanding of the sub-domains of Data Science courses. The various specifications are:

Cloud Computing

Cloud Computing enables companies to access different computing services like servers, software, and data analytics over the Internet. It is referred to as the cloud, meaning the Internet, in a way. Companies and organizations pay for the cloud services they use. It helps them to lower their operating costs and run the business efficiently. Cloud Computing has many applications in everyday life. From using online services to sending emails, editing documents, storing large files, and playing games. Cloud Computing has made Data Analytics and Data Management much simpler for Data Scientists.

Networking

Networking is the connectivity between systems, cloud services, mobile devices, and other Internet of Things (IoT) devices. The devices connect via networks can communicate with each other and across networks. In Networking, data exchanges between networks. Data Science helps in dealing with the analysis and the maintenance of these large amounts of data.

Cyber Security

Data generates exponentially via our social media posts, photos, videos, search history, and many other data sets. Cybersecurity deals with the software and techniques used to protect these data and systems.

Cybersecurity Data Science (CSDS) is an approach to recognize potential attacks on the digital web. It uses the data-based approach, which applies machine learning methods and techniques to predict future threats. Thus, through the help of Data Science, cybersecurity professionals get the information that helps them counter cyber-attacks.

Steganography

Steganography is the art of encrypting, embedding, and concealing confidential data in pictures and videos. It has emerged as a powerful data encryption tool in the Information Technology (IT) industry and protects essential data such as bank information, customer information, and personal information.

Ethical Hacking

Data Science companies look for ethical hackers who can safeguard their data from the black hat hackers involved in illegal activities. Ethical hackers get access to the network from the authorities, and then they report any susceptibility in the data set and systems.  They are essential for Data Scientists as they can predict specific large amounts of data potential from cyber attacks.

Scope of Data Science

In present times, Data Science applies across all industries, including businesses, finance, healthcare, education, manufacturing, transport, etc. There is an ever-increasing demand for Data Scientists in all these industries. Let us take a look at some of the Data Science job profiles:

• Big Data Engineer: they develop, maintain, test, and evaluate Big Data solutions within different organizations.
• Machine Learning Engineer: they design and implement Machine Learning applications/algorithms to deal with various industry challenges
• Data Architect: they develop, construct, test, and maintain highly scalable data management systems
• Data Analysts: they are involved in data handlings and data visualizations.
• Business Intelligence Professional: they analyze past trends using Data Visualisation tools to develop and implement business strategies.

Data Science is a field of study with excellent career prospects. It is a high-paying, less saturated area that can guarantee constant growth and development for professionals who commit to this emerging and promising field of study.