WHAT IS DATA SCIENCE?
Data science is a blend of multidisciplinary branches of data-related technology like algorithms, data inference and development to analytically solve problems. A data scientist thoroughly analyses the data and processes it on the system using various machine learning algorithms to predict the future patterns or trends. Data science involves processing of both structured and unstructured data and puts specific focus on the present and future of the data processed.
SKILLS NEEDED TO BE A DATA SCIENTIST
Data science is one of the most popular careers sought after by people in the IT sector. Data scientists are some of the highest paid professionals in this sector and becoming a data scientist requires the need for some important skills in different areas.
Data scientists need to be highly educated. Most of the data scientists hired by IT companies have a Master’s degree while around 46% of the data scientists have PhDs. This has pointed out the fact that one needs a fairly strong educational background in order to acquire knowledge to become a data scientist.
Data scientists are also required to have good programming skills, especially in languages like Python and R. R is a programming language that is specific for data science requirements and it is vital that a data scientist masters the language. Many online and offline courses can help one grasp the programming language R. Most data scientists utilise R for solving the statistical problems encountered in data science.
Python is one of the most common programming languages required for data science along with Perl, C, C++ or Java. Python is most preferred by data scientists due to its versatility and ease of coding when compared with other programming languages. It can take in various different data formats and allows the data scientist to import SQL tables with ease into the code. Python also allows for the creation of datasets and thus it is an important skill required by a data scientist.
Another important skill required by data scientists is experience with Pig or Hive which is not necessary but is very advantageous. Having knowledge of cloud tools like Amazon S is also beneficial. Having knowledge of Hadoop and utilising it for data filtration, data exploration, summarization and data sampling. It is also utilised for quickly conveying data to various system points.
Data scientists also need to be proficient in SQL or Structured Query Language which helps in operations like adding, deleting and extracting data from any database. It helps in carrying out various analytical functions and also in transforming structures of databases. SQL is an important skill for a data scientist as it is specifically made for helping in access, communication and working of data.
Having a good amount of knowledge in Apache Spark is also very advantageous and helps boost one’s career in the data science field. It is specifically designed for helping run algorithms at a faster pace and helps the data scientist deal with large amounts of data easily. It also helps data scientists in preventing loss of any data.
Another advantageous skill for data scientists is being knowledgeable and proficient in areas like machine learning, deep learning and artificial intelligence or AI. The topics included under this are adversarial learning, reinforcement learning, neural networks etc. Having knowledge of various techniques involved in machine learning such as logistic regression, supervised machine learning, decision trees etc. can help you get an edge over other data scientists and will definitely help boost your career.
Data visualization is also an important skill required by data scientists that helps make data and information easier to understand. It helps data scientists to work with data directly and visualize its impacts and understand its implications better. A data scientist should also be able to work with various different unstructured data which may not fit in the database tables. This includes blog posts, reviews by consumers, videos, social media posts, etc. A data scientist must have the skill to properly analyse and also manipulate unstructured data in various different platforms. These skills are extremely necessary for anyone who wishes to become a successful data scientist in the future.
APPLICATIONS OF DATA SCIENCE IN EVERYDAY LIFE
It helps organizations and industries to reduce costs, get into a new market by tapping a new and different demographic, launching new products or services and identifying the feasibility or effectiveness of a product in the retail market. Good examples of applications of data science include self-driving automobiles, demand forecasting, fraud or risk detection in case of insurances, predicting flight delay in the travel industry, drones and pilot-less aircrafts, disease prediction and medication effectiveness in the healthcare industry, digital marketing on social media etc.
Shopping portals incorporate data science to understand customer mindset and preferences, technological or software companies employ data scientists for research on machine learning, deep learning and artificial intelligence to make smart devices that can be adjusted according to the user’s preferences. Financial gateways also hire data scientist to check the thousands of transactions going on daily in order to check for fraudulent transactions, customize products and financial or revenue services among other work.
SCOPE OF DATA SCIENCE
The data science industry has evolved exponentially over the last decade. Data science has become an important of businesses and industries. According to a study, data scientists earn almost 26% more than the average engineer in the software or IT industry in India. Many students from different engineering branches are now actively taking courses in data sciences for better job prospects.
Data science is the booming industry of today and is a game-changer for many businesses and industries that have incorporated data science in their working system and planning. Data science application can help businesses optimize performance and maximize profits to be successful in a particular domain.